From 0aa2901f4c318d03124445c97176d3f9724c644f Mon Sep 17 00:00:00 2001 From: XiangJianxiong <1152203250@qq.com> Date: Tue, 8 Oct 2024 14:06:24 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9C=AC=E6=AC=A1=E7=9A=84=E6=8F=90=E4=BA=A4?= =?UTF-8?q?=E4=BF=A1=E6=81=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env | 23 + .idea/.gitignore | 8 + .idea/.name | 1 + .idea/dataSources.xml | 12 + .idea/inspectionProfiles/Project_Default.xml | 146 +++++++ .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 7 + .idea/modules.xml | 8 + .idea/other.xml | 6 + .idea/sqldialects.xml | 6 + .idea/wance_data.iml | 15 + banner.txt | 19 + migrations/models/3_20240909094115_None.py | 239 ++++++++++ models.py | Bin 0 -> 40996 bytes pyproject.toml | 4 + src/__init__.py | 4 + src/__pycache__/__init__.cpython-311.pyc | Bin 0 -> 246 bytes src/__pycache__/constants.cpython-311.pyc | Bin 0 -> 2255 bytes src/__pycache__/exceptions.cpython-311.pyc | Bin 0 -> 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| 14 + src/models/tran_position.py | 13 + src/models/tran_return.py | 13 + src/models/tran_trade_info.py | 12 + src/models/trand_info.py | 30 ++ src/models/transaction.py | 30 ++ src/models/transaction_strategy.py | 19 + src/models/user.py | 27 ++ src/models/wance_data_stock.py | 43 ++ src/models/wance_data_storage_backtest.py | 67 +++ .../backtest_request.cpython-311.pyc | Bin 0 -> 3881 bytes .../codelistrequest.cpython-311.pyc | Bin 0 -> 549 bytes .../factor_request.cpython-311.pyc | Bin 0 -> 3494 bytes .../__pycache__/request_data.cpython-311.pyc | Bin 0 -> 2640 bytes src/pydantic/backtest_request.py | 29 ++ src/pydantic/codelistrequest.py | 5 + src/pydantic/factor_request.py | 140 ++++++ src/pydantic/request_data.py | 21 + src/responses.py | 68 +++ src/settings/__init__.py | 0 .../__pycache__/__init__.cpython-311.pyc | Bin 0 -> 168 bytes .../__pycache__/config.cpython-311.pyc | Bin 0 -> 2084 bytes src/settings/config.py | 52 +++ src/tests/__init__.py | 0 src/tests/stock_query.py | 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src/backtest/bollinger_bands.py create mode 100644 src/backtest/dual_moving_average.py create mode 100644 src/backtest/living_backtesting.py create mode 100644 src/backtest/macd_strategy.py create mode 100644 src/backtest/reverse_dual_ma_strategy.py create mode 100644 src/backtest/router.py create mode 100644 src/backtest/rsi_strategy.py create mode 100644 src/backtest/service.py create mode 100644 src/backtest/until.py create mode 100644 src/constants.py rename readme.md => src/data_processing/__init__.py (100%) create mode 100644 src/data_processing/history_data_processing.py create mode 100644 src/data_processing/response_factor.py create mode 100644 src/exceptions.py create mode 100644 src/main.py create mode 100644 src/models/__init__.py create mode 100644 src/models/__pycache__/__init__.cpython-311.pyc create mode 100644 src/models/__pycache__/back_observed_data.cpython-311.pyc create mode 100644 src/models/__pycache__/back_observed_data_detail.cpython-311.pyc create mode 100644 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src/models/backtest.py create mode 100644 src/models/backtest_parameters.py create mode 100644 src/models/observed_data.py create mode 100644 src/models/order.py create mode 100644 src/models/page_Info.py create mode 100644 src/models/position.py create mode 100644 src/models/security_account.py create mode 100644 src/models/snowball.py create mode 100644 src/models/stock.py create mode 100644 src/models/stock_bt_history.py create mode 100644 src/models/stock_data_processing.py create mode 100644 src/models/stock_details.py create mode 100644 src/models/stock_history.py create mode 100644 src/models/strategy.py create mode 100644 src/models/test_table.py create mode 100644 src/models/tran_observer_data.py create mode 100644 src/models/tran_orders.py create mode 100644 src/models/tran_position.py create mode 100644 src/models/tran_return.py create mode 100644 src/models/tran_trade_info.py create mode 100644 src/models/trand_info.py create mode 100644 src/models/transaction.py create mode 100644 src/models/transaction_strategy.py create mode 100644 src/models/user.py create mode 100644 src/models/wance_data_stock.py create mode 100644 src/models/wance_data_storage_backtest.py create mode 100644 src/pydantic/__pycache__/backtest_request.cpython-311.pyc create mode 100644 src/pydantic/__pycache__/codelistrequest.cpython-311.pyc create mode 100644 src/pydantic/__pycache__/factor_request.cpython-311.pyc create mode 100644 src/pydantic/__pycache__/request_data.cpython-311.pyc create mode 100644 src/pydantic/backtest_request.py create mode 100644 src/pydantic/codelistrequest.py create mode 100644 src/pydantic/factor_request.py create mode 100644 src/pydantic/request_data.py create mode 100644 src/responses.py create mode 100644 src/settings/__init__.py create mode 100644 src/settings/__pycache__/__init__.cpython-311.pyc create mode 100644 src/settings/__pycache__/config.cpython-311.pyc create mode 100644 src/settings/config.py create mode 100644 src/tests/__init__.py create mode 100644 src/tests/stock_query.py create mode 100644 src/tests/xtquan_data_test.py create mode 100644 src/tortoises.py create mode 100644 src/tortoises_orm_config.py create mode 100644 src/utils/__init__.py create mode 100644 src/utils/__pycache__/__init__.cpython-311.pyc create mode 100644 src/utils/__pycache__/helpers.cpython-311.pyc create mode 100644 src/utils/__pycache__/history_data_processing_utils.cpython-311.pyc create mode 100644 src/utils/__pycache__/models.cpython-311.pyc create mode 100644 src/utils/__pycache__/paginations.cpython-311.pyc create mode 100644 src/utils/__pycache__/redis.cpython-311.pyc create mode 100644 src/utils/helpers.py create mode 100644 src/utils/history_data_processing_utils.py create mode 100644 src/utils/history_stock.py create mode 100644 src/utils/jsonformatter.py create mode 100644 src/utils/models.py create mode 100644 src/utils/paginations.py create mode 100644 src/utils/redis.py create mode 100644 src/utils/remove_duplicates_databases.py create mode 100644 src/utils/split_stock_utils.py create mode 100644 src/xtdata/__init__.py create mode 100644 src/xtdata/__pycache__/__init__.cpython-311.pyc create mode 100644 src/xtdata/__pycache__/router.cpython-311.pyc create mode 100644 src/xtdata/__pycache__/service.cpython-311.pyc create mode 100644 src/xtdata/router.py create mode 100644 src/xtdata/service.py diff --git a/.env b/.env new file mode 100644 index 0000000..42bf6bc --- /dev/null +++ b/.env @@ -0,0 +1,23 @@ +;使用本地docker数据库就将这段代码解开，如何将IP换成本机IP或者localhost +; DATABASE_URL="mysql://root:123456@10.1.5.219:3306/ftrade" +; DATABASE_CREATE_URL ="mysql://root:123456@10.1.5.219:3306/ftrade" + +DATABASE_URL="mysql://wangche:fN7sXX8saiQKXWbG@cqxqg.tech:3308/wangche" +DATABASE_CREATE_URL ="mysql://wangche:fN7sXX8saiQKXWbG@cqxqg.tech:3308/wangche" + +REDIS_URL="redis://:redis_tQNjCH@cqxqg.tech:6380" + +SITE_DOMAIN=127.0.0.1 +SECURE_COOKIES=false + +ENVIRONMENT=LOCAL + +CORS_HEADERS=["*"] +CORS_ORIGINS=["http://localhost:3000"] + +# postgres variables, must be the same as in DATABASE_URL +POSTGRES_USER=app +POSTGRES_PASSWORD=app +POSTGRES_HOST=app_db +POSTGRES_PORT=5432 +POSTGRES_DB=app \ No newline at end of file diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..35410ca --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/.name b/.idea/.name new file mode 100644 index 0000000..7bfcf52 --- /dev/null +++ b/.idea/.name @@ -0,0 +1 @@ +wance_data \ No newline at end of file diff --git a/.idea/dataSources.xml b/.idea/dataSources.xml new file mode 100644 index 0000000..1e41358 --- /dev/null +++ b/.idea/dataSources.xml @@ -0,0 +1,12 @@ + + + + + mysql.8 + true + com.mysql.cj.jdbc.Driver + jdbc:mysql://lawyer5.cn:3308/wangche + $ProjectFileDir$ + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..dd1720e --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,146 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..39831c4 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..4c5c71c --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/other.xml b/.idea/other.xml new file mode 100644 index 0000000..2e75c2e --- /dev/null +++ b/.idea/other.xml @@ -0,0 +1,6 @@ + + + + + \ No newline at end of file diff --git a/.idea/sqldialects.xml b/.idea/sqldialects.xml new file mode 100644 index 0000000..6743fb4 --- /dev/null +++ b/.idea/sqldialects.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/wance_data.iml b/.idea/wance_data.iml new file mode 100644 index 0000000..d33acca --- /dev/null +++ b/.idea/wance_data.iml @@ -0,0 +1,15 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/banner.txt b/banner.txt new file mode 100644 index 0000000..d37d152 --- /dev/null +++ b/banner.txt @@ -0,0 +1,19 @@ + # ## # # # ## ## # # # # # # # # # # # # + ## ## # ## ## ## # ########## # ## # # #### ## ## ## ## # ############## ### ## ############# + ## ############# ## ## ## # ## # ############## # #### # ## ## ## # ############### ## ## ## ## ## ## ## + ## ## # ## ## ## # ## ############### ## # ## ## ## ## ## ########## ## ## ## ## ## ## ## # ## + # ### 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## # ## ## ## ## ### ## ## ## #### ### ########### ## + #### ## #### ## ###### # ## # # # ### # ## ## ### ## ### ## ### ## #### ### ##### ## #### + # # # # # # ############### # # # # # # # # # # ## # # # # # +========================================================================================================================================================================================================================== +*********************************************************************************************聪明帅气可爱睿智的老板保佑我永不报错************************************************************************************************ +========================================================================================================================================================================================================================== diff --git a/migrations/models/3_20240909094115_None.py b/migrations/models/3_20240909094115_None.py new file mode 100644 index 0000000..98b6f97 --- /dev/null +++ b/migrations/models/3_20240909094115_None.py @@ -0,0 +1,239 @@ +from tortoise import BaseDBAsyncClient + + +async def upgrade(db: BaseDBAsyncClient) -> str: + return """ + CREATE TABLE IF NOT EXISTS `users` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `nickname` VARCHAR(30) COMMENT '用户昵称', + `avatar_url` VARCHAR(255) COMMENT '头像', + `member_type` INT COMMENT '会员类型', + `beta_account_type` VARCHAR(30) COMMENT '内测账号类型', + `pre_cost_time` INT COMMENT '预支付时间（单位年）', + `qr_code` VARCHAR(255) COMMENT '专属客服二维码', + `dedicated_id` INT COMMENT '专属客服id', + `invited_user_id` INT COMMENT '邀请人id', + `created_user_id` INT COMMENT '创建人id', + `is_deleted` BOOL NOT NULL COMMENT '是否删除' DEFAULT 0, + `login_at` DATETIME(6) COMMENT '最后一次登录时间', + `created_at` DATETIME(6) NOT NULL COMMENT '创建时间' DEFAULT CURRENT_TIMESTAMP(6), + `updated_at` DATETIME(6) NOT NULL COMMENT '修改时间' DEFAULT CURRENT_TIMESTAMP(6) ON UPDATE CURRENT_TIMESTAMP(6), + `deleted_at` DATETIME(6) COMMENT '删除时间', + KEY `idx_users_is_dele_9cdc79` (`is_deleted`) +) CHARACTER SET utf8mb4 COMMENT='用户'; +CREATE TABLE IF NOT EXISTS `stock` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `stock_code` VARCHAR(30) NOT NULL COMMENT '股票代码', + `stock_name` VARCHAR(30) COMMENT '股票名称', + `type` VARCHAR(2) COMMENT '类型', + `stock_pinyin` VARCHAR(30) NOT NULL COMMENT '股票拼音' +) CHARACTER SET utf8mb4 COMMENT='股票相关信息'; +CREATE TABLE IF NOT EXISTS `security_account` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `securities_name` VARCHAR(30) NOT NULL COMMENT '证券公司名字昵称', + `fund_account` BIGINT COMMENT '资金账户', + `account_alias` VARCHAR(30) NOT NULL COMMENT '账户别名', + `money` DOUBLE COMMENT '账户金额', + `available_money` DOUBLE COMMENT '可用金额', + `available_proportion` DOUBLE COMMENT '可用资金占比', + `freeze` DOUBLE COMMENT '冻结金额' +) CHARACTER SET utf8mb4 COMMENT='证券账户'; +CREATE TABLE IF NOT EXISTS `backtest` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键id', + `key` VARCHAR(30) NOT NULL COMMENT '回测key' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `Entrust` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键id', + `fund_account` INT COMMENT '资金账户', + `securities_alias` VARCHAR(30) COMMENT '账户别名', + `account_type` SMALLINT COMMENT '账户类型', + `stock_code` VARCHAR(30) NOT NULL COMMENT '证券代码', + `stock_name` VARCHAR(30) NOT NULL COMMENT '证券名称', + `limit_price` DOUBLE NOT NULL COMMENT '委托价', + `entrust_number` INT NOT NULL COMMENT '委托数量', + `deal_price` DOUBLE COMMENT '成交价格', + `deal_number` INT COMMENT '成家数量', + `order_type` VARCHAR(30) NOT NULL COMMENT '操作', + `entrust_date` DATE COMMENT '委托日期', + `entrust_money` DOUBLE NOT NULL COMMENT '委托金额', + `is_repair` BOOL NOT NULL COMMENT '是否补单' DEFAULT 0, + `entrust_time` TIME(6), + KEY `idx_Entrust_is_repa_5a195d` (`is_repair`) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `orders` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '订单ID', + `stock_code` VARCHAR(30) NOT NULL COMMENT '股票代码', + `stock_name` VARCHAR(30) NOT NULL COMMENT '股票名称', + `limit_price` DOUBLE NOT NULL COMMENT '限价', + `order_quantity` INT NOT NULL COMMENT '委托数量', + `order_amount` DOUBLE NOT NULL COMMENT '委托金额', + `order_type` VARCHAR(20) NOT NULL COMMENT '订单类型', + `position` VARCHAR(30) NOT NULL COMMENT '仓位', + `user_id` INT NOT NULL COMMENT '用户Id', + `entrust_date` DATETIME(6) COMMENT '委托日期', + `entrust_time` TIME(6) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `snowball` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `snowball_token` VARCHAR(10000) COMMENT '雪球用户的token' +) CHARACTER SET utf8mb4 COMMENT='雪球相关信息'; +CREATE TABLE IF NOT EXISTS `Strategy` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `strategy_name` VARCHAR(255) NOT NULL COMMENT '策略名称', + `strategy_hash` VARCHAR(255) NOT NULL COMMENT '策略版本号', + `strategy_type` VARCHAR(255) COMMENT '策略类型', + `user_id` INT COMMENT '所属用户', + `backtest_count` INT COMMENT '回测次数', + `backtest_keys` JSON COMMENT '回测key列表', + `is_deleted` BOOL NOT NULL COMMENT '是否删除' DEFAULT 0, + `created_at` DATETIME(6) NOT NULL COMMENT '创建时间' DEFAULT CURRENT_TIMESTAMP(6), + `updated_at` DATETIME(6) NOT NULL COMMENT '修改时间' DEFAULT CURRENT_TIMESTAMP(6) ON UPDATE CURRENT_TIMESTAMP(6), + `deleted_at` DATETIME(6) COMMENT '删除时间' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `Backtest` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(20) NOT NULL UNIQUE COMMENT '回测key', + `user_id` INT COMMENT '回测用户', + `backtest_at` DATETIME(6) NOT NULL COMMENT '回测时间' DEFAULT CURRENT_TIMESTAMP(6), + `backtest_code` LONGTEXT NOT NULL COMMENT '回测代码', + `is_running` BOOL NOT NULL COMMENT '回测状态' DEFAULT 1, + `updated_at` DATETIME(6) NOT NULL COMMENT '修改时间' DEFAULT CURRENT_TIMESTAMP(6) ON UPDATE CURRENT_TIMESTAMP(6), + `deleted_at` DATETIME(6) COMMENT '删除时间', + `is_active` BOOL NOT NULL COMMENT '是否可用' DEFAULT 1, + `strategy_id` INT NOT NULL, + CONSTRAINT `fk_Backtest_Strategy_7eabb20d` FOREIGN KEY (`strategy_id`) REFERENCES `Strategy` (`id`) ON DELETE CASCADE, + KEY `idx_Backtest_key_eb92b1` (`key`), + KEY `idx_Backtest_strateg_83ac3d` (`strategy_id`) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `stock_details` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `stock_code` VARCHAR(30) NOT NULL COMMENT '股票代码', + `stock_name` VARCHAR(30) COMMENT '股票名称', + `type` VARCHAR(2) COMMENT '类型', + `stock_pinyin` VARCHAR(30) NOT NULL COMMENT '股票拼音', + `latest_price` DOUBLE COMMENT '最新价', + `rise_fall` DOUBLE COMMENT '跌涨幅' +) CHARACTER SET utf8mb4 COMMENT='股票相关信息'; +CREATE TABLE IF NOT EXISTS `Transaction` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `key` VARCHAR(20) NOT NULL COMMENT 'key,数据标识', + `cash` DOUBLE COMMENT '资金', + `transaction_name` VARCHAR(255) NOT NULL COMMENT '交易名称', + `transaction_type` VARCHAR(255) COMMENT '交易类型', + `user_id` INT COMMENT '用户id', + `is_running` BOOL NOT NULL COMMENT '运行状态' DEFAULT 1, + `is_deleted` BOOL NOT NULL COMMENT '是否删除' DEFAULT 0, + `process_id` INT COMMENT '进程号', + `bar` VARCHAR(10) COMMENT '频率K线', + `created_at` DATETIME(6) NOT NULL COMMENT '创建时间' DEFAULT CURRENT_TIMESTAMP(6), + `updated_at` DATETIME(6) NOT NULL COMMENT '修改时间' DEFAULT CURRENT_TIMESTAMP(6) ON UPDATE CURRENT_TIMESTAMP(6), + `stopped_at` DATETIME(6) COMMENT '停止时间', + `deleted_at` DATETIME(6) COMMENT '删除时间', + `strategy_id` INT NOT NULL, + CONSTRAINT `fk_Transact_Strategy_849d0577` FOREIGN KEY (`strategy_id`) REFERENCES `Strategy` (`id`) ON DELETE CASCADE, + KEY `idx_Transaction_strateg_b8f0b2` (`strategy_id`) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `position` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT '主键', + `key` VARCHAR(30) NOT NULL COMMENT '键', + `date` DATE NOT NULL COMMENT '日期', + `name` VARCHAR(30) NOT NULL COMMENT '名称', + `size` INT NOT NULL COMMENT '数量', + `price` DOUBLE NOT NULL COMMENT '价格', + `adjbase` DOUBLE NOT NULL COMMENT '复权基数', + `profit` DOUBLE NOT NULL COMMENT '利润' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `trand_info` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT 'id', + `key` VARCHAR(255) COMMENT '唯一索引', + `tran_info_data` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `tran_observer_data` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(255) NOT NULL, + `tran_observer_data` LONGBLOB COMMENT '存储大量数据' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `tranorders` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(255), + `order_return` VARCHAR(255) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `tran_return` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(255), + `tran_return_data` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `tran_trade_info` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(25), + `tran_trade_info` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `back_observed_data` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT COMMENT 'id表', + `key` VARCHAR(255) COMMENT 'key', + `observed_data` LONGBLOB COMMENT '格式化后的json数据' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `back_observed_data_detail` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(255) NOT NULL, + `back_observed_data` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `back_position` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(25), + `back_position_data` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `back_result_indicator` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(25), + `indicator` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `back_trand_info` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(25), + `trade_info` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `tran_position` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `key` VARCHAR(25), + `tran_position_data` LONGBLOB +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `stock_bt_history` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `end_bt_time` VARCHAR(8) COMMENT '回测最终时间', + `bt_stock_code` VARCHAR(8) COMMENT '回测股票代码', + `bt_stock_name` VARCHAR(10) COMMENT '回测股票名称', + `bt_benchmark_code` VARCHAR(8) COMMENT '股票基准代码', + `bt_stock_period` VARCHAR(10) COMMENT '回测类型', + `bt_strategy_name` VARCHAR(10) COMMENT '回测策略名称', + `bt_stock_data` LONGBLOB COMMENT '回测股票数据' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `stock_history` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `stock_code` INT NOT NULL COMMENT '股票代码', + `stock_name` VARCHAR(10), + `start_time_to_market` VARCHAR(10) COMMENT '股票上市时间', + `end_bt_time` VARCHAR(10) COMMENT '最终回测时间', + `symbol_data` LONGBLOB COMMENT '股票数据' +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `stock_data_processing` ( + `bt_benchmark_code` VARCHAR(6) COMMENT '基准代码', + `bt_stock_period` VARCHAR(10) COMMENT '数据类型', + `bt_strategy_name` VARCHAR(10) COMMENT '回测策略名', + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `processing_data` LONGBLOB COMMENT '清洗后的数据', + `prosessing_date` VARCHAR(10) COMMENT '当前回测时间', + `stock_name` VARCHAR(10), + `stocke_code` VARCHAR(10) +) CHARACTER SET utf8mb4; +CREATE TABLE IF NOT EXISTS `aerich` ( + `id` INT NOT NULL PRIMARY KEY AUTO_INCREMENT, + `version` VARCHAR(255) NOT NULL, + `app` VARCHAR(100) NOT NULL, + `content` JSON NOT NULL +) CHARACTER SET utf8mb4;""" + + +async def downgrade(db: BaseDBAsyncClient) -> str: + return """ + """ diff --git a/models.py b/models.py new file mode 100644 index 0000000000000000000000000000000000000000..4b13f384dd40c6941788c135a4328c8936bf2108 GIT binary patch literal 40996 zcmeHQeT-Dq6@NgA%g3UPDYi6?*rc*h3TPEAYN7}#)qT4=Gt068XV}@DoyGlhXLewr zE@0P6#1%IwA2HPggO)!@n%G$Xs7+dHthzPu2Z@cbZTQEeXrd8oYPaY2E|>S-%(-{o zn|brzLd^!j*_n5L_uTV+&OI~z-)>{r7&Q8grAD6q9=86w!^jyKdSwT_K6LTbrA8zD 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100644 index 0000000..bf5ef49 --- /dev/null +++ b/src/backtest/backtest.py @@ -0,0 +1,106 @@ +import bt +import pandas as pd +import numpy as np +from xtquant import xtdata +import matplotlib.pyplot as plt + +# 数据的列名 +columns = ['open', 'high', 'low', 'close', 'volume', 'amount', 'settelmentPrice', + 'openInterest', 'preClose', 'suspendFlag'] + + +# 获取本地数据并进行处理 +def get_local_data(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, dividend_type: str, fill_data: bool, data_dir: str): + result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data, + data_dir=data_dir) + return data_processing(result) + + +# 数据处理函数 +def data_processing(result_local): + # 初始化一个空的列表，用于存储每个股票的数据框 + df_list = [] + + # 遍历字典中的 DataFrame + for stock_code, df in result_local.items(): + # 确保 df 是一个 DataFrame + if isinstance(df, pd.DataFrame): + # 将时间戳转换为日期时间格式，只保留年-月-日 + df['time'] = pd.to_datetime(df['time'], unit='ms').dt.date + # 将 'time' 列设置为索引，保留为日期格式 + df.set_index('time', inplace=True) + # 指定列名 + df.columns = columns + # 添加一列 'stock_code' 用于标识不同的股票 + df['stock_code'] = stock_code + # 将 DataFrame 添加到列表中 + df_list.append(df[['close']]) # 只保留 'close' 列 + else: + print(f"数据格式错误: {stock_code} 不包含 DataFrame") + + # 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame + combined_df = pd.concat(df_list, axis=1) + + # 确保返回的 DataFrame 索引是日期格式 + combined_df.index = pd.to_datetime(combined_df.index) + + # 打印最终的 DataFrame + print(combined_df) + + return combined_df + + +# 定义策略 +def moving_average_strategy(data, short_window=20, long_window=50): + data['Short_MA'] = data['close'].rolling(window=short_window, min_periods=1).mean() + data['Long_MA'] = data['close'].rolling(window=long_window, min_periods=1).mean() + + data['Signal'] = 0 + data.loc[data.index[short_window:], 'Signal'] = np.where( + data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0) + data['Position'] = data['Signal'].diff() + + return data + + +# 定义策略函数 +def bt_strategy(data): + # 计算策略信号 + # data = moving_average_strategy(data) + + # 定义策略 + dual_ma_strategy = bt.Strategy('Dual MA Strategy', [bt.algos.RunOnce(), + bt.algos.SelectAll(), + bt.algos.WeighEqually(), + bt.algos.Rebalance()]) + return dual_ma_strategy + + +# 运行回测 +def run_backtest(): + # 生成数据 + data = get_local_data(field_list=[], stock_list=["300391.SZ"], period='1d', start_time='', end_time='', count=-1, + dividend_type='none', fill_data=True, data_dir="") + + # 创建策略 + strategy = bt_strategy(data) + + # 创建回测 + portfolio = bt.Backtest(strategy, data) + result = bt.run(portfolio) + + return result + + +# 执行回测并显示结果 +if __name__ == "__main__": + result = run_backtest() + result.plot() + b = xtdata.get_sector_list() + print(b) + xtdata.download_sector_data() + a = result.stats + print(a) + plt.show() diff --git a/src/backtest/bollinger.py b/src/backtest/bollinger.py new file mode 100644 index 0000000..f6e020f --- /dev/null +++ b/src/backtest/bollinger.py @@ -0,0 +1,79 @@ +import bt + +# 模拟从前端接收到的因子列表 +factors = [ + {'name': 'SMA', 'period': 50}, + {'name': 'EMA', 'period': 200}, + {'name': 'RSI', 'period': 14}, + {'name': 'BollingerBands', 'period': 20} +] + + +def create_indicator(name, **kwargs): + """ + 根据名称和参数动态创建bt的技术指标。 + """ + if name == 'SMA': + return bt.indicators.SMA(kwargs['period']) + elif name == 'EMA': + return bt.indicators.EMA(kwargs['period']) + elif name == 'RSI': + return bt.indicators.RSI(kwargs['period']) + elif name == 'BollingerBands': + return bt.indicators.BollingerBands(kwargs['period']) + + else: + raise ValueError(f"未知的指标名称: {name}") + + +def create_dynamic_strategy(name, factors): + """ + 根据传递的因子列表动态构建策略。 + """ + algos = [bt.algos.RunMonthly(), # 每月运行一次 + bt.algos.SelectAll()] # 选择所有资产 + + # 动态生成指标选择逻辑 + for factor in factors: + indicator = create_indicator(factor['name'], **factor) + if factor['name'] == 'RSI': + # RSI 特定的买入/卖出逻辑 + buy_signal = bt.algos.SelectWhere(indicator < 30) # 超卖区域买入 + sell_signal = bt.algos.SelectWhere(indicator > 70) # 超买区域卖出 + algos.append(buy_signal) + algos.append(bt.algos.WeighTarget(1.0)) + algos.append(bt.algos.Rebalance()) + algos.append(sell_signal) + algos.append(bt.algos.WeighTarget(0.0)) + algos.append(bt.algos.Rebalance()) + elif factor['name'] == 'BollingerBands': + # 布林带特定的逻辑（买入接近下轨，卖出接近上轨） + buy_signal = bt.algos.SelectWhere(bt.data < indicator['lower']) + sell_signal = bt.algos.SelectWhere(bt.data > indicator['upper']) + algos.append(buy_signal) + algos.append(bt.algos.WeighTarget(1.0)) + algos.append(bt.algos.Rebalance()) + algos.append(sell_signal) + algos.append(bt.algos.WeighTarget(0.0)) + algos.append(bt.algos.Rebalance()) + # 可以为其他指标添加更多逻辑 + + return bt.Strategy(name, algos) + + +# 创建动态策略 +dynamic_strategy = create_dynamic_strategy("Dynamic_Strategy", factors) + +# 获取数据 +data = bt.get('spy,agg', start='2020-01-01', end='2023-01-01') + +# 创建回测 +backtest = bt.Backtest(dynamic_strategy, data) + +# 运行回测 +result = bt.run(backtest) + +# 展示结果 +result.display() +result.plot() + diff --git a/src/backtest/bollinger_bands.py b/src/backtest/bollinger_bands.py new file mode 100644 index 0000000..576e26d --- /dev/null +++ b/src/backtest/bollinger_bands.py @@ -0,0 +1,274 @@ +import asyncio +import json +import time +from datetime import datetime + +import bt +import numpy as np +import pandas as pd +from xtquant import xtdata +import matplotlib.pyplot as plt + +from src.backtest.until import get_local_data, convert_pandas_to_json_serializable +from src.models import wance_data_storage_backtest, wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +# 布林带策略函数 +async def create_bollinger_bands_strategy(data, stock_code: str, bollingerMA: int = 50, std_dev: int = 200): + # 生成布林带策略信号 + signal = await bollinger_bands_strategy(data, bollingerMA, std_dev) + + # 使用bt框架构建策略 + strategy = bt.Strategy(f'{stock_code} 布林带策略', + [bt.algos.RunDaily(), + bt.algos.SelectAll(), # 选择所有股票 + bt.algos.WeighTarget(signal), # 根据信号调整权重 + bt.algos.Rebalance()]) # 调仓 + return strategy, signal + + +async def bollinger_bands_strategy(df, window=20, num_std_dev=2): + """ + 基于布林带策略生成买卖信号。 + + 参数: + df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。 + window: int, 计算布林带中轨线的窗口期。 + num_std_dev: float, 标准差的倍数，用于计算上下轨。 + + 返回: + signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。 + """ + # 计算中轨线（移动平均） + middle_band = df.rolling(window=window, min_periods=1).mean() + + # 计算滚动标准差 + rolling_std = df.rolling(window=window, min_periods=1).std() + + # 计算上轨线和下轨线 + upper_band = middle_band + (rolling_std * num_std_dev) + lower_band = middle_band - (rolling_std * num_std_dev) + + # 初始化信号 DataFrame + signal = pd.DataFrame(index=df.index, columns=df.columns) + + # 生成买入信号：当价格突破下轨时 + for column in df.columns: + signal[column] = np.where(df[column] < lower_band[column], 1, np.nan) # 买入信号 + + # 生成卖出信号：当价格突破上轨时 + for column in df.columns: + signal[column] = np.where(df[column] > upper_band[column], 0, signal[column]) # 卖出信号 + + # 前向填充信号，持仓不变 + signal = signal.ffill() + + # 将剩余的 NaN 替换为 0 + signal = signal.fillna(0) + + return signal + + +async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, bollingerMA, + std_dev): + await init_tortoise() + + # 要存储的字段列表 + fields_to_store = [ + 'stock_code', 'strategy_name', 'stock_close_price', 'daily_price', + 'price', 'returns', 'data_start_time', 'data_end_time', + 'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr', + 'max_drawdown', 'calmar', 'mtd', 'three_month', + 'six_month', 'ytd', 'one_year', 'three_year', + 'five_year', 'ten_year', 'incep', 'daily_sharpe', + 'daily_sortino', 'daily_mean', 'daily_vol', + 'daily_skew', 'daily_kurt', 'best_day', 'worst_day', + 'monthly_sharpe', 'monthly_sortino', 'monthly_mean', + 'monthly_vol', 'monthly_skew', 'monthly_kurt', + 'best_month', 'worst_month', 'yearly_sharpe', + 'yearly_sortino', 'yearly_mean', 'yearly_vol', + 'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year', + 'avg_drawdown', 'avg_drawdown_days', 'avg_up_month', + 'avg_down_month', 'win_year_perc', 'twelve_month_win_perc' + ] + + # 准备要存储的数据 + data_to_store = { + 'stock_code': stock_code, + 'strategy_name': "布林带策略", + 'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign( + time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')), + 'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices), + 'price': convert_pandas_to_json_serializable(result[source_column_name].prices), + 'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)), + 'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'), + 'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'), + 'backtest_end_time': int(datetime.now().strftime('%Y%m%d')), + 'position': convert_pandas_to_json_serializable(signal), + 'backtest_name': f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差', + 'indicator_type': 'Bollinger', + 'indicator_information': json.dumps({'bollingerMA': bollingerMA, 'std_dev': std_dev}) + } + + # 使用循环填充其他字段 + for field in fields_to_store[12:]: # 从第10个字段开始 + value = result.stats.loc[field].iloc[0] + data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value + + # 检查是否存在该 backtest_name + existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=data_to_store['backtest_name'] + ).first() + + if existing_record: + # 如果存在，更新记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + id=existing_record.id + ).update(**data_to_store) + else: + # 如果不存在，创建新的记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store) + + return data_to_store + + +async def run_bollinger_backtest(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = 100, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + bollingerMA: int = 50, + std_dev: int = 200): + try: + # 初始化一个列表用于存储每只股票的回测结果字典 + results_list = [] + + # 遍历每只股票的数据（每列代表一个股票的收盘价） + data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type, + fill_data, + data_dir) + + for stock_code in stock_list: + + data_column_name = f'close_{stock_code}' + source_column_name = f'{stock_code} 布林带策略' + backtest_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差' + now_time = int(datetime.now().strftime('%Y%m%d')) + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + if db_result: + if db_result[0].backtest_end_time == now_time: + results_list.append({source_column_name: db_result[0]}) + + # elif data_column_name in data.columns: + if data_column_name in data.columns: + stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame + stock_data_series.columns = ['close'] # 重命名列为 'close' + + # 创建布林带策略 + strategy, signal = await create_bollinger_bands_strategy(stock_data_series, stock_code, + bollingerMA=bollingerMA, + std_dev=std_dev) + # 创建回测 + backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000) + # 运行回测 + result = bt.run(backtest) + # 存储回测结果 + data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code, + stock_data_series, + bollingerMA, std_dev) + # # 绘制回测结果图表 + # result.plot() + # # 绘制个别股票数据图表 + # plt.figure(figsize=(12, 6)) + # plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price') + # plt.title(f'Stock Price for {stock_code}') + # plt.xlabel('Date') + # plt.ylabel('Price') + # plt.legend() + # plt.grid(True) + # plt.show() + # 将结果存储为字典并添加到列表中 + results_list.append({source_column_name: data_to_store}) + + else: + print(f"数据中缺少列: {data_column_name}") + + return results_list # 返回结果列表 + + except Exception as e: + print(f"Error occurred: {e}") + + +async def start_bollinger_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + bollingerMA: int = 50, + std_dev: int = 200): + for stock_code in stock_list: + backtest_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差' + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + now_time = int(datetime.now().strftime('%Y%m%d')) + + if db_result and db_result[0].backtest_end_time == now_time: + return db_result + else: + # 执行回测 + result = await run_bollinger_backtest( + field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir, + bollingerMA=bollingerMA, + std_dev=std_dev, + ) + return result + + +async def init_backtest_db(): + bollinger_list = [{"bollingerMA": 20, "std_dev": 2}, {"bollingerMA": 30, "std_dev": 2}, + {"bollingerMA": 70, "std_dev": 2}, {"bollingerMA": 5, "std_dev": 1}, + {"bollingerMA": 20, "std_dev": 3}, {"bollingerMA": 50, "std_dev": 2.5}] + await init_tortoise() + wance_db = await wance_data_stock.WanceDataStock.all() + bollinger_list_lenght = len(bollinger_list) + + for stock_code in wance_db: + for i in range(bollinger_list_lenght): + bollingerMA = bollinger_list[i]['bollingerMA'] + std_dev = bollinger_list[i]['std_dev'] + source_column_name = f'{stock_code} 布林带策略 MA{bollingerMA}-{std_dev}倍标准差' + result = await run_bollinger_backtest(field_list=['close', 'time'], + stock_list=[stock_code.stock_code], + bollingerMA=bollingerMA, + std_dev=std_dev) + + print(f"回测成功 {source_column_name}") + + +if __name__ == '__main__': + # 测试类的回测 + asyncio.run(run_bollinger_backtest(field_list=['close', 'time'], + stock_list=['601222.SH', '601677.SH'], + bollingerMA=20, + std_dev=2)) + + # # 初始化数据库表 + # asyncio.run(init_backtest_db()) diff --git a/src/backtest/dual_moving_average.py b/src/backtest/dual_moving_average.py new file mode 100644 index 0000000..d5c274f --- /dev/null +++ b/src/backtest/dual_moving_average.py @@ -0,0 +1,267 @@ +import asyncio +import json +import time +from datetime import datetime + +import bt +import numpy as np +import pandas as pd +from xtquant import xtdata +import matplotlib.pyplot as plt + +from src.backtest.until import get_local_data, convert_pandas_to_json_serializable +from src.models import wance_data_storage_backtest, wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +# 双均线策略函数 +async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200): + # 生成双均线策略信号 + signal = await dual_ma_strategy(data, short_window, long_window) + + # 使用bt框架构建策略 + strategy = bt.Strategy(f'{stock_code} 双均线策略', + [bt.algos.RunDaily(), + bt.algos.SelectAll(), # 选择所有股票 + bt.algos.WeighTarget(signal), # 根据信号调整权重 + bt.algos.Rebalance()]) # 调仓 + return strategy, signal + + +async def dual_ma_strategy(df, short_window=20, long_window=50): + """ + 基于双均线策略生成买卖信号。 + + 参数: + df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。 + short_window: int, 短期均线窗口期。 + long_window: int, 长期均线窗口期。 + + 返回: + signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。 + """ + # 计算短期均线和长期均线 + short_ma = df.rolling(window=short_window, min_periods=1).mean() + long_ma = df.rolling(window=long_window, min_periods=1).mean() + + # 生成买入信号: 当短期均线从下方穿过长期均线 + buy_signal = np.where(short_ma > long_ma, 1, np.nan) + + # 生成卖出信号: 当短期均线从上方穿过长期均线 + sell_signal = np.where(short_ma < long_ma, 0, np.nan) + + # 合并买卖信号 + signal = pd.DataFrame(buy_signal, index=df.index, columns=df.columns) + signal = np.where(short_ma < long_ma, 0, signal) + + # 前向填充信号，持仓不变 + signal = pd.DataFrame(signal, index=df.index, columns=df.columns).ffill() + + # 将剩余的 NaN 替换为 0 + signal = signal.fillna(0) + + return signal + + +async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window, + long_window): + await init_tortoise() + + # 要存储的字段列表 + fields_to_store = [ + 'stock_code', 'strategy_name', 'stock_close_price', 'daily_price', + 'price', 'returns', 'data_start_time', 'data_end_time', + 'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr', + 'max_drawdown', 'calmar', 'mtd', 'three_month', + 'six_month', 'ytd', 'one_year', 'three_year', + 'five_year', 'ten_year', 'incep', 'daily_sharpe', + 'daily_sortino', 'daily_mean', 'daily_vol', + 'daily_skew', 'daily_kurt', 'best_day', 'worst_day', + 'monthly_sharpe', 'monthly_sortino', 'monthly_mean', + 'monthly_vol', 'monthly_skew', 'monthly_kurt', + 'best_month', 'worst_month', 'yearly_sharpe', + 'yearly_sortino', 'yearly_mean', 'yearly_vol', + 'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year', + 'avg_drawdown', 'avg_drawdown_days', 'avg_up_month', + 'avg_down_month', 'win_year_perc', 'twelve_month_win_perc' + ] + + # 准备要存储的数据 + data_to_store = { + 'stock_code': stock_code, + 'strategy_name': "双均线策略", + 'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign( + time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')), + 'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices), + 'price': convert_pandas_to_json_serializable(result[source_column_name].prices), + 'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)), + 'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'), + 'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'), + 'backtest_end_time': int(datetime.now().strftime('%Y%m%d')), + 'position': convert_pandas_to_json_serializable(signal), + 'backtest_name': f'{stock_code} 双均线策略 MA{short_window}-{long_window}日', + 'indicator_type': 'SMA', + 'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window}) + } + + # 使用循环填充其他字段 + for field in fields_to_store[12:]: # 从第10个字段开始 + value = result.stats.loc[field].iloc[0] + data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value + + # 检查是否存在该 backtest_name + existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=data_to_store['backtest_name'] + ).first() + + if existing_record: + # 如果存在，更新记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + id=existing_record.id + ).update(**data_to_store) + else: + # 如果不存在，创建新的记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store) + + return data_to_store + + +async def run_sma_backtest(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = 100, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + try: + # 初始化一个列表用于存储每只股票的回测结果字典 + results_list = [] + + # 遍历每只股票的数据（每列代表一个股票的收盘价） + data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type, + fill_data, + data_dir) + + for stock_code in stock_list: + + data_column_name = f'close_{stock_code}' + source_column_name = f'{stock_code} 双均线策略' + backtest_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日' + now_data = int(datetime.now().strftime('%Y%m%d')) + db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + + if db_result_data: + if db_result_data[0].backtest_end_time == now_data: + results_list.append({source_column_name: db_result_data[0]}) + + if data_column_name in data.columns: + stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame + stock_data_series.columns = ['close'] # 重命名列为 'close' + + # 创建双均线策略 + strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code, + short_window=short_window, + long_window=long_window) + # 创建回测 + backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000) + # 运行回测 + result = bt.run(backtest) + # 存储回测结果 + data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code, + stock_data_series, + short_window, long_window) + # # 绘制回测结果图表 + # result.plot() + # # 绘制个别股票数据图表 + # plt.figure(figsize=(12, 6)) + # plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price') + # plt.title(f'Stock Price for {stock_code}') + # plt.xlabel('Date') + # plt.ylabel('Price') + # plt.legend() + # plt.grid(True) + # plt.show() + # 将结果存储为字典并添加到列表中 + results_list.append({source_column_name: data_to_store}) + + else: + print(f"数据中缺少列: {data_column_name}") + + return results_list # 返回结果列表 + + except Exception as e: + print(f"Error occurred: {e}") + + +async def start_sma_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + for stock_code in stock_list: + backtest_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日' + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + now_time = int(datetime.now().strftime('%Y%m%d')) + + if db_result and db_result[0].backtest_end_time == now_time: + return db_result + else: + # 执行回测 + result = await run_sma_backtest( + field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir, + short_window=short_window, + long_window=long_window, + ) + return result + + +async def init_backtest_db(): + sma_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30}, + {"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90}, + {"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}] + await init_tortoise() + wance_db = await wance_data_stock.WanceDataStock.all() + sma_list_lenght = len(sma_list) + + for stock_code in wance_db: + for i in range(sma_list_lenght): + short_window = sma_list[i]['short_window'] + long_window = sma_list[i]['long_window'] + source_column_name = f'{stock_code} 双均线策略 MA{short_window}-{long_window}日' + result = await run_sma_backtest(field_list=['close', 'time'], + stock_list=[stock_code.stock_code], + short_window=short_window, + long_window=long_window) + + print(f"回测成功 {source_column_name}") + + +if __name__ == '__main__': + # 测试类的回测 + # asyncio.run(run_sma_backtest(field_list=['close', 'time'], + # stock_list=['601222.SH', '601677.SH'], + # short_window=10, + # long_window=30)) + + # 初始化数据库表 + asyncio.run(init_backtest_db()) diff --git a/src/backtest/living_backtesting.py b/src/backtest/living_backtesting.py new file mode 100644 index 0000000..6da418c --- /dev/null +++ b/src/backtest/living_backtesting.py @@ -0,0 +1,92 @@ +import bt +import pandas as pd +import numpy as np +from xtquant import xtdata +import matplotlib.pyplot as plt + +# 数据的列名 +columns = ['open', 'high', 'low', 'close', 'volume', 'amount', 'settelmentPrice', + 'openInterest', 'preClose', 'suspendFlag'] + + +def get_local_data(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, dividend_type: str, fill_data: bool, data_dir: str): + result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data, + data_dir=data_dir) + return data_processing(result) + + +# 数据处理函数 +def data_processing(result_local): + # 初始化一个空的列表，用于存储每个股票的数据框 + df_list = [] + + # 遍历字典中的 DataFrame + for stock_code, df in result_local.items(): + # 确保 df 是一个 DataFrame + if isinstance(df, pd.DataFrame): + # 将时间戳转换为日期时间格式，只保留年-月-日 + df['time'] = pd.to_datetime(df['time'], unit='ms').dt.date + # 将 'time' 列设置为索引，保留为日期格式 + df.set_index('time', inplace=True) + # 指定列名 + df.columns = columns + # 添加一列 'stock_code' 用于标识不同的股票 + df['stock_code'] = stock_code + # 将 DataFrame 添加到列表中 + df_list.append(df[['close']]) # 只保留 'close' 列 + else: + print(f"数据格式错误: {stock_code} 不包含 DataFrame") + + # 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame + combined_df = pd.concat(df_list, axis=1) + + # 确保返回的 DataFrame 索引是日期格式 + combined_df.index = pd.to_datetime(combined_df.index) + + # 打印最终的 DataFrame + print(combined_df) + + return combined_df + + +def bt_strategy(data): + + # 计算策略信号 + # data = moving_average_strategy(data) + + # 定义策略 + dual_ma_strategy = bt.Strategy('Dual MA Strategy', [bt.algos.RunOnce(), + bt.algos.SelectAll(), + bt.algos.WeighEqually(), + bt.algos.Rebalance()]) + return dual_ma_strategy + + +# 运行回测 +def run_backtest(): + # 生成数据 + data = get_local_data(field_list=[], stock_list=["300391.SZ"], period='1d', start_time='', end_time='', count=-1, + dividend_type='none', fill_data=True, data_dir="") + + # 创建策略 + strategy = bt_strategy(data) + + # 创建回测 + portfolio = bt.Backtest(strategy, data) + result = bt.run(portfolio) + + return result + + +# 执行回测并显示结果 +if __name__ == "__main__": + result = run_backtest() + result.plot() + b = xtdata.get_sector_list() + print(b) + xtdata.download_sector_data() + a = result.stats + print(a) + plt.show() diff --git a/src/backtest/macd_strategy.py b/src/backtest/macd_strategy.py new file mode 100644 index 0000000..7d23359 --- /dev/null +++ b/src/backtest/macd_strategy.py @@ -0,0 +1,271 @@ +import asyncio +import json +import time +from datetime import datetime + +import bt +import numpy as np +import pandas as pd +from xtquant import xtdata +import matplotlib.pyplot as plt + +from src.backtest.until import get_local_data, convert_pandas_to_json_serializable +from src.models import wance_data_storage_backtest, wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +# MACD策略函数 +async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200): + # 生成MACD策略信号 + signal = await macd_strategy(data, short_window, long_window) + + # 使用bt框架构建策略 + strategy = bt.Strategy(f'{stock_code} MACD策略', + [bt.algos.RunDaily(), + bt.algos.SelectAll(), # 选择所有股票 + bt.algos.WeighTarget(signal), # 根据信号调整权重 + bt.algos.Rebalance()]) # 调仓 + return strategy, signal + + +# 定义 MACD 策略的函数 +def macd_strategy(data, short_window=12, long_window=26, signal_window=9): + """ + MACD 策略，当 MACD 线穿过信号线时买入，反之卖出。 + + 参数: + data: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。 + short_window: int, 短期 EMA 的窗口期。 + long_window: int, 长期 EMA 的窗口期。 + signal_window: int, 信号线 EMA 的窗口期。 + + 返回: + signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，-1 表示卖出。 + """ + # 计算短期和长期的 EMA + short_ema = data.ewm(span=short_window, adjust=False).mean() + long_ema = data.ewm(span=long_window, adjust=False).mean() + + # 计算 MACD 线 + macd_line = short_ema - long_ema + + # 计算信号线 + signal_line = macd_line.ewm(span=signal_window, adjust=False).mean() + + # 生成买入和卖出信号 + signal = pd.DataFrame(index=data.index, columns=data.columns) + for column in data.columns: + signal[column] = 0 # 初始化信号为 0 + # 买入信号：MACD 线从下方穿过信号线 + signal[column] = (macd_line[column] > signal_line[column]) & (macd_line[column].shift(1) <= signal_line[column].shift(1)).astype(int) + # 卖出信号：MACD 线从上方穿过信号线 + signal[column] = (macd_line[column] < signal_line[column]) & (macd_line[column].shift(1) >= signal_line[column].shift(1)).astype(int) * -1 + signal[column] + + # 前向填充信号，保持持仓不变 + signal = signal.ffill().fillna(0) + + return signal + + +async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window, + long_window): + await init_tortoise() + + # 要存储的字段列表 + fields_to_store = [ + 'stock_code', 'strategy_name', 'stock_close_price', 'daily_price', + 'price', 'returns', 'data_start_time', 'data_end_time', + 'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr', + 'max_drawdown', 'calmar', 'mtd', 'three_month', + 'six_month', 'ytd', 'one_year', 'three_year', + 'five_year', 'ten_year', 'incep', 'daily_sharpe', + 'daily_sortino', 'daily_mean', 'daily_vol', + 'daily_skew', 'daily_kurt', 'best_day', 'worst_day', + 'monthly_sharpe', 'monthly_sortino', 'monthly_mean', + 'monthly_vol', 'monthly_skew', 'monthly_kurt', + 'best_month', 'worst_month', 'yearly_sharpe', + 'yearly_sortino', 'yearly_mean', 'yearly_vol', + 'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year', + 'avg_drawdown', 'avg_drawdown_days', 'avg_up_month', + 'avg_down_month', 'win_year_perc', 'twelve_month_win_perc' + ] + + # 准备要存储的数据 + data_to_store = { + 'stock_code': stock_code, + 'strategy_name': "MACD策略", + 'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign( + time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')), + 'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices), + 'price': convert_pandas_to_json_serializable(result[source_column_name].prices), + 'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)), + 'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'), + 'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'), + 'backtest_end_time': int(datetime.now().strftime('%Y%m%d')), + 'position': convert_pandas_to_json_serializable(signal), + 'backtest_name': f'{stock_code} MACD策略 MA{short_window}-{long_window}日', + 'indicator_type': 'MACD', + 'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window}) + } + + # 使用循环填充其他字段 + for field in fields_to_store[12:]: # 从第10个字段开始 + value = result.stats.loc[field].iloc[0] + data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value + + # 检查是否存在该 backtest_name + existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=data_to_store['backtest_name'] + ).first() + + if existing_record: + # 如果存在，更新记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + id=existing_record.id + ).update(**data_to_store) + else: + # 如果不存在，创建新的记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store) + + return data_to_store + + +async def run_macd_backtest(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = 100, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + try: + # 初始化一个列表用于存储每只股票的回测结果字典 + results_list = [] + + # 遍历每只股票的数据（每列代表一个股票的收盘价） + data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type, + fill_data, + data_dir) + + for stock_code in stock_list: + + data_column_name = f'close_{stock_code}' + source_column_name = f'{stock_code} MACD策略' + backtest_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日' + now_data = int(datetime.now().strftime('%Y%m%d')) + db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + + if db_result_data: + if db_result_data[0].backtest_end_time == now_data: + results_list.append({source_column_name: db_result_data[0]}) + + elif data_column_name in data.columns: + stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame + stock_data_series.columns = ['close'] # 重命名列为 'close' + + # 创建MACD策略 + strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code, + short_window=short_window, + long_window=long_window) + # 创建回测 + backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000) + # 运行回测 + result = bt.run(backtest) + # 存储回测结果 + data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code, + stock_data_series, + short_window, long_window) + # # 绘制回测结果图表 + # result.plot() + # # 绘制个别股票数据图表 + # plt.figure(figsize=(12, 6)) + # plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price') + # plt.title(f'Stock Price for {stock_code}') + # plt.xlabel('Date') + # plt.ylabel('Price') + # plt.legend() + # plt.grid(True) + # plt.show() + # 将结果存储为字典并添加到列表中 + results_list.append({source_column_name: data_to_store}) + + else: + print(f"数据中缺少列: {data_column_name}") + + return results_list # 返回结果列表 + + except Exception as e: + print(f"Error occurred: {e}") + + +async def start_macd_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + for stock_code in stock_list: + backtest_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日' + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + now_time = int(datetime.now().strftime('%Y%m%d')) + + if db_result and db_result[0].backtest_end_time == now_time: + return db_result + else: + # 执行回测 + result = await run_macd_backtest( + field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir, + short_window=short_window, + long_window=long_window, + ) + return result + + +async def init_backtest_db(): + MACD_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30}, + {"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90}, + {"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}] + await init_tortoise() + wance_db = await wance_data_stock.WanceDataStock.all() + MACD_list_lenght = len(MACD_list) + + for stock_code in wance_db: + for i in range(MACD_list_lenght): + short_window = MACD_list[i]['short_window'] + long_window = MACD_list[i]['long_window'] + source_column_name = f'{stock_code} MACD策略 MA{short_window}-{long_window}日' + result = await run_macd_backtest(field_list=['close', 'time'], + stock_list=[stock_code.stock_code], + short_window=short_window, + long_window=long_window) + + print(f"回测成功 {source_column_name}") + + +if __name__ == '__main__': + # 测试类的回测 + # asyncio.run(run_macd_backtest(field_list=['close', 'time'], + # stock_list=['601222.SH', '601677.SH'], + # short_window=10, + # long_window=30)) + + # 初始化数据库表 + asyncio.run(init_backtest_db()) diff --git a/src/backtest/reverse_dual_ma_strategy.py b/src/backtest/reverse_dual_ma_strategy.py new file mode 100644 index 0000000..2417c53 --- /dev/null +++ b/src/backtest/reverse_dual_ma_strategy.py @@ -0,0 +1,264 @@ +import asyncio +import json +import time +from datetime import datetime + +import bt +import numpy as np +import pandas as pd +from xtquant import xtdata +import matplotlib.pyplot as plt + +from src.backtest.until import get_local_data, convert_pandas_to_json_serializable +from src.models import wance_data_storage_backtest, wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +# 反双均线策略函数 +async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200): + # 生成反双均线策略信号 + signal = await reverse_dual_ma_strategy(data, short_window, long_window) + + # 使用bt框架构建策略 + strategy = bt.Strategy(f'{stock_code} 反双均线策略', + [bt.algos.RunDaily(), + bt.algos.SelectAll(), # 选择所有股票 + bt.algos.WeighTarget(signal), # 根据信号调整权重 + bt.algos.Rebalance()]) # 调仓 + return strategy, signal + + + +# 定义反反双均线策略的函数 +def reverse_dual_ma_strategy(data, short_window=50, long_window=200): + """ + 反反双均线策略，当短期均线跌破长期均线时买入，穿过长期均线时卖出。 + + 参数: + data: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。 + short_window: int, 短期均线的窗口期。 + long_window: int, 长期均线的窗口期。 + + 返回: + signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。 + """ + # 计算短期均线和长期均线 + short_ma = data.rolling(window=short_window).mean() + long_ma = data.rolling(window=long_window).mean() + + # 初始化信号 DataFrame + signal = pd.DataFrame(index=data.index, columns=data.columns) + + # 生成买入信号：短期均线从上往下穿过长期均线 + for column in data.columns: + signal[column] = (short_ma[column] < long_ma[column]).astype(int) # 跌破时买入，信号为1 + signal[column] = (short_ma[column] > long_ma[column]).astype(int) * -1 + signal[column] # 穿过时卖出，信号为0 + + # 前向填充信号，保持持仓不变 + signal = signal.ffill() + + return signal + + +async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window, + long_window): + await init_tortoise() + + # 要存储的字段列表 + fields_to_store = [ + 'stock_code', 'strategy_name', 'stock_close_price', 'daily_price', + 'price', 'returns', 'data_start_time', 'data_end_time', + 'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr', + 'max_drawdown', 'calmar', 'mtd', 'three_month', + 'six_month', 'ytd', 'one_year', 'three_year', + 'five_year', 'ten_year', 'incep', 'daily_sharpe', + 'daily_sortino', 'daily_mean', 'daily_vol', + 'daily_skew', 'daily_kurt', 'best_day', 'worst_day', + 'monthly_sharpe', 'monthly_sortino', 'monthly_mean', + 'monthly_vol', 'monthly_skew', 'monthly_kurt', + 'best_month', 'worst_month', 'yearly_sharpe', + 'yearly_sortino', 'yearly_mean', 'yearly_vol', + 'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year', + 'avg_drawdown', 'avg_drawdown_days', 'avg_up_month', + 'avg_down_month', 'win_year_perc', 'twelve_month_win_perc' + ] + + # 准备要存储的数据 + data_to_store = { + 'stock_code': stock_code, + 'strategy_name': "反双均线策略", + 'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign( + time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')), + 'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices), + 'price': convert_pandas_to_json_serializable(result[source_column_name].prices), + 'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)), + 'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'), + 'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'), + 'backtest_end_time': int(datetime.now().strftime('%Y%m%d')), + 'position': convert_pandas_to_json_serializable(signal), + 'backtest_name': f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日', + 'indicator_type': 'reverse_SMA', + 'indicator_information': json.dumps({'short_window': short_window, 'long_window': long_window}) + } + + # 使用循环填充其他字段 + for field in fields_to_store[12:]: # 从第10个字段开始 + value = result.stats.loc[field].iloc[0] + data_to_store[field] = 0.0 if (isinstance(value, float) and np.isnan(value)) else value + + # 检查是否存在该 backtest_name + existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=data_to_store['backtest_name'] + ).first() + + if existing_record: + # 如果存在，更新记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + id=existing_record.id + ).update(**data_to_store) + else: + # 如果不存在，创建新的记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store) + + return data_to_store + + +async def run_reverse_reverse_SMA_backtest(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = 100, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + try: + # 初始化一个列表用于存储每只股票的回测结果字典 + results_list = [] + + # 遍历每只股票的数据（每列代表一个股票的收盘价） + data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type, + fill_data, + data_dir) + + for stock_code in stock_list: + + data_column_name = f'close_{stock_code}' + source_column_name = f'{stock_code} 反双均线策略' + backtest_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日' + now_data = int(datetime.now().strftime('%Y%m%d')) + db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + + if db_result_data: + if db_result_data[0].backtest_end_time == now_data: + results_list.append({source_column_name: db_result_data[0]}) + + elif data_column_name in data.columns: + stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame + stock_data_series.columns = ['close'] # 重命名列为 'close' + + # 创建反双均线策略 + strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code, + short_window=short_window, + long_window=long_window) + # 创建回测 + backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000) + # 运行回测 + result = bt.run(backtest) + # 存储回测结果 + data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code, + stock_data_series, + short_window, long_window) + # # 绘制回测结果图表 + # result.plot() + # # 绘制个别股票数据图表 + # plt.figure(figsize=(12, 6)) + # plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price') + # plt.title(f'Stock Price for {stock_code}') + # plt.xlabel('Date') + # plt.ylabel('Price') + # plt.legend() + # plt.grid(True) + # plt.show() + # 将结果存储为字典并添加到列表中 + results_list.append({source_column_name: data_to_store}) + + else: + print(f"数据中缺少列: {data_column_name}") + + return results_list # 返回结果列表 + + except Exception as e: + print(f"Error occurred: {e}") + + +async def start_reverse_SMA_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200): + for stock_code in stock_list: + backtest_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日' + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + now_time = int(datetime.now().strftime('%Y%m%d')) + + if db_result and db_result[0].backtest_end_time == now_time: + return db_result + else: + # 执行回测 + result = await run_reverse_reverse_SMA_backtest( + field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir, + short_window=short_window, + long_window=long_window, + ) + return result + + +async def init_backtest_db(): + reverse_SMA_list = [{"short_window": 5, "long_window": 10}, {"short_window": 10, "long_window": 30}, + {"short_window": 30, "long_window": 60}, {"short_window": 30, "long_window": 90}, + {"short_window": 70, "long_window": 140}, {"short_window": 120, "long_window": 250}] + await init_tortoise() + wance_db = await wance_data_stock.WanceDataStock.all() + reverse_SMA_list_lenght = len(reverse_SMA_list) + + for stock_code in wance_db: + for i in range(reverse_SMA_list_lenght): + short_window = reverse_SMA_list[i]['short_window'] + long_window = reverse_SMA_list[i]['long_window'] + source_column_name = f'{stock_code} 反双均线策略 MA{short_window}-{long_window}日' + result = await run_reverse_reverse_SMA_backtest(field_list=['close', 'time'], + stock_list=[stock_code.stock_code], + short_window=short_window, + long_window=long_window) + + print(f"回测成功 {source_column_name}") + + +if __name__ == '__main__': + # 测试类的回测 + # asyncio.run(run_reverse_SMA_backtest(field_list=['close', 'time'], + # stock_list=['601222.SH', '601677.SH'], + # short_window=10, + # long_window=30)) + + # 初始化数据库表 + asyncio.run(init_backtest_db()) diff --git a/src/backtest/router.py b/src/backtest/router.py new file mode 100644 index 0000000..4eed6ea --- /dev/null +++ b/src/backtest/router.py @@ -0,0 +1,23 @@ +from fastapi import APIRouter, HTTPException # 从 FastAPI 中导入 APIRouter，用于创建 API 路由器 + +from src.backtest.service import start_backtest_service +from src.pydantic.backtest_request import BackRequest + +router = APIRouter() # 创建一个 FastAPI 路由器实例 + + +@router.get("/start_backtest") +async def start_backtest(request: BackRequest): + result = await start_backtest_service(field_list=['close', 'time'], + stock_list=request.stock_list, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + count=request.count, + dividend_type=request.dividend_type, + fill_data=request.fill_data, + ma_type=request.ma_type, + short_window=request.short_window, + long_window=request.long_window + ) + return result diff --git a/src/backtest/rsi_strategy.py b/src/backtest/rsi_strategy.py new file mode 100644 index 0000000..9832db2 --- /dev/null +++ b/src/backtest/rsi_strategy.py @@ -0,0 +1,296 @@ +import asyncio +import json +import time +from datetime import datetime + +import bt +import numpy as np +import pandas as pd +from xtquant import xtdata +import matplotlib.pyplot as plt + +from src.backtest.until import get_local_data, convert_pandas_to_json_serializable +from src.models import wance_data_storage_backtest, wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +# RSI策略函数 +async def create_dual_ma_strategy(data, stock_code: str, short_window: int = 50, long_window: int = 200, + overbought: int = 70, oversold: int = 30): + # 生成RSI策略信号 + signal = await rsi_strategy(data, short_window, long_window, overbought, oversold) + + # 使用bt框架构建策略 + strategy = bt.Strategy(f'{stock_code} RSI策略', + [bt.algos.RunDaily(), + bt.algos.SelectAll(), # 选择所有股票 + bt.algos.WeighTarget(signal), # 根据信号调整权重 + bt.algos.Rebalance()]) # 调仓 + return strategy, signal + + +async def rsi_strategy(df, short_window=14, long_window=28, overbought=70, oversold=30): + """ + 基于RSI的策略生成买卖信号。 + + 参数: + df: pd.DataFrame, 股票的价格数据，行索引为日期，列为股票代码。 + short_window: int, 短期RSI的窗口期。 + long_window: int, 长期RSI的窗口期。 + overbought: int, 超买水平。 + oversold: int, 超卖水平。 + + 返回: + signal: pd.DataFrame, 每只股票的买卖信号，1 表示买入，0 表示卖出。 + """ + delta = df.diff().fillna(0) + + gain = (delta.where(delta > 0, 0).rolling(window=short_window).mean()).fillna(0) + loss = (-delta.where(delta < 0, 0).rolling(window=short_window).mean()).fillna(0) + + short_rsi = (100 - (100 / (1 + (gain / loss)))).fillna(0) + + long_gain = (delta.where(delta > 0, 0).rolling(window=long_window).mean()).fillna(0) + long_loss = (-delta.where(delta < 0, 0).rolling(window=long_window).mean()).fillna(0) + + long_rsi = (100 - (100 / (1 + (long_gain / long_loss)))).fillna(0) + + signal = pd.DataFrame(index=df.index, columns=df.columns) + + for column in df.columns: + signal[column] = np.where((short_rsi[column] < 30) & (long_rsi[column] < 30) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 1, 0) + signal[column] = np.where((short_rsi[column] > 70) & (long_rsi[column] > 70) & (short_rsi[column] != 0) & (long_rsi[column] != 0), 0, signal[column]) + + return signal.ffill().fillna(0) + + +async def storage_backtest_data(source_column_name, result, signal, stock_code, stock_data_series, short_window: int, + long_window: int, overbought: int = 70, + oversold: int = 30): + await init_tortoise() + + # 要存储的字段列表 + fields_to_store = [ + 'stock_code', 'strategy_name', 'stock_close_price', 'daily_price', + 'price', 'returns', 'data_start_time', 'data_end_time', + 'backtest_end_time', 'position', 'backtest_name', 'rf', 'total_return', 'cagr', + 'max_drawdown', 'calmar', 'mtd', 'three_month', + 'six_month', 'ytd', 'one_year', 'three_year', + 'five_year', 'ten_year', 'incep', 'daily_sharpe', + 'daily_sortino', 'daily_mean', 'daily_vol', + 'daily_skew', 'daily_kurt', 'best_day', 'worst_day', + 'monthly_sharpe', 'monthly_sortino', 'monthly_mean', + 'monthly_vol', 'monthly_skew', 'monthly_kurt', + 'best_month', 'worst_month', 'yearly_sharpe', + 'yearly_sortino', 'yearly_mean', 'yearly_vol', + 'yearly_skew', 'yearly_kurt', 'best_year', 'worst_year', + 'avg_drawdown', 'avg_drawdown_days', 'avg_up_month', + 'avg_down_month', 'win_year_perc', 'twelve_month_win_perc' + ] + + # 准备要存储的数据 + data_to_store = { + 'stock_code': stock_code, + 'strategy_name': "RSI策略", + 'stock_close_price': json.dumps(stock_data_series.fillna(0).rename_axis('time').reset_index().assign( + time=stock_data_series.index.strftime('%Y%m%d')).set_index('time').to_dict(orient='index')), + 'daily_price': convert_pandas_to_json_serializable(result[source_column_name].daily_prices), + 'price': convert_pandas_to_json_serializable(result[source_column_name].prices), + 'returns': convert_pandas_to_json_serializable(result[source_column_name].returns.fillna(0)), + 'data_start_time': pd.to_datetime(result.stats.loc["start"].iloc[0]).strftime('%Y%m%d'), + 'data_end_time': pd.to_datetime(result.stats.loc["end"].iloc[0]).strftime('%Y%m%d'), + 'backtest_end_time': int(datetime.now().strftime('%Y%m%d')), + 'position': convert_pandas_to_json_serializable(signal), + 'backtest_name': f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}-overbought{overbought}-oversold{oversold}', + 'indicator_type': 'RSI', + 'indicator_information': json.dumps( + {'short_window': short_window, 'long_window': long_window, 'overbought': overbought, 'oversold': oversold}) + } + + # 使用循环填充其他字段 + for field in fields_to_store[12:]: # 从第12个字段开始 + value = result.stats.loc[field].iloc[0] + + if isinstance(value, float): + if np.isnan(value): + data_to_store[field] = 0.0 # NaN 处理为 0 + elif np.isinf(value): # 判断是否为无穷大或无穷小 + if value > 0: + data_to_store[field] = 99999.9999 # 正无穷处理 + else: + data_to_store[field] = -99999.9999 # 负无穷处理 + else: + data_to_store[field] = value # 正常的浮点值 + else: + data_to_store[field] = value # 非浮点类型保持不变 + + # 检查是否存在该 backtest_name + existing_record = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=data_to_store['backtest_name'] + ).first() + + if existing_record: + # 如果存在，更新记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + id=existing_record.id + ).update(**data_to_store) + else: + # 如果不存在，创建新的记录 + await wance_data_storage_backtest.WanceDataStorageBacktest.create(**data_to_store) + + return data_to_store + + +async def run_rsi_backtest(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = 100, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200, + overbought: int = 70, + oversold: int = 30 + ): + try: + # 初始化一个列表用于存储每只股票的回测结果字典 + results_list = [] + + # 遍历每只股票的数据（每列代表一个股票的收盘价） + data = await get_local_data(field_list, stock_list, period, start_time, end_time, count, dividend_type, + fill_data, + data_dir) + + for stock_code in stock_list: + + data_column_name = f'close_{stock_code}' + source_column_name = f'{stock_code} RSI策略' + backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}' + now_data = int(datetime.now().strftime('%Y%m%d')) + db_result_data = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + + if db_result_data: + if db_result_data[0].backtest_end_time == now_data: + results_list.append({source_column_name: db_result_data[0]}) + + # elif data_column_name in data.columns: + if data_column_name in data.columns: + stock_data_series = data[[data_column_name]] # 提取该股票的收盘价 DataFrame + stock_data_series.columns = ['close'] # 重命名列为 'close' + + # 创建RSI策略 + strategy, signal = await create_dual_ma_strategy(stock_data_series, stock_code, + short_window=short_window, long_window=long_window) + # 创建回测 + backtest = bt.Backtest(strategy=strategy, data=stock_data_series, initial_capital=100000) + # 运行回测 + result = bt.run(backtest) + # 存储回测结果 + data_to_store = await storage_backtest_data(source_column_name, result, signal, stock_code, + stock_data_series, short_window, long_window, overbought, + oversold) + # # 绘制回测结果图表 + # result.plot() + # # 绘制个别股票数据图表 + # plt.figure(figsize=(12, 6)) + # plt.plot(stock_data_series.index, stock_data_series['close'], label='Stock Price') + # plt.title(f'Stock Price for {stock_code}') + # plt.xlabel('Date') + # plt.ylabel('Price') + # plt.legend() + # plt.grid(True) + # plt.show() + # 将结果存储为字典并添加到列表中 + results_list.append({source_column_name: data_to_store}) + + else: + print(f"数据中缺少列: {data_column_name}") + + return results_list # 返回结果列表 + + except Exception as e: + print(f"Error occurred: {e}") + + +async def start_rsi_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + short_window: int = 50, + long_window: int = 200, + overbought: int = 70, + oversold: int = 30 + ): + for stock_code in stock_list: + backtest_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}' + db_result = await wance_data_storage_backtest.WanceDataStorageBacktest.filter( + backtest_name=backtest_name) + now_time = int(datetime.now().strftime('%Y%m%d')) + + if db_result and db_result[0].backtest_end_time == now_time: + return db_result + else: + # 执行回测 + result = await run_rsi_backtest( + field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir, + short_window=short_window, + long_window=long_window, + overbought=overbought, + oversold=oversold + ) + return result + + +async def init_backtest_db(): + sma_list = [{"short_window": 3, "long_window": 6}, {"short_window": 6, "long_window": 12}, + {"short_window": 12, "long_window": 24}, {"short_window": 14, "long_window": 18}, + {"short_window": 15, "long_window": 10}] + await init_tortoise() + wance_db = await wance_data_stock.WanceDataStock.all() + sma_list_lenght = len(sma_list) + + for stock_code in wance_db: + for i in range(sma_list_lenght): + short_window = sma_list[i]['short_window'] + long_window = sma_list[i]['long_window'] + source_column_name = f'{stock_code} RSI策略 RSI{short_window}-RSI{long_window}' + result = await start_rsi_backtest_service(field_list=['close', 'time'], + stock_list=[stock_code.stock_code], + short_window=short_window, + long_window=long_window, + overbought=70, + oversold=30) + + print(f"回测成功 {source_column_name}") + + +if __name__ == '__main__': + # 测试类的回测 + asyncio.run(run_rsi_backtest(field_list=['close', 'time'], + stock_list=['601222.SH', '601677.SH'], + count=-1, + short_window=10, + long_window=30, + overbought=70, + oversold=30 + )) + + # # 初始化数据库表 + # asyncio.run(init_backtest_db()) diff --git a/src/backtest/service.py b/src/backtest/service.py new file mode 100644 index 0000000..e07e9b8 --- /dev/null +++ b/src/backtest/service.py @@ -0,0 +1,74 @@ +from src.backtest.bollinger_bands import run_bollinger_backtest, start_bollinger_backtest_service +from src.backtest.dual_moving_average import run_sma_backtest, start_sma_backtest_service +from src.backtest.reverse_dual_ma_strategy import start_reverse_SMA_backtest_service +from src.backtest.rsi_strategy import start_rsi_backtest_service +from src.backtest.until import data_check + + +async def start_backtest_service(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', + ma_type: str = 'SMA', + short_window: int = 50, + long_window: int = 200, + bollingerMA: int = 200, + std_dev: int = 200, + overbought: int = 70, + oversold: int = 30, + signal_window: int = 9): + # 数据检查 + await data_check(field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir=data_dir) + + # 策略映射 + strategies = { + 'SMA': start_sma_backtest_service, + 'Bollinger': start_bollinger_backtest_service, + 'RSI': start_rsi_backtest_service, + 'RESMA': start_reverse_SMA_backtest_service, + 'MACD': start_rsi_backtest_service + } + + # 通用参数 + base_params = { + 'field_list': field_list, + 'stock_list': stock_list, + 'period': period, + 'start_time': start_time, + 'end_time': end_time, + 'count': count, + 'dividend_type': dividend_type, + 'fill_data': fill_data, + 'data_dir': data_dir, + } + + # 特定策略参数 + strategy_params = { + 'SMA': {'short_window': short_window, 'long_window': long_window}, + 'Bollinger': {'bollingerMA': bollingerMA, 'std_dev': std_dev}, + 'RSI': {'short_window': short_window, 'long_window': long_window, 'overbought': overbought, + 'oversold': oversold}, + 'RESMA': {'short_window': short_window, 'long_window': long_window}, + 'MACD': {'short_window': short_window, 'long_window': signal_window} + } + + # 选择策略并执行 + strategy_func = strategies.get(ma_type) + if strategy_func: + result = await strategy_func(**base_params, **strategy_params[ma_type]) + return result + else: + return None diff --git a/src/backtest/until.py b/src/backtest/until.py new file mode 100644 index 0000000..253919c --- /dev/null +++ b/src/backtest/until.py @@ -0,0 +1,99 @@ +import json +from datetime import datetime + +import numpy as np +from xtquant import xtdata +import pandas as pd + +# 数据的列名 +columns = ['open', 'high', 'low', 'close', 'volume', 'amount', 'settelmentPrice', + 'openInterest', 'preClose', 'suspendFlag'] + + +# 获取本地数据并进行处理 +async def get_local_data(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, dividend_type: str, fill_data: bool, data_dir: str): + result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data, + data_dir=data_dir) + return await data_processing(result) + + +async def data_processing(result_local): + # 初始化一个空的列表，用于存储每个股票的数据框 + df_list = [] + + # 遍历字典中的 DataFrame + for stock_code, df in result_local.items(): + # 确保 df 是一个 DataFrame + if isinstance(df, pd.DataFrame): + # 将时间戳转换为日期时间格式，只保留年-月-日 + df['time'] = pd.to_datetime(df['time'], unit='ms').dt.date + # 将 'time' 列设置为索引，保留为日期格式 + df.set_index('time', inplace=True) + # 将 'close' 列重命名为 'close_股票代码' + df.rename(columns={'close': f'close_{stock_code}'}, inplace=True) + # 将 DataFrame 添加到列表中 + df_list.append(df[[f'close_{stock_code}']]) # 只保留 'close_股票代码' 列 + else: + print(f"数据格式错误: {stock_code} 不包含 DataFrame") + + # 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame + combined_df = pd.concat(df_list, axis=1) + + # 确保返回的 DataFrame 索引是日期格式 + combined_df.index = pd.to_datetime(combined_df.index) + + return combined_df + + +def convert_pandas_to_json_serializable(data: pd.Series) -> str: + """ + 将 Pandas Series 或 DataFrame 中的 Timestamp 索引转换为字符串，并返回 JSON 可序列化的结果。 + + 参数: + data: pd.Series 或 pd.DataFrame, 带有时间戳索引的 pandas 数据 + + 返回: + JSON 字符串，键为日期字符串，值为原数据的值。 + """ + # 判断数据类型 + if isinstance(data, (pd.Series, pd.DataFrame)): + # 如果索引是时间戳类型，则转换为 YYYYMMDD 格式 + if isinstance(data.index, pd.DatetimeIndex): + data.index = data.index.strftime('%Y%m%d') + + # 处理 NaN 和 None 的情况，替换为 0 或其他合适的默认值 + data = data.replace([np.nan, None], 0) + + # 将索引重置为普通列，然后转换为字典 + json_serializable_data = data.rename_axis('date').reset_index().to_dict(orient='records') + + # 将字典转换为 JSON 格式字符串 + json_string = json.dumps(json_serializable_data) + return json_string + else: + raise ValueError("输入必须为 Pandas Series 或 DataFrame") + + +async def data_check(field_list: list, + stock_list: list, + period: str = '1d', + start_time: str = '', + end_time: str = '', + count: int = -1, + dividend_type: str = 'none', + fill_data: bool = True, + data_dir: str = '', ): + result_data = xtdata.get_local_data(field_list=[], stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data, + data_dir=data_dir) + time_now = int(datetime.now().strftime('%Y%m%d')) + for i in stock_list: + close = int(result_data.get(i).index[-1]) + if close != 0 and close < time_now: + xtdata.download_history_data(stock_code=i, + period='1d', + start_time='', + end_time='', + incrementally=True) diff --git a/src/constants.py b/src/constants.py new file mode 100644 index 0000000..e1eab81 --- /dev/null +++ b/src/constants.py @@ -0,0 +1,43 @@ +from enum import Enum + +THISPOLICYNAME = '' +def setThisPolicyName(name): + THISPOLICYNAME = name + +def getThisPolicyName(): + return THISPOLICYNAME + +DB_NAMING_CONVENTION = { + "ix": "%(column_0_label)s_idx", + "uq": "%(table_name)s_%(column_0_name)s_key", + "ck": "%(table_name)s_%(constraint_name)s_check", + "fk": "%(table_name)s_%(column_0_name)s_fkey", + "pk": "%(table_name)s_pkey", +} + + +class RedisKeyConstants: + SMS_CODE_KEY = "sms:code:" + TOKEN_KEY = "Youcailogin:" + ACCESS_TOKEN_KEY = "applet_access_token_youcai" + SESSION_KEY = "session_id_youcai:" + OFFICIAL_ACCESS_TOKEN_KEY = "official_access_token_youcai" + + +class Environment(str, Enum): + LOCAL = "LOCAL" + STAGING = "STAGING" + TESTING = "TESTING" + PRODUCTION = "PRODUCTION" + + @property + def is_debug(self): + return self in (self.LOCAL, self.STAGING, self.TESTING) + + @property + def is_testing(self): + return self == self.TESTING + + @property + def is_deployed(self) -> bool: + return self in (self.STAGING, self.PRODUCTION) diff --git a/readme.md b/src/data_processing/__init__.py similarity index 100% rename from readme.md rename to src/data_processing/__init__.py diff --git a/src/data_processing/history_data_processing.py b/src/data_processing/history_data_processing.py new file mode 100644 index 0000000..63c1531 --- /dev/null +++ b/src/data_processing/history_data_processing.py @@ -0,0 +1,412 @@ +import asyncio +from datetime import datetime + +import akshare as ak +import pandas as pd +from xtquant import xtdata + +from src.models import wance_data_stock +from src.tortoises_orm_config import init_tortoise +from src.utils.history_data_processing_utils import translation_dict, get_best_match, safe_get_value, on_progress +from src.utils.split_stock_utils import split_stock_code, join_stock_code, percent_to_float +from src.xtdata.service import download_history_data_service, get_full_tick_keys_service, download_history_data2_service + +# period - 周期，用于表示要获取的周期和具体数据类型 +period_list = ["1d", "1h", "30m", "15m", "5m", "1m", "tick", "1w", "1mon", "1q", "1hy", "1y"] +# 数据的列名 +columns = ['open', 'high', 'low', 'close', 'volume', 'amount', 'settelmentPrice', + 'openInterest', 'preClose', 'suspendFlag'] + + +def processing_data(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, count: int, + dividend_type: str, fill_data: bool): + """ + + :param field_list: [] + :param stock_list: ["186511.SH","173312.SH","231720.SH","173709.SH","019523.SH"] + :param period: "1d" + :param start_time: "20240506" + :param end_time: "" + :param count: -1 + :param dividend_type: "none" + :param fill_data: False + :return: + """ + try: + # 获取本地数据 + result_local = xtdata.get_local_data(field_list=field_list, + stock_list=stock_list, + period=period, + start_time=start_time, + end_time=end_time, + count=count, + dividend_type=dividend_type, + fill_data=fill_data, + data_dir="" + ) + + # 初始化一个空的列表，用于存储每个股票的数据框 + df_list = [] + # 遍历字典中的 DataFrame + for stock_code, df in result_local.items(): + # 确保 df 是一个 DataFrame + if isinstance(df, pd.DataFrame): + # 将时间戳转换为日期时间格式，并格式化为字符串 'YYYYMMDD' + df['time'] = pd.to_datetime(df['time'], unit='ms').dt.strftime('%Y%m%d') + # 将 'time' 列设置为索引 + df.set_index('time', inplace=True) + # 指定列名 + df.columns = columns + # 添加一列 'stock_code' 用于标识不同的股票 + df['stock_code'] = stock_code + # 将 DataFrame 添加到列表中 + df_list.append(df) + else: + print(f"数据格式错误: {stock_code} 不包含 DataFrame") + + # 使用 pd.concat() 将所有 DataFrame 合并为一个大的 DataFrame + combined_df = pd.concat(df_list) + + # 打印合并后的 DataFrame + print(combined_df) + + print(f"开始获取股票数据{result_local}") + except Exception as e: + print(f"处理数据发生错误: {str(e)}") + + +def history_data_processing(): + """ + 本地路径 D:\\e海方舟-量化交易版\\userdata_mini\\datadir + :return: + """ + + try: + result = xtdata.get_full_tick(code_list=['SH', 'SZ']) + result = list(result.keys()) + # for key in result: + # xtdata.download_history_data(stock_code=key, period="1d", start_time="", end_time="", incrementally="") + datas = xtdata.get_local_data(field_list=[], stock_list=result, period='1d', start_time='', end_time='', + count=-1, + dividend_type='none', fill_data=True) + # datas = xtdata.download_history_data2(stock_list=result, period="1d", start_time="", end_time="", + # callback=on_progress) + print(datas, "这里是返回的数据") + except Exception as e: + print(f"处理数据发生错误: {str(e)}") + + +async def init_indicator(): + await init_tortoise() + # 从数据库中获取股票列表 + stock_list = await wance_data_stock.WanceDataStock.filter(stock_type__contains=["stock"]).all() + stock_zh_a_spot_em_df = ak.stock_zh_a_spot_em() + + # 遍历股票列表拿到股票实体 + for stock in stock_list: + try: + # 使用 akshare 获取股票指标数据 + stock_code = stock.stock_code # 假设 stock_code 是股票代码的字段 + stock_code_suffix = split_stock_code(stock_code) # 提取股票代码部分 + stock_code_xq = join_stock_code(stock_code_suffix) + stock_code_front = stock_code_suffix[0] + # 筛选匹配的行 + match = stock_zh_a_spot_em_df.loc[stock_zh_a_spot_em_df['代码'] == stock_code_front, '涨跌幅'] + + # 从akshare中获取数据 + stock_a_indicator_lg_df = ak.stock_a_indicator_lg(symbol=stock_code_front) # 乐咕乐股-A 股个股指标: 市盈率, 市净率, 股息率 + stock_financial_abstract_ths_df = ak.stock_financial_abstract_ths(symbol=stock_code_front, + indicator="按报告期") # 同花顺-财务指标-主要指标 + stock_financial_abstract_df = ak.stock_financial_abstract(symbol=stock_code_front) # 新浪财经-财务报表-关键指标 + stock_individual_spot_xq_df = ak.stock_individual_spot_xq(symbol=stock_code_xq) # 雪球-行情中心-个股 + stock_zh_valuation_baidu_df = ak.stock_zh_valuation_baidu(symbol=stock_code_front, indicator="市现率", + period="近一年") # 百度股市通-A 股-财务报表-估值数据 + stock_fhps_detail_em_df = ak.stock_fhps_detail_em(symbol=stock_code_front) # 东方财富网-数据中心-分红送配-分红送配详情 + + # 查询数据库中是否已有该股票的数据 + existing_record = await wance_data_stock.WanceDataStock.filter(stock_code=stock_code).first() + + if existing_record is None: + print(f"未找到股票记录: {stock_code}") + continue + + # 处理并插入数据到数据库 + last_indicator_row = stock_a_indicator_lg_df.iloc[-1] + last_abstract_row = stock_financial_abstract_ths_df.iloc[0] + last_financial_row = stock_financial_abstract_df.iloc[:, 2] + last_spot_xq_row = stock_individual_spot_xq_df.iloc[:, 1] + last_baidu_df_row = stock_zh_valuation_baidu_df.iloc[-1] + last_detail_em_row = stock_fhps_detail_em_df.iloc[-1] + + # 更新字段的对应数据 + # 每股指标模块 + existing_record.financial_dividend = safe_get_value(last_detail_em_row.get('现金分红-现金分红比例')) + existing_record.financial_ex_gratia = safe_get_value(last_spot_xq_row[8]) + existing_record.financial_cash_flow = safe_get_value(last_financial_row[10]) + existing_record.financial_asset_value = safe_get_value(last_financial_row[9]) + existing_record.financial_reserve_per = safe_get_value(last_abstract_row.get('每股资本公积金')) + existing_record.financial_undistributed_profit = safe_get_value(last_abstract_row.get('每股未分配利润')) + + # 盈利能力模块 + existing_record.profit_asset_value = safe_get_value(last_financial_row.iloc[33]) + existing_record.profit_sale_ratio = safe_get_value(last_financial_row.iloc[43]) + existing_record.profit_gross_rate = safe_get_value(last_financial_row.iloc[44]) + existing_record.profit_business_increase = safe_get_value(last_financial_row.iloc[53]) + existing_record.profit_dividend_rate = safe_get_value(last_spot_xq_row.iloc[26]) + + # 成长能力 + existing_record.growth_Income_rate = percent_to_float( + safe_get_value(last_abstract_row.get('营业总收入同比增长率', 0.0))) + existing_record.growth_growth_rate = safe_get_value(last_financial_row.iloc[46]) + existing_record.growth_nonnet_profit = percent_to_float( + safe_get_value(last_abstract_row.get('扣非净利润同比增长率', 0.0))) + existing_record.growth_attributable_rate = safe_get_value(last_financial_row.iloc[54]) + + # 估值指标 + existing_record.valuation_PEGTTM_ratio = safe_get_value(last_indicator_row.get('pe_ttm')) + existing_record.valuation_PEG_percentile = safe_get_value(last_indicator_row.get('pe')) + existing_record.valuation_PB_TTM = safe_get_value(last_indicator_row.get('ps')) + existing_record.valuation_PB_percentile = safe_get_value(last_indicator_row.get('pb')) + existing_record.valuation_PTS_TTM = safe_get_value(last_indicator_row.get('dv_ratio')) + existing_record.valuation_PTS_percentile = safe_get_value(last_indicator_row.get('ps_ttm')) + existing_record.valuation_market_TTM = safe_get_value(last_baidu_df_row[-1]) + existing_record.valuation_market_percentile = safe_get_value(1 / last_baidu_df_row[-1] * 100) if \ + last_baidu_df_row[-1] != 0 else 0 + + # 行情指标 + existing_record.market_indicator = safe_get_value(match.values[0]) if not match.empty else 0 + + # 保存更改 + await existing_record.save() + + print(f"更新股票指标成功！: {stock_code}") + + except Exception as e: + print(f"处理股票 {stock_code} 时发生错误: {e}") + continue # 继续处理下一个股票 + + +async def init_stock_pool(incremental: bool = False): + """ + 初始化股票池参数，包括股票名、股票代码、股票上市时间、股票板块等信息。 + @param incremental: 是否执行增量下载 + @type incremental: bool + """ + await init_tortoise() + + # 获取所有现有股票代码 + existing_stocks = set() + if incremental: + existing_stocks = {stock.stock_code for stock in await wance_data_stock.WanceDataStock.all()} + + # 初始化股票池 + tick_result = xtdata.get_full_tick(['SH', 'SZ']) + stocks_to_create = [] # 使用一个列表批量创建 + for key in tick_result.keys(): + if incremental and key in existing_stocks: + print(f"股票代码 {key} 已经存在，跳过...\n") + continue + + detail_result = xtdata.get_instrument_detail(key, False) + InstrumentName_result = detail_result.get("InstrumentName") + start_time = detail_result.get("OpenDate") or detail_result.get("CreateDate") + end_time = datetime.now().strftime('%Y%m%d') + time_expire = detail_result.get("ExpireDate") + type_dict = xtdata.get_instrument_type(key) + type_list = [] + if type_dict: + for i in type_dict.keys(): + type_list.append(i) + + # 只在 type_list 包含 "stock" 时继续执行 + if "stock" in type_list: + # 检查股票是否已存在 + existing_stock = await wance_data_stock.WanceDataStock.filter(stock_code=key).first() + if not existing_stock: + stocks_to_create.append( + wance_data_stock.WanceDataStock( + stock_code=key, + stock_name=InstrumentName_result, + stock_sector=[], # 初始化为空列表，后续会加入板块 + stock_type=type_list, + time_start=start_time, + time_end=end_time, + time_expire=time_expire, + market_sector=key.rsplit('.', 1)[-1] + ) + ) + print(f"加载成功 股票名称 {InstrumentName_result} 股票代码 {key} 股票类型 {type_list} \n") + else: + print(f"股票代码 {key} 已经存在，跳过...\n") + else: + print(f"跳过非股票类型：{key} 类型：{type_list} \n") + + # 如果有新的股票，批量创建所有新股票记录 + if stocks_to_create: + bulk_db_result = await wance_data_stock.WanceDataStock.bulk_create(stocks_to_create) + print(bulk_db_result, "股票池创建完成 \n") + else: + print("没有新的股票需要创建 \n") + + # 获取并更新sector模块 + sector_list = xtdata.get_sector_list() + + for sector in sector_list: + # 使用模糊匹配找到最佳的中文板块匹配 + best_match = get_best_match(sector, translation_dict) + if best_match: + translated_sector = translation_dict[best_match] # 获取对应的英文名称 + else: + print(f"没有找到合适的板块匹配：{sector} \n") + continue # 如果没有找到匹配，跳过该板块 + + # 获取板块对应的股票列表 + sector_stock = xtdata.get_stock_list_in_sector(sector) + # 获取所有相关股票 + stocks_to_update = await wance_data_stock.WanceDataStock.filter(stock_code__in=sector_stock) + + # 遍历并更新每个股票的sector，避免重复添加相同的英文板块 + for stock in stocks_to_update: + if translated_sector not in stock.stock_sector: # 检查是否已经存在该英文板块 + stock.stock_sector.append(translated_sector) + await stock.save() # 保存更新后的数据 + else: + print(f"{stock.stock_code} 已经包含板块 {translated_sector}, 跳过重复添加 \n") + + print(f"更新板块完成 {sector}: {translated_sector} \n") + + print(f"所有股票已经加载完成 \n") + + + """ + # 初始化股票池参数 股票名、股票代码、股票上市时间、股票板块、股票最后回测时间、股票退市时间、股票所属市场 + @param incremental:是否执行增量下载 + @type incremental:bool + @return: + @rtype: + """ + + +""" +async def init_stock_pool(incremental: bool = False): + # 这行代码存在一个问题就是，存入的板块信息，是中文的，在查询的时候应为是中文的数据所以没有办法被选中 + + await init_tortoise() + + # 获取所有现有股票代码 + existing_stocks = set() + if incremental: + existing_stocks = {stock.stock_code for stock in await wance_data_stock.WanceDataStock.all()} + + # 初始化股票池 + tick_result = xtdata.get_full_tick(['SH', 'SZ']) + stocks_to_create = [] # 使用一个列表批量创建 + for key in tick_result.keys(): + # 如果是增量更新，且该股票已经存在，则跳过 + if incremental and key in existing_stocks: + continue + + detail_result = xtdata.get_instrument_detail(key, False) + InstrumentName_result = detail_result.get("InstrumentName") + start_time = detail_result.get("OpenDate") or detail_result.get("CreateDate") + end_time = datetime.now().strftime('%Y%m%d') + time_expire = detail_result.get("ExporeDate") + type_dict = xtdata.get_instrument_type(key) + type_list = [] + if type_dict: + for i in type_dict.keys(): + type_list.append(i) + + stocks_to_create.append( + wance_data_stock.WanceDataStock( + stock_code=key, + stock_name=InstrumentName_result, + stock_sector=[], + stock_type=type_list, + time_start=start_time, + time_end=end_time, + time_expire=time_expire, + market_sector=key.rsplit('.', 1)[-1] + ) + ) + print(f"加载成功 股票名称 {InstrumentName_result} 股票代码 {key} 股票类型 {type_list} \n") + + # 如果有新的股票，批量创建所有新股票记录 + if stocks_to_create: + bulk_db_result = await wance_data_stock.WanceDataStock.bulk_create(stocks_to_create) + print(bulk_db_result, "股票池创建完成 \n") + else: + print("没有新的股票需要创建 \n") + + # 获取并更新sector模块 + sector_list = xtdata.get_sector_list() + if incremental: + # 获取已经更新过的sector + updated_sectors = set() + # 获取所有已经存在的股票及其相关板块 + existing_stock_sectors = await wance_data_stock.WanceDataStock.all().values('stock_code', 'stock_sector') + for stock_info in existing_stock_sectors: + for sector in stock_info['stock_sector']: + updated_sectors.add(sector) + + # 过滤出没有更新过的sector + sector_list = [sector for sector in sector_list if sector not in updated_sectors] + + for sector in sector_list: + sector_stock = xtdata.get_stock_list_in_sector(sector) + # 获取所有相关股票 + stocks_to_update = await wance_data_stock.WanceDataStock.filter(stock_code__in=sector_stock) + + # 遍历并更新每个股票的sector，避免重复添加 + for stock in stocks_to_update: + if sector not in stock.stock_sector: + stock.stock_sector.append(sector) + await stock.save() # 保存更新后的数据 + print(f"更新板块完成 {stock.stock_code}: {stock.stock_sector} \n") + + print(f"所有股票已经加载完成 \n") +""" + +""" +# 每次都会操作数据库 +async def init_stock_pool(): + await init_tortoise() + + # 初始化股票池 + tick_result = xtdata.get_full_tick(['SH', 'SZ']) + tick_keys = list(tick_result.keys()) + for key in tick_keys: + detail_result = xtdata.get_instrument_detail(key, False) + InstrumentName_result = detail_result.get("InstrumentName") + type_result = xtdata.get_instrument_type(key) + # 创建新的股票记录 + await wance_data_stock.WanceDataStock.create( + stock_code=key, + stock_name=InstrumentName_result, + stock_sector="", + stock_type=type_result + ) + + # 获取并更新sector模块 + sector_list = xtdata.get_sector_list() + for sector in sector_list: + sector_stock = xtdata.get_stock_list_in_sector(sector) + for stock in sector_stock: + # 更新已有股票的sector + await wance_data_stock.WanceDataStock.filter(stock_code=stock).update(stock_sector=sector) +""" + +if __name__ == '__main__': + # processing_data(field_list=[], + # stock_list=["000062.SZ","600611.SH"], + # period="1d", + # start_time="20240506", + # end_time="", + # count=-1, + # dividend_type="none", + # fill_data=False, + # ) + # history_data_processing() + asyncio.run(init_stock_pool(False)) + asyncio.run(init_indicator()) +# xtdata.run() diff --git a/src/data_processing/response_factor.py b/src/data_processing/response_factor.py new file mode 100644 index 0000000..6f6f000 --- /dev/null +++ b/src/data_processing/response_factor.py @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/src/exceptions.py b/src/exceptions.py new file mode 100644 index 0000000..0e45d3f --- /dev/null +++ b/src/exceptions.py @@ -0,0 +1,92 @@ +from typing import Any + +from fastapi import FastAPI, HTTPException, Request, status +from fastapi.encoders import jsonable_encoder +from fastapi.exceptions import RequestValidationError +from fastapi.responses import JSONResponse +from starlette.exceptions import HTTPException as StarletteHTTPException + +from src.utils.helpers import first + + +def register_exception_handler(app: FastAPI): + @app.exception_handler(RequestValidationError) + async def validation_exception_handler(request: Request, exc: RequestValidationError): + """请求参数验证错误处理""" + error = first(exc.errors()) + field = error.get('loc')[1] or '' + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=jsonable_encoder({ + "status_code": status.HTTP_422_UNPROCESSABLE_ENTITY, + "message": "{} param {}".format(field, error.get('msg')), + "detail": exc.errors()}), + ) + + @app.exception_handler(StarletteHTTPException) + async def http_exception_handler(request: Request, exc: StarletteHTTPException): + """Http 异常处理""" + return JSONResponse( + status_code=exc.status_code, + content=jsonable_encoder({ + "status_code": exc.status_code, + "message": exc.detail}), + ) + + @app.exception_handler(Exception) + async def exception_callback(request: Request, exc: Exception): + + """其他异常处理，遇到其他问题再自定义异常""" + return JSONResponse( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + content=jsonable_encoder({ + "status_code": status.HTTP_500_INTERNAL_SERVER_ERROR, + "message": "Internal Server Error", + # "detail": ''.join(exc.args) + }), + ) + + +class CommonHttpException(HTTPException): + def __init__(self, detail, status_code, **kwargs: dict[str, Any]) -> None: + super().__init__(status_code=status_code, detail=detail, **kwargs) + + +class DetailedHTTPException(HTTPException): + STATUS_CODE = status.HTTP_500_INTERNAL_SERVER_ERROR + DETAIL = "Server error" + + def __init__(self, **kwargs: dict[str, Any]) -> None: + super().__init__(status_code=self.STATUS_CODE, detail=self.DETAIL, **kwargs) + + +class PermissionDenied(DetailedHTTPException): + STATUS_CODE = status.HTTP_403_FORBIDDEN + DETAIL = "Permission denied" + + +class NotFound(DetailedHTTPException): + STATUS_CODE = status.HTTP_404_NOT_FOUND + + +class BadRequest(DetailedHTTPException): + STATUS_CODE = status.HTTP_400_BAD_REQUEST + DETAIL = "Bad Request" + + +class UnprocessableEntity(DetailedHTTPException): + STATUS_CODE = status.HTTP_422_UNPROCESSABLE_ENTITY + DETAIL = "Unprocessable entity" + + +class NotAuthenticated(DetailedHTTPException): + STATUS_CODE = status.HTTP_401_UNAUTHORIZED + DETAIL = "User not authenticated" + + +class WxResponseError(DetailedHTTPException): + STATUS_CODE = status.HTTP_400_BAD_REQUEST + DETAIL = "请求微信异常" + + def __init__(self) -> None: + super().__init__(headers={"WWW-Authenticate": "Bearer"}) diff --git a/src/main.py b/src/main.py new file mode 100644 index 0000000..9f9cac5 --- /dev/null +++ b/src/main.py @@ -0,0 +1,52 @@ +import sentry_sdk +import uvicorn +from fastapi import FastAPI +from starlette.middleware.cors import CORSMiddleware + +from src.exceptions import register_exception_handler +from src.tortoises import register_tortoise_orm +from src.xtdata.router import router as xtdata_router +from src.backtest.router import router as backtest_router + +from xtquant import xtdata +from src.settings.config import app_configs, settings + + + + +app = FastAPI(**app_configs) + +register_tortoise_orm(app) + +register_exception_handler(app) + +app.include_router(xtdata_router, prefix="/getwancedata", tags=["盘口数据"]) +app.include_router(backtest_router, prefix="/backtest", tags=["盘口数据"]) + + +if settings.ENVIRONMENT.is_deployed: + sentry_sdk.init( + dsn=settings.SENTRY_DSN, + environment=settings.ENVIRONMENT, + ) + +app.add_middleware( + CORSMiddleware, + allow_origins=settings.CORS_ORIGINS, + allow_origin_regex=settings.CORS_ORIGINS_REGEX, + allow_credentials=True, + allow_methods=("GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"), + allow_headers=settings.CORS_HEADERS, +) + + + + +@app.get("/") +async def root(): + return {"message": "Hello, FastAPI!"} + + + +if __name__ == "__main__": + uvicorn.run('src.main:app', host="0.0.0.0", port=8011, reload=True) diff --git a/src/models/__init__.py b/src/models/__init__.py new file mode 100644 index 0000000..508eccf --- /dev/null +++ b/src/models/__init__.py @@ -0,0 +1,292 @@ +from datetime import datetime +from decimal import Decimal +from enum import Enum, IntEnum +from typing import Optional + +from pydantic import BaseModel + +from src.settings.config import settings + + +def with_table_name(table_name: str): + return f"{settings.DATABASE_PREFIX}{table_name}" + + +class Decimals: + MAX_DIGITS = 15 + DECIMAL_PLACES = 3 + DECIMAL_PLACES_MORE = 6 + + +class SecurityPriceCode(Enum): + TRANSFER_FEE = "fee_1" # 过户费 + + STAMP_TAX = "fee_2" # 印花税 + + TOTAL_COMMISSION = "csf_fee" # 总证券佣金 + + +class UserSecurityExtraReminded(BaseModel): + """ + reminded_target_buy_price: 最近提醒目标买入价 + reminded_target_buy_time: 目标买入价最近提醒时间 + reminded_buy_price: 最近提醒买入价 + reminded_buy_fluctuation: 最近提醒买入波动 + reminded_buy_time: 买入最近提醒时间 + reminded_sell_price: 最近提醒目标卖出价 + reminded_sell_fluctuation: 最近提醒卖出波动 + reminded_sell_time: 卖出最近提醒时间 + buy_up_first_interval: 买入上涨初次提醒波动间隔 + buy_up_continue_interval: 买入继续上涨提醒波动间隔 + buy_down_first_interval: 买入下跌初次提醒波动间隔 + buy_down_continue_interval: 买入继续下跌提醒波动间隔 + sell_down_first_interval: 卖出下跌初次提醒波动间隔 + sell_down_continue_interval: 卖出继续下跌提醒波动间隔 + browse_msg_alarm_flag: 浏览器消息提醒通知设置 + wx_msg_alarm_flag: 微信公众号消息提醒通知设置 + """ + reminded_target_buy_price: int = 0 + reminded_target_buy_time: Optional[datetime] = None + reminded_buy_price: Decimal = 0.000 + reminded_buy_fluctuation: int = 0 + reminded_buy_time: Optional[datetime] = None + reminded_sell_price: Decimal = 0.000 + reminded_sell_fluctuation: int = 0 + reminded_sell_time: Optional[datetime] = None + buy_up_first_interval: int = 1 + buy_up_continue_interval: int = 1 + buy_down_first_interval: int = -1 + buy_down_continue_interval: int = -1 + sell_down_first_interval: int = -1 + sell_down_continue_interval: int = -1 + browse_msg_alarm_flag: bool = True + wx_msg_alarm_flag: bool = True + + class Config: + json_encoders = {Decimal: str} + + +class Status(IntEnum): + """ + 状态 + 0-正常 + 1-删除 + 2-禁用 + 3-退市 + """ + # 正常 + NORMAL = 0 + STOP = 1 + DISABLED = 2 + DELISTED = 3 + + +class StockType(str, Enum): + """ + 股票类型 + """ + SH = "sh" + SZ = "sz" + + +class UserSecurityAccountType(IntEnum): + """ + 用户中国证券账户账户类型 + """ + + NORMAL = 0 # 普通账户 + + MARGIN = 1 # 融资账户 + + +class ExchangeType(str, Enum): + """ + 交易类型 + """ + SH = "SH" + SZ = "SZ" + NQ = "NQ" + BJ = "BJ" + + +class SellReferType(IntEnum): + """ + 参考卖出类型 + highest-最高卖出价 + latest-最近卖出价 + """ + HIGHEST = 0 # 最高卖出价 + LATEST = 1 # 最近卖出价 + + +class InvestmentSource(IntEnum): + """ + 投资资金来源 + """ + CASH = 0 # 现金 + FINANCING = 1 # 场内融资 + LOANED = 2 # 场外借贷 + + +class SecuritySource(IntEnum): + """ + 证券获得方式 + """ + BUY = 0 # 正常买入 + BONUS_SHARES = 1 # 送股 + CONVERT_SHARES = 2 # 转增股 + + +class StatusSell(IntEnum): + """ + 售出状态 + """ + + UNSOLD = 0 # 未售出 + PARTIAL_SOLD = 1 # 部分售出 + ALL_SOLD = 2 # 全部售出 + + +class BuyAndSellType(IntEnum): + """ + 卖出卖出记录类型 + """ + COMMISSION = 0 # 委托单 + CONDITION = 1 # 条件单 + CONTRACT = 2 # 成交单 + + +class SecurityType(IntEnum): + """ + 证券类型 + """ + + security_stock_a = 0 # A 股股票 + + security_stock_b = 1 # B 股股票 + + security_etf = 2 # 场内基金-ETF + + security_lof = 3 # 场内基金-LOF + + +class PushConfigModel(BaseModel): + """ + 驼峰转下线 + """ + device_id: int = None + device_name: str = None + eable_flag: str = None + update_at: Optional[datetime] = None + + +class CashFlowType(IntEnum): + INTO = 0 # 转入 + OUT = 1 # 转出 + + +class AdminStatus(str, Enum): + """ + 管理员账户状态 + """ + + DELETE = "delete" # 删除 + NORMAL = "normal" # 正常 + FORBIDDEN = "forbidden" # 禁用 + UNAUTHORIZED = "unauthorized" # 未验证 + + +class CustomerSecurityAccountSecurityAdminStatus(str, Enum): + """ + 客户的证券账户中的券商状态 + """ + JOIN_GROUP = "join_group" # 新入群 + ACCOUNT_READY_NOT_DEPOSIT = "account_ready_not_deposit" # 已开户但未入金 + ACCOUNT_SUBSTANDARD = "account_substandard" # 非有效户(20个交易日里平均市值1万) + ACCOUNT_UNSETTLED = "account_unsettled" # 待结算 + ACCOUNT_SETTLED = "account_settled" # 已结算 + + +class CustomerSecurityAccountVipStatus(str, Enum): + """ + 客户的证券账户中的vip状态 + """ + REWARDED = "rewarded" # 已奖励 + UNREWARD = "unreward" # 未奖励 + + +class UserBenefitStatus(IntEnum): + """ + 用户福利领取状态 + """ + UNRECEIVED = 0 # 未领取 + RECEIVED = 1 # 已领取 + + +class SecurityAdminTransferStatus(IntEnum): + """ + 券商转账的状态 + """ + PAYED = 1 # 已支付 + CONFIRMED = 2 # 已确认 + DISAGREED = 3 # 有异议 + + +class YuanMaTalkQuestionPublicStatus(IntEnum): + """ + 问题是否公开 + """ + PUBLIC = 1 # 公开 + PRIVATE = 0 # 私密 + + +class YuanMaTalkQuestionStatus(str, Enum): + """ + 问题状态 + """ + NEW = "new" # 新问题 + REPLIED = "replied" # 已回复 + WAITING = "waiting" # 待回复 + CLOSED = "closed" # 关闭 + DELETED = "deleted" # 删除 + FORBIDDEN = "forbidden" # 禁止 + + +class LastReplySourceType(str, Enum): + """ + 问题状态 + """ + ADMIN = "admin" # 管理后台 + USER = "user" # 用户端 + + +class AccountType(IntEnum): + """ + 账户类型 + """ + LIANGRONG = 1 # 两融账户 + ORDINARY = 0 # 普通用户 + + +class supervisionFee(IntEnum): + """ + 深市是否申请监管费 + """ + NOT_CHARGE = 1 # 不收 + CHARGE = 0 # 收 + + +class transferFee(IntEnum): + """ + 账户类型 + """ + NOT_CHARGE = 1 # 不收 + CHARGE = 0 # 收 + + +class IsType(IntEnum): + """ + 是否默认 + """ + YES = 1 # 是 + NO = 0 # 否 diff --git a/src/models/__pycache__/__init__.cpython-311.pyc b/src/models/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0f0082b4c28670ceba057c2b05393c2267f34c6 GIT binary patch literal 12289 zcmbtaYj6`+mTt+dm){Tk24l>_RuTx0B+M{LW@Je=VrA(O(g@Lo2~DU#XA`0e=w2%80DLXe^bFQ7nv{< z)=mUCsUlFaCU<2%gAz+|sHfo$O1x)nb5H(L&0v392j8;xm1*+iQ z#8;2$!{*o6K#iUmWWtu$m~iD)J;Qtom-GqLQltuyYM#9cWB3#<8LOjM4PdprMTON< ztPZex-k`!7DAoX2Baf}J&?;PVtBn+G0(6t6W)sDl0c$Bya}z~d0d3RNY^GQ{U>zE) zg<_q6ZPs9|6x#w=mj-L2*jB*0HCQ{vwgJ|o!8$0m9k3l5xph)(Ct$lY^)^$i7qGq( z+;5?%70@S2pj{Mw640kgpj#=r8_+$PUb`vwG+=u**fxso18lzr>!H{Iz@915<93QZ z3+Qtt&>a*#2|9N?>+$HAkm`luMJ&aA~w(1276d`pE zo8Q)N^EtT@uXDh4%&HU6e27@9`_C@`bsRczEE0^39|3X(#y5TX~bmK=8*t^AHdwge3pT^Y9Q18t}(60bQ60=)<}|MOYtT!xaHTm<7HJ#4HO~%+*2B 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diff --git a/src/models/back_observed_data.py b/src/models/back_observed_data.py new file mode 100644 index 0000000..ce6662e --- /dev/null +++ b/src/models/back_observed_data.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class BackObservedData(Model): + id = fields.IntField(pk=True, description='id表', ) + key = fields.CharField(max_length=255, null=True, description='key', ) + observed_data = fields.BinaryField(null=True, description='格式化后的json数据', ) + + + class Meta: + table = with_table_name("back_observed_data") \ No newline at end of file diff --git a/src/models/back_observed_data_detail.py b/src/models/back_observed_data_detail.py new file mode 100644 index 0000000..3b23197 --- /dev/null +++ b/src/models/back_observed_data_detail.py @@ -0,0 +1,12 @@ +from tortoise import Model, fields +from src.models import with_table_name + + +class BackObservedDataDetail(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=255, ) + back_observed_data = fields.BinaryField(null=True, ) + + + class Meta: + table = with_table_name("back_observed_data_detail") diff --git a/src/models/back_position.py b/src/models/back_position.py new file mode 100644 index 0000000..4646eaa --- /dev/null +++ b/src/models/back_position.py @@ -0,0 +1,14 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class BackPosition(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=25, null=True, ) + back_position_data = fields.BinaryField(null=True, ) + + + + class Meta: + table = with_table_name("back_position") \ No newline at end of file diff --git a/src/models/back_result_indicator.py b/src/models/back_result_indicator.py new file mode 100644 index 0000000..ce3b07b --- /dev/null +++ b/src/models/back_result_indicator.py @@ -0,0 +1,14 @@ +from tortoise import Model, fields +from src.models import with_table_name + + + +class BackResultIndicator(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=25, null=True, ) + indicator = fields.BinaryField(null=True, ) + + + class Meta: + table = with_table_name("back_result_indicator") + diff --git a/src/models/back_trand_info.py b/src/models/back_trand_info.py new file mode 100644 index 0000000..f80f8f9 --- /dev/null +++ b/src/models/back_trand_info.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name + + + +class BackTrandInfo(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=25, null=True, ) + trade_info = fields.BinaryField(null=True, ) + + + class Meta: + table = with_table_name("back_trand_info") \ No newline at end of file diff --git a/src/models/backtest.py b/src/models/backtest.py new file mode 100644 index 0000000..6ec1224 --- /dev/null +++ b/src/models/backtest.py @@ -0,0 +1,25 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class Backtest(Model): + id = fields.IntField(pk=True) + strategy: fields.ForeignKeyRelation[Strategy] = fields.ForeignKeyField( + model_name="models.Strategy", + related_name="integration_strategy", + # db_constraint=False, + on_delete=fields.CASCADE, + index=True, + ) + key = fields.CharField(max_length=20, unique=True, index=True, description="回测key") + user_id = fields.IntField(null=True, description="回测用户") + backtest_at = fields.DatetimeField(auto_now_add=True, description="回测时间") + backtest_code = fields.TextField(description="回测代码") + is_running = fields.BooleanField(default=True, description="回测状态") + updated_at = fields.DatetimeField(auto_now=True, description="修改时间") + deleted_at = fields.DatetimeField(null=True, description="删除时间") + is_active = fields.BooleanField(default=True, description="是否可用") + + class Meta: + table = with_table_name("Backtest") diff --git a/src/models/backtest_parameters.py b/src/models/backtest_parameters.py new file mode 100644 index 0000000..0c1ee0b --- /dev/null +++ b/src/models/backtest_parameters.py @@ -0,0 +1,23 @@ +from pydantic import BaseModel, Field +from typing import Dict + + +class BenchmarkCodes(BaseModel): + """ + 模型类用于表示基准名称和对应代码的映射。 + """ + codes: Dict[str, str] = Field( + default_factory=lambda: { + "沪深300": "000300", # 使用字符串来表示代码 + "中证500": "000905" + }, + title="Benchmark Codes", + description="A mapping of benchmark names to their respective codes." + ) + + # 添加一个属性或方法来根据基准名称返回代码 + async def get_code(self, benchmark_name: str) -> str: + if benchmark_name in self.codes: + return self.codes[benchmark_name] + else: + raise ValueError(f"Unknown benchmark name: {benchmark_name}") diff --git a/src/models/observed_data.py b/src/models/observed_data.py new file mode 100644 index 0000000..3ba07ae --- /dev/null +++ b/src/models/observed_data.py @@ -0,0 +1,123 @@ +from objectbox.model import Entity, Id, Property, PropertyType + + +@Entity(id=1, uid=1) +class ObservedData: + id = Id(id=1, uid=1001) + key = Property(str, id=2, uid=1002) + date = Property(str, id=3, uid=1003) + time_return = Property(float, type=PropertyType.float, id=4, uid=1004) + cash = Property(float, type=PropertyType.float, id=5, uid=1005) + benchmark_return = Property(float, type=PropertyType.float, id=6, uid=1006) + is_running = Property(bool, id=7, uid=1007) + benchmark_ratio = Property(float, type=PropertyType.float, id=8, uid=1008) + max_draw_down = Property(float, type=PropertyType.float, id=9, uid=1009) + strategy_ratio = Property(float, type=PropertyType.float, id=10, uid=1010) + + + +@Entity(id=2, uid=2) +class BackResultIndicator: + id = Id(id=5, uid=2001) + key = Property(str, id=6, uid=2002) + indicator = Property(str, id=7, uid=2003) + + +@Entity(id=3, uid=3) +class Position: + id = Id(id=1, uid=3001) + key = Property(str, id=2, uid=3002) + date = Property(str, id=3, uid=3003) + name = Property(str, id=4, uid=3004) + size = Property(int, id=5, uid=3005) + price = Property(float, id=6, uid=3006) + adjbase = Property(float, id=7, uid=3007) + profit_loss = Property(float, id=8, uid=3008) + + +@Entity(id=4, uid=4) +class TradeInfo: + id = Id(id=1, uid=4001) + key = Property(str, id=2, uid=4002) + stock_code = Property(str, id=3, uid=4003) + ordtype = Property(str, id=4, uid=4004) + executed_size = Property(int, id=5, uid=4005) + executed_price = Property(float, id=6, uid=4006) + value = Property(float, id=7, uid=4007) + size = Property(int, id=8, uid=4008) + price = Property(float, id=9, uid=4009) + status = Property(str, id=10, uid=4010) + pnl = Property(float, id=11, uid=4011) + date = Property(str, id=13, uid=4013) + commission = Property(float, id=14, uid=4014) + + +@Entity(id=5, uid=5) +class TranObserverData: + id = Id(id=1, uid=5001) + key = Property(str, id=2, uid=5002) + date = Property(str, id=3, uid=5003) + time_return = Property(float, type=PropertyType.float, id=4, uid=5004) + cumulative_return = Property(float, type=PropertyType.float, id=5, uid=5005) + trades = Property(float, type=PropertyType.float, id=6, uid=5006) + total_revenue = Property(float, type=PropertyType.float, id=7, uid=5007) + current_price = Property(float, type=PropertyType.float, id=8, uid=5008) + open = Property(float, type=PropertyType.float, id=9, uid=5009) + close = Property(float, type=PropertyType.float, id=10, uid=50010) + high = Property(float, type=PropertyType.float, id=11, uid=50011) + low = Property(float, type=PropertyType.float, id=12, uid=50012) + volume = Property(float, type=PropertyType.float, id=13, uid=50013) + cash = Property(float, type=PropertyType.float, id=14, uid=50014) + annualized_return = Property(float, type=PropertyType.float, id=15, uid=50015) + + +@Entity(id=6, uid=6) +class TranReturn: + id = Id(id=1, uid=6001) + key = Property(str, id=2, uid=6002) + date = Property(str, id=3, uid=6003) + time_return = Property(float, type=PropertyType.float, id=4, uid=6004) + benchmark_return = Property(float, type=PropertyType.float, id=5, uid=6005) + cumulative_return = Property(float, type=PropertyType.float, id=6, uid=6006) + max_draw_down = Property(float, type=PropertyType.float, id=7, uid=6007) + + +@Entity(id=7, uid=7) +class TranOrders: + id = Id(id=1, uid=7001) + key = Property(str, id=2, uid=7002) + order_return = Property(str, id=3, uid=7003) + + +@Entity(id=8, uid=8) +class TranTradeInfo: + id = Id(id=1, uid=8001) + key = Property(str, id=2, uid=8002) + stock_code = Property(str, id=3, uid=8003) + ordtype = Property(str, id=4, uid=8004) + executed_size = Property(int, id=5, uid=8005) + executed_price = Property(float, id=6, uid=8006) + value = Property(float, id=7, uid=8007) + size = Property(int, id=8, uid=8008) + price = Property(float, id=9, uid=8009) + status = Property(str, id=10, uid=8010) + pnl = Property(float, id=11, uid=8011) + date = Property(str, id=13, uid=8013) + commission = Property(float, id=14, uid=8014) + exec_type = Property(str, id=15, uid=8015) + trail_stop = Property(float, id=16, uid=8016) + trail_price_limit = Property(float, id=17, uid=8017) + + +@Entity(id=9, uid=9) +class TranData: + id = Id(id=1, uid=9001) + key = Property(str, id=2, uid=9002) + alpha = Property(float,type=PropertyType.float, id=3,uid=9003) + beta = Property(float, type=PropertyType.float, id=4,uid=9004) + sharpe_ratio = Property(float, id=5,uid=9005) + start_date = Property(str,id=6, uid=9006) + close_date = Property(str, id=7, uid=9007) + use_date = Property(str, id=8, uid=9008) + filename = Property(str, id=11, uid=1011) + day_alpha_array = Property(str,id=12,uid=1012) \ No newline at end of file diff --git a/src/models/order.py b/src/models/order.py new file mode 100644 index 0000000..a428d5b --- /dev/null +++ b/src/models/order.py @@ -0,0 +1,68 @@ +from tortoise.models import Model +from tortoise import fields, models +from tortoise.contrib.pydantic import pydantic_model_creator + +from src.models import with_table_name, AccountType + + +class Order(Model): + id = fields.IntField(pk=True, description="订单ID") + stock_code = fields.CharField(max_length=30, description="股票代码") + stock_name = fields.CharField(max_length=30, description="股票名称") + limit_price = fields.FloatField(description="限价") + order_quantity = fields.IntField(description="委托数量") + order_amount = fields.FloatField(description="委托金额") + order_type = fields.CharField(max_length=20, description="订单类型") # 例如：买入、卖出 + position = fields.CharField(max_length=30, description="仓位") + user_id = fields.IntField(null=False, description="用户Id") + entrust_date = fields.DatetimeField(null=True, description="委托日期") + entrust_time = fields.TimeField(null=True) + + class Meta: + table = with_table_name("orders") + + +OrderResponse = pydantic_model_creator( + Order +) + + +class Entrust(Model): + id = fields.IntField(pk=True, description="主键id") + fund_account = fields.IntField(null=True, description="资金账户") + securities_alias = fields.CharField(max_length=30, null=True, description="账户别名") + account_type = fields.IntEnumField(AccountType, null=True, description="账户类型") + stock_code = fields.CharField(max_length=30, description="证券代码") + stock_name = fields.CharField(max_length=30, null=False, description="证券名称") + limit_price = fields.FloatField(description="委托价") + entrust_number = fields.IntField(description="委托数量") + deal_price = fields.FloatField(null=True, max_digits=10, description="成交价格") + deal_number = fields.IntField(null=True, description="成家数量") + order_type = fields.CharField(max_length=30, description="操作") # 买入卖出 + entrust_date = fields.DateField(null=True, description="委托日期") + entrust_money = fields.FloatField(description="委托金额") + # Python + is_repair = fields.BooleanField(default=False, index=True, description="是否补单") + + entrust_time = fields.TimeField(null=True) # 委托时间 + + class Meta: + table = with_table_name("Entrust") + + +EntrustResponse = pydantic_model_creator( + Entrust +) + + +class Backtesting(Model): + id = fields.IntField(pk=True, description="主键id") + key = fields.CharField(max_length=30, description="回测key") + + class Meta: + table = with_table_name("backtest") + + +BacktestingResponse = pydantic_model_creator( + Backtesting +) diff --git a/src/models/page_Info.py b/src/models/page_Info.py new file mode 100644 index 0000000..35e7293 --- /dev/null +++ b/src/models/page_Info.py @@ -0,0 +1,65 @@ +from datetime import datetime + +from pydantic import BaseModel, Field +from typing import Optional, List + +from src.pydantics.transaction import TransactionsPydantic, TransactionPydantic + + +class PageInfo(BaseModel): + page: Optional[int] + pageSize: Optional[int] + searchname: Optional[str] + + + +# class PageResponse(BaseModel): +# current_page: int +# page_size: int +# total_pages: int +# total_items: int +# items: List[dict] # 使用 List[dict] 类型 +# +# # 单个事务的 Pydantic 模型 +# class TransactionPagePydantic(BaseModel): +# id: int +# key: str +# cash: float +# transaction_name: str +# transaction_type: Optional[str] # 可以为 None +# user_id: Optional[int] = None +# is_running: bool +# is_deleted: bool +# process_id: Optional[int] = None +# bar: Optional[str] = None +# created_at: datetime +# updated_at: datetime +# stopped_at: Optional[str] = None +# deleted_at: Optional[str] = None +# strategy_id: int +# +# class Config: +# orm_mode = True +# from_attributes = True +# +# # 分页的 Pydantic 模型 +# class PageData(BaseModel): +# results: List[TransactionPydantic] +# total: int +# page: int +# pages: int +# size: int +# # next: Optional[str] = None +# # previous: Optional[str] = None +# # total_pages: int +# +# # 包装响应的 Pydantic 模型 +# class EntityResponse(BaseModel): +# status_code: int +# message: str +# data: PageData + + + + + diff --git a/src/models/position.py b/src/models/position.py new file mode 100644 index 0000000..16991fe --- /dev/null +++ b/src/models/position.py @@ -0,0 +1,36 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class Position(Model): + id = fields.IntField(pk=True, description="主键") + key = fields.CharField(max_length=30, description="键") + date = fields.DateField(description="日期") + name = fields.CharField(max_length=30, description="名称") + size = fields.IntField(description="数量") + price = fields.FloatField(description="价格") + adjbase = fields.FloatField(description="复权基数") + profit = fields.FloatField(description="利润") + + class Meta: + table = with_table_name("position") + + +def create(id,pk,key, date, name, size, price, adjbase, profit): + try: + position = Position(id=id,pk=pk,key=key, date=date, name=name, size=size, price=price, adjbase=adjbase, profit=profit) + position.save() + return position + except Exception as e: + print(f"Position create error: {str(e)}") + return None + + +def bulk_create(param): + try: + Position.bulk_create(param) + return "Position bulk create success" + except Exception as e: + print(f"Position bulk create error: {str(e)}") + return None \ No newline at end of file diff --git a/src/models/security_account.py b/src/models/security_account.py new file mode 100644 index 0000000..1ff3711 --- /dev/null +++ b/src/models/security_account.py @@ -0,0 +1,31 @@ +from tortoise import Model, fields + +from src.models import with_table_name, AccountType, supervisionFee, transferFee, IsType + +from tortoise.contrib.pydantic import pydantic_model_creator + + +class SecurityAccount(Model): + """ + 证券账户 + """ + id = fields.IntField(pk=True, description="主键") + securities_name = fields.CharField(max_length=30, null=False, description="证券公司名字昵称") + + fund_account = fields.BigIntField(null=True, description="资金账户") + + + account_alias = fields.CharField(max_length=30, null=False, description="账户别名") + + money = fields.FloatField(null=True, description="账户金额") + available_money = fields.FloatField(null=True, description="可用金额") + available_proportion = fields.FloatField(null=True, description="可用资金占比") + freeze = fields.FloatField(null=True, description="冻结金额") + + class Meta: + table = with_table_name("security_account") + + +SecurityAccountResponse = pydantic_model_creator( + SecurityAccount +) diff --git a/src/models/snowball.py b/src/models/snowball.py new file mode 100644 index 0000000..767819e --- /dev/null +++ b/src/models/snowball.py @@ -0,0 +1,21 @@ +from tortoise import Model, fields + +from src.models import with_table_name + +from tortoise.contrib.pydantic import pydantic_model_creator + + +class Snowball(Model): + """ + 雪球相关信息 + """ + id = fields.IntField(pk=True, description="主键") + snowball_token = fields.CharField(max_length=10000,null=True, description="雪球用户的token") + + class Meta: + table = with_table_name("snowball") + + +SnowballResponse = pydantic_model_creator( + Snowball +) diff --git a/src/models/stock.py b/src/models/stock.py new file mode 100644 index 0000000..74fdeea --- /dev/null +++ b/src/models/stock.py @@ -0,0 +1,23 @@ +from tortoise import Model, fields +from tortoise.contrib.pydantic import pydantic_model_creator + +from src.models import with_table_name, StockType + + +class Stock(Model): + """ + 股票相关信息 + """ + id = fields.IntField(pk=True, description="主键") + stock_code = fields.CharField(max_length=30, description="股票代码") + stock_name = fields.CharField(max_length=30, null=True, description="股票名称") + type = fields.CharEnumField(StockType, null=True, description="类型") + stock_pinyin = fields.CharField(max_length=30, description="股票拼音") + + class Meta: + table = with_table_name("stock") + + +StockResponse = pydantic_model_creator( + Stock +) diff --git a/src/models/stock_bt_history.py b/src/models/stock_bt_history.py new file mode 100644 index 0000000..011670e --- /dev/null +++ b/src/models/stock_bt_history.py @@ -0,0 +1,17 @@ +from tortoise import Model, fields +from src.models import with_table_name + +class StockBtHistory(Model): + id = fields.IntField(pk=True, ) + end_bt_time = fields.CharField(max_length=8, null=True,description='回测最终时间', ) + bt_stock_code = fields.CharField(max_length=8, null=True,description='回测股票代码', ) + bt_stock_name = fields.CharField(max_length=10, null=True, description='回测股票名称', ) + bt_benchmark_code = fields.CharField(max_length=8, null=True,description='股票基准代码', ) + bt_stock_period = fields.CharField(max_length=10, null=True, description='回测类型', ) + bt_strategy_name = fields.CharField(max_length=10, null=True, description='回测策略名称', ) + bt_stock_data = fields.BinaryField(null=True, description='回测股票数据', ) + + + + class Meta: + table = with_table_name("stock_bt_history") \ No newline at end of file diff --git a/src/models/stock_data_processing.py b/src/models/stock_data_processing.py new file mode 100644 index 0000000..bcf16f7 --- /dev/null +++ b/src/models/stock_data_processing.py @@ -0,0 +1,19 @@ +from tortoise import Model, fields +from tortoise.contrib.pydantic import pydantic_model_creator + +from src.models import with_table_name, StockType + + +class StockDataProcessing(Model): + bt_benchmark_code = fields.CharField(max_length=6, null=True, description='基准代码', ) + bt_stock_period = fields.CharField(max_length=10, null=True, description='数据类型', ) + bt_strategy_name = fields.CharField(max_length=10, null=True, description='回测策略名', ) + id = fields.IntField(pk=True, ) + processing_data = fields.BinaryField(null=True, description='清洗后的数据', ) + prosessing_date = fields.CharField(max_length=10, null=True, description='当前回测时间', ) + stock_name = fields.CharField(max_length=10, null=True, ) + stocke_code = fields.CharField(max_length=10, null=True, ) + + + class Meta: + table = with_table_name("stock_data_processing") diff --git a/src/models/stock_details.py b/src/models/stock_details.py new file mode 100644 index 0000000..2f53022 --- /dev/null +++ b/src/models/stock_details.py @@ -0,0 +1,25 @@ +from tortoise import Model, fields +from tortoise.contrib.pydantic import pydantic_model_creator + +from src.models import with_table_name, StockType + + +class StockDetails(Model): + """ + 股票相关信息 + """ + id = fields.IntField(pk=True, description="主键") + stock_code = fields.CharField(max_length=30, description="股票代码") + stock_name = fields.CharField(max_length=30, null=True, description="股票名称") + type = fields.CharEnumField(StockType, null=True, description="类型") + stock_pinyin = fields.CharField(max_length=30, description="股票拼音") + latest_price = fields.FloatField(null=True, description="最新价") + rise_fall = fields.FloatField(null=True, description="跌涨幅") + + class Meta: + table = with_table_name("stock_details") + + +StockDetailsResponse = pydantic_model_creator( + StockDetails +) diff --git a/src/models/stock_history.py b/src/models/stock_history.py new file mode 100644 index 0000000..50ba323 --- /dev/null +++ b/src/models/stock_history.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name +class StockHistory(Model): + id = fields.IntField(pk=True, ) + stock_code = fields.IntField(description='股票代码', ) + stock_name = fields.CharField(max_length=10, null=True, ) + start_time_to_market = fields.CharField(max_length=10, null=True, description='股票上市时间', ) + end_bt_time = fields.CharField(max_length=10, null=True, description='最终回测时间', ) + symbol_data = fields.BinaryField(null=True, description='股票数据', ) + + + class Meta: + table = with_table_name("stock_history") \ No newline at end of file diff --git a/src/models/strategy.py b/src/models/strategy.py new file mode 100644 index 0000000..fc01fdd --- /dev/null +++ b/src/models/strategy.py @@ -0,0 +1,19 @@ +from tortoise import Model, fields +from src.models import with_table_name + + +class Strategy(Model): + id = fields.IntField(pk=True, description='主键') + strategy_name = fields.CharField(max_length=255, description='策略名称') + strategy_hash = fields.CharField(max_length=255, description='策略版本号') + strategy_type = fields.CharField(max_length=255, null=True, description='策略类型') + user_id = fields.IntField(null=True, description='所属用户') + backtest_count = fields.IntField(null=True, description='回测次数') + backtest_keys = fields.JSONField(null=True, description='回测key列表') + is_deleted = fields.BooleanField(max_length=255, default=False, description='是否删除') + created_at = fields.DatetimeField(auto_now_add=True, description="创建时间") + updated_at = fields.DatetimeField(auto_now=True, description="修改时间") + deleted_at = fields.DatetimeField(null=True, description="删除时间") + + class Meta: + table = with_table_name("Strategy") diff --git a/src/models/test_table.py b/src/models/test_table.py new file mode 100644 index 0000000..50caed8 --- /dev/null +++ b/src/models/test_table.py @@ -0,0 +1,35 @@ +from tortoise import Model, fields +from tortoise.contrib.pydantic import pydantic_model_creator + +from src.models import with_table_name + + +class TestTable(Model): + """ + 用户 + """ + id = fields.IntField(pk=True, description="主键") + nickname = fields.CharField(max_length=30, null=True, description="用户昵称") + avatar_url = fields.CharField(max_length=255, null=True, description="头像") + member_type = fields.IntField(null=True, description="会员类型") + beta_account_type = fields.CharField(null=True, max_length=30, description="内测账号类型") + pre_cost_time = fields.IntField(null=True, description="预支付时间（单位年）") + + qr_code = fields.CharField(max_length=255, null=True, description="专属客服二维码") + dedicated_id = fields.IntField(null=True, description="专属客服id") + + invited_user_id = fields.IntField(null=True, description="邀请人id") + created_user_id = fields.IntField(null=True, description="创建人id") + is_deleted = fields.BooleanField(default=False, index=True, description="是否删除") + login_at = fields.DatetimeField(null=True, description="最后一次登录时间") + created_at = fields.DatetimeField(auto_now_add=True, description="创建时间") + updated_at = fields.DatetimeField(auto_now=True, description="修改时间") + deleted_at = fields.DatetimeField(null=True, description="删除时间") + + class Meta: + table = with_table_name("users") + + +UserResponse = pydantic_model_creator( + TestTable +) diff --git a/src/models/tran_observer_data.py b/src/models/tran_observer_data.py new file mode 100644 index 0000000..01d145e --- /dev/null +++ b/src/models/tran_observer_data.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class TranObserverData(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=255, ) + tran_observer_data = fields.BinaryField(null=True, description='存储大量数据', ) + + class Meta: + table = with_table_name("tran_observer_data") + diff --git a/src/models/tran_orders.py b/src/models/tran_orders.py new file mode 100644 index 0000000..868ea50 --- /dev/null +++ b/src/models/tran_orders.py @@ -0,0 +1,14 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + + + +class TranOrders(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=255, null=True, ) + order_return = fields.CharField(max_length=255, null=True, ) + +class meta: + table = with_table_name('tradorders') \ No newline at end of file diff --git a/src/models/tran_position.py b/src/models/tran_position.py new file mode 100644 index 0000000..8e1f6ee --- /dev/null +++ b/src/models/tran_position.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + + +class TranPosition(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=25, null=True, ) + tran_position_data = fields.BinaryField(null=True, ) + + class Meta: + table = with_table_name("tran_position") diff --git a/src/models/tran_return.py b/src/models/tran_return.py new file mode 100644 index 0000000..f89f4a3 --- /dev/null +++ b/src/models/tran_return.py @@ -0,0 +1,13 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class TranReturn(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=255, null=True, ) + tran_return_data = fields.BinaryField(null=True, ) + + class Meta: + table = with_table_name('tran_return') + diff --git a/src/models/tran_trade_info.py b/src/models/tran_trade_info.py new file mode 100644 index 0000000..dc3e742 --- /dev/null +++ b/src/models/tran_trade_info.py @@ -0,0 +1,12 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class TranTradeInfo(Model): + id = fields.IntField(pk=True, ) + key = fields.CharField(max_length=25, null=True, ) + tran_trade_info = fields.BinaryField(null=True, ) + + class Meta: + table = with_table_name("tran_trade_info") diff --git a/src/models/trand_info.py b/src/models/trand_info.py new file mode 100644 index 0000000..dd836c6 --- /dev/null +++ b/src/models/trand_info.py @@ -0,0 +1,30 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + +class TrandInfo(Model): + id = fields.IntField(pk=True, description='id', ) + key = fields.CharField(max_length=255, null=True, description='唯一索引', ) + tran_info_data = fields.BinaryField(null=True, ) + + + class Meta: + table = with_table_name("trand_info") + + + + + + + + + + + + + + + + + + diff --git a/src/models/transaction.py b/src/models/transaction.py new file mode 100644 index 0000000..4cf6516 --- /dev/null +++ b/src/models/transaction.py @@ -0,0 +1,30 @@ +from tortoise import Model, fields +from src.models import with_table_name +from src.models.strategy import Strategy + + +class Transaction(Model): + id = fields.IntField(pk=True, description='主键') + key = fields.CharField(max_length=20, description='key,数据标识') + cash = fields.FloatField(description='资金', null=True) + transaction_name = fields.CharField(max_length=255, description='交易名称') + transaction_type = fields.CharField(max_length=255, null=True, description='交易类型') + user_id = fields.IntField(null=True, description='用户id') + is_running = fields.BooleanField(max_length=255, default=True, description='运行状态') + is_deleted = fields.BooleanField(max_length=255, default=False, description='是否删除') + process_id = fields.IntField(null=True, description='进程号') + bar = fields.CharField(max_length=10, description='频率K线', null=True) + created_at = fields.DatetimeField(auto_now_add=True, description="创建时间") + updated_at = fields.DatetimeField(auto_now=True, description="修改时间") + stopped_at = fields.DatetimeField(null=True, description="停止时间") + deleted_at = fields.DatetimeField(null=True, description="删除时间") + strategy: fields.ForeignKeyRelation[Strategy] = fields.ForeignKeyField( + model_name="models.Strategy", + related_name="transaction_strategy", + # db_constraint=False, + on_delete=fields.CASCADE, + index=True, + ) + + class Meta: + table = with_table_name("Transaction") diff --git a/src/models/transaction_strategy.py b/src/models/transaction_strategy.py new file mode 100644 index 0000000..f472707 --- /dev/null +++ b/src/models/transaction_strategy.py @@ -0,0 +1,19 @@ +from tortoise import Model, fields +from src.models import with_table_name + + +class Strategy(Model): + id = fields.IntField(pk=True, description='主键') + strategy_name = fields.CharField(max_length=255, description='策略名称') + strategy_hash = fields.CharField(max_length=255, description='策略版本号') + strategy_type = fields.CharField(max_length=255, null=True, description='策略类型') + user_id = fields.IntField(null=True, description='所属用户') + backtest_count = fields.IntField(null=True, description='回测次数') + backtest_keys = fields.JSONField(null=True, description='回测key列表') + is_deleted = fields.BooleanField(max_length=255, default=False, description='是否删除') + created_at = fields.DatetimeField(auto_now_add=True, description="创建时间") + updated_at = fields.DatetimeField(auto_now=True, description="修改时间") + deleted_at = fields.DatetimeField(null=True, description="删除时间") + + class Meta: + table = with_table_name("strategy") diff --git a/src/models/user.py b/src/models/user.py new file mode 100644 index 0000000..97f2195 --- /dev/null +++ b/src/models/user.py @@ -0,0 +1,27 @@ +from tortoise import Model, fields +from src.models import with_table_name + + + + +class Users(Model): + avatar_url = fields.CharField(max_length=255, null=True, ) + beta_account_type = fields.CharField(max_length=30, null=True, ) + created_at = fields.DatetimeField(auto_now_add=True, ) + created_user_id = fields.IntField() + dedicated_id = fields.IntField() + deleted_at = fields.DatetimeField(null=True, ) + id = fields.IntField(pk=True, ) + invitation_code = fields.CharField(max_length=10, null=True, ) + invited_user_id = fields.IntField() + is_deleted = fields.IntField(index=True, ) + login_at = fields.DatetimeField(null=True, ) + member_type = fields.IntField() + nickname = fields.CharField(max_length=30, null=True, ) + pre_cost_time = fields.IntField() + qr_code = fields.CharField(max_length=255, null=True, ) + status = fields.BooleanField(null=True, default=False, ) + updated_at = fields.DatetimeField(auto_now_add=True, ) + + class Meta: + table = with_table_name("users") \ No newline at end of file diff --git a/src/models/wance_data_stock.py b/src/models/wance_data_stock.py new file mode 100644 index 0000000..17d6e63 --- /dev/null +++ b/src/models/wance_data_stock.py @@ -0,0 +1,43 @@ +from tortoise import Model, fields +from src.models import with_table_name + + +class WanceDataStock(Model): + id = fields.IntField(pk=True, ) + stock_name = fields.CharField(max_length=50, null=True, description='股票名称', ) + stock_code = fields.CharField(max_length=50, null=True, description='股票代码', ) + stock_sector = fields.JSONField(null=True, description='股票板块', ) + stock_type = fields.JSONField(null=True, description='股票类型', ) + time_start = fields.CharField(max_length=10, null=True, description='上市时间', ) + time_expire = fields.CharField(max_length=10, null=True, description='退市时间', ) + time_end = fields.CharField(max_length=10, null=True, description='上一次回测结束时间', ) + market_sector = fields.CharField(max_length=20, null=True, description='所属市场', ) + financial_dividend = fields.FloatField(null=True, default=0, description='分红率', ) + financial_ex_gratia = fields.FloatField(null=True, default=0, description='扣非后每股收益', ) + financial_cash_flow = fields.FloatField(null=True, default=0, description='每股现金流', ) + financial_asset_value = fields.FloatField(null=True, default=0, description='每股净资产', ) + financial_reserve_per = fields.FloatField(null=True, default=0, description='每股资本公积金', ) + financial_undistributed_profit = fields.FloatField(null=True, default=0, description='每股未分配利润', ) + profit_asset_value = fields.FloatField(null=True, default=0, description='净资产收益率', ) + profit_sale_ratio = fields.FloatField(null=True, default=0, description='盈利销售净利率', ) + profit_gross_rate = fields.FloatField(null=True, default=0, description='销售毛利率', ) + profit_business_increase = fields.FloatField(null=True, default=0, description='营业收入增长率', ) + profit_dividend_rate = fields.FloatField(null=True, default=0, description='股息率', ) + growth_Income_rate = fields.FloatField(null=True, default=0, description='营业总收入同比增长率', ) + growth_growth_rate = fields.FloatField(null=True, default=0, description='营业利润同比增长率', ) + growth_nonnet_profit = fields.FloatField(null=True, default=0, description='扣非净利润增长率', ) + growth_attributable_rate = fields.FloatField(null=True, default=0, description='归母净利润同比增长率', ) + valuation_PEGTTM_ratio = fields.FloatField(null=True, default=0, description='市盈率TTM', ) + valuation_PEG_percentile = fields.FloatField(null=True, default=0, description='市盈率百分位', ) + valuation_PB_TTM = fields.FloatField(null=True, default=0, description='市净率TTM', ) + valuation_PB_percentile = fields.FloatField(null=True, default=0, description='市净率百分比', ) + valuation_PTS_TTM = fields.FloatField(null=True, default=0, description='市销率TTM', ) + valuation_PTS_percentile = fields.FloatField(null=True, default=0, description='市销率百分位', ) + valuation_market_TTM = fields.FloatField(null=True, default=0, description='市现率TTM', ) + valuation_market_percentile = fields.FloatField(null=True, default=0, description='市现率百分比', ) + market_indicator = fields.FloatField(null=True, default=0, description='行情指标', ) + + + + class Meta: + table = with_table_name("wance_data_stock") \ No newline at end of file diff --git a/src/models/wance_data_storage_backtest.py b/src/models/wance_data_storage_backtest.py new file mode 100644 index 0000000..02914cc --- /dev/null +++ b/src/models/wance_data_storage_backtest.py @@ -0,0 +1,67 @@ +from tortoise import Model, fields +from src.models import with_table_name + +class WanceDataStorageBacktest(Model): + avg_down_month = fields.FloatField(null=True, default=0, description='回测期间每个月下跌时的平均损失', ) + avg_drawdown = fields.FloatField(null=True, default=0, description='平均回撤，表示每次回撤的平均幅度', ) + avg_drawdown_days = fields.FloatField(null=True, default=0, description='平均回撤持续的天数', ) + avg_up_month = fields.FloatField(null=True, default=0, description='回测期间每个月上涨时的平均收益', ) + backtest_end_time = fields.IntField(description='回测结束时间', ) + backtest_name = fields.CharField(max_length=100, null=True, ) + best_day = fields.FloatField(null=True, default=0, description='回测期间单日的最大回报', ) + best_month = fields.FloatField(null=True, default=0, description='回测期间的最佳月份回报', ) + best_year = fields.FloatField(null=True, default=0, description='回测期间的最佳年度回报（若为空，表示回测不足一年）', ) + cagr = fields.FloatField(null=True, default=0, description='年化复合增长率，表示投资在整个回测期间的年均增长率', ) + calmar = fields.FloatField(null=True, default=0, description='Calmar 比率，表示年化收益率与最大回撤的比率，用于衡量风险调整后的收益。Calmar 比率越高，意味着风险调整后的表现越好', ) + daily_kurt = fields.FloatField(null=True, default=0, description='每日回报的峰度，表示回报分布的尖锐程度', ) + daily_mean = fields.FloatField(null=True, default=0, description='每日的平均回报率', ) + daily_price = fields.JSONField(null=True, description='每日的价格', ) + daily_sharpe = fields.FloatField(null=True, default=0, description='日频率的夏普比率，表示每单位风险所获得的超额收益', ) + daily_skew = fields.FloatField(null=True, default=0, description='每日回报的偏度，表示回报分布的对称性', ) + daily_sortino = fields.FloatField(null=True, default=0, description='日频率的 Sortino 比率，衡量投资组合的下行风险，考虑的是负波动率', ) + daily_vol = fields.FloatField(null=True, default=0, description='每日回报率的标准差（波动率），表示收益的波动性', ) + data_end_time = fields.CharField(max_length=10, null=True, description='回测数据结束时间', ) + data_start_time = fields.CharField(max_length=10, null=True, description='回测数据开始时间', ) + five_year = fields.FloatField(null=True, default=0, description='过去五年的回报', ) + id = fields.IntField(pk=True, ) + incep = fields.FloatField(null=True, default=0, description='自策略开始运行以来的年化回报率（如果策略没有完整的一年，则等于 cagr）', ) + indicator_information = fields.JSONField(null=True, description='指标信息', ) + indicator_type = fields.CharField(max_length=20, null=True, description='指标类型', ) + max_drawdown = fields.FloatField(null=True, default=0, description='最大回撤，表示投资组合在回测期间从最高点到最低点的最大亏损百分比', ) + monthly_kurt = fields.FloatField(null=True, default=0, description='月度回报的峰度', ) + monthly_mean = fields.FloatField(null=True, default=0, description='月度平均回报率', ) + monthly_sharpe = fields.FloatField(null=True, default=0, description='月频率的夏普比率', ) + monthly_skew = fields.FloatField(null=True, default=0, description='月度回报的偏度', ) + monthly_sortino = fields.FloatField(null=True, default=0, description='月频率的 Sortino 比率', ) + monthly_vol = fields.FloatField(null=True, default=0, description='月度回报率的标准差（波动率）', ) + mtd = fields.FloatField(null=True, default=0, description='月内截至目前的回报', ) + one_year = fields.FloatField(null=True, default=0, description='的回报', ) + position = fields.JSONField(null=True, description='持仓记录', ) + price = fields.JSONField(null=True, description='每日的余额', ) + returns = fields.JSONField(null=True, description='每日的回报率', ) + rf = fields.FloatField(null=True, default=0, description='无风险收益率，通常用来计算夏普比率等指标，通常为0表示忽略无风险收益', ) + six_month = fields.FloatField(null=True, default=0, description='过去六个月的回报（如果为空，表示无相关数据', ) + stock_close_price = fields.JSONField(null=True, description='股票收盘价', ) + stock_code = fields.CharField(max_length=20, null=True, ) + strategy_name = fields.CharField(max_length=50, null=True, description='回测的策略名称', ) + ten_year = fields.IntField(description='过去十年的回报', ) + three_month = fields.FloatField(null=True, default=0, description='过去三个月的回报', ) + three_year = fields.FloatField(null=True, default=0, description='过去三年的回报', ) + total_return = fields.FloatField(null=True, default=0, description='总回报率', ) + twelve_month_win_perc = fields.FloatField(null=True, default=0, description='过去12个月的胜率，表示在过去12个月中有多少月的回报为正', ) + win_year_perc = fields.FloatField(null=True, default=0, description='策略年度胜率，表示回测期间策略表现优于无风险收益率的年份百分比', ) + worst_day = fields.FloatField(null=True, default=0, description='回测期间单日的最差回报', ) + worst_month = fields.FloatField(null=True, default=0, description='回测期间的最差月份回报', ) + worst_year = fields.FloatField(null=True, default=0, description='回测期间的最差年度回报（若为空，表示回测不足一年）', ) + yearly_kurt = fields.FloatField(null=True, default=0, description='年度的峰度', ) + yearly_mean = fields.FloatField(null=True, default=0, description='年度的平均回报', ) + yearly_sharpe = fields.FloatField(null=True, default=0, description='年度的夏普比率', ) + yearly_skew = fields.FloatField(null=True, default=0, description='年度的偏度', ) + yearly_sortino = fields.FloatField(null=True, default=0, description='年度的Sortino 比率', ) + yearly_vol = fields.FloatField(null=True, default=0, description='年度的波动率', ) + ytd = fields.FloatField(null=True, default=0, description='年初至今的回报', ) + + + + class Meta: + table = with_table_name("wance_data_storage_backtest") \ No newline at end of file diff --git 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description="分红类型，默认值为 'none'") + fill_data: bool = Field(default=True, description="是否填充数据，默认为 True") + incrementally: bool = Field(default=True, description="是否增量下载") + callback: bool = Field(default=True, description="是否开启回调函数") + data_dir: str = Field(default="D:\\e海方舟-量化交易版\\userdata_mini\\datadir", description="数据存储路径") + sector_name:str = Field(default="沪深指数", description="板块名称") + iscomplete: bool = Field(default=False, description="是否获取全部字段") + ma_type: str = Field(default='SMA', description="移动平均线类型，如 'SMA' 表示简单移动平均线") + short_window: int = Field(default=50, description="短周期线长度") + long_window: int = Field(default=200, description="长周期线长度") + bollingerMA: int = Field(default=50, description="布林带中的移动平均线周期，决定计算均值的时间窗口长度") + std_dev: int = Field(default=200, description="布林带中用于计算上下轨的标准差倍数，影响带宽大小") + overbought: int = Field(default=70, description="超买区间的RSI阈值，表示价格处于相对高点，可能面临回调") + oversold: int = Field(default=30, description="超卖区间的RSI阈值，表示价格处于相对低点，可能面临反弹") + signal_window: int = Field(default=9, description="超卖区间的RSI阈值，表示价格处于相对低点，可能面临反弹") + diff --git a/src/pydantic/codelistrequest.py b/src/pydantic/codelistrequest.py new file mode 100644 index 0000000..2b0de4e --- /dev/null +++ b/src/pydantic/codelistrequest.py @@ -0,0 +1,5 @@ +from pydantic import BaseModel + + +class CodeListRequest(BaseModel): + code_list : list[str] \ No newline at end of file diff --git a/src/pydantic/factor_request.py b/src/pydantic/factor_request.py new file mode 100644 index 0000000..09af0ab --- /dev/null +++ b/src/pydantic/factor_request.py @@ -0,0 +1,140 @@ +from typing import List, Optional, Any, Dict + +from pydantic import BaseModel, Field + + +# 定义查询条件模型，覆盖所有字段 +class StockQuery(BaseModel): + financial_asset_value: Optional[float] = None + financial_cash_flow: Optional[float] = None + financial_dividend: Optional[float] = None + financial_ex_gratia: Optional[float] = None + financial_reserve_per: Optional[float] = None + financial_undistributed_profit: Optional[float] = None + growth_attributable_rate: Optional[float] = None + growth_growth_rate: Optional[float] = None + growth_Income_rate: Optional[float] = None + growth_nonnet_profit: Optional[float] = None + id: Optional[int] = None + market_indicator: Optional[float] = None + market_sector: Optional[str] = None + profit_asset_value: Optional[float] = None + profit_business_increase: Optional[float] = None + profit_dividend_rate: Optional[float] = None + profit_gross_rate: Optional[float] = None + profit_sale_ratio: Optional[float] = None + stock_code: Optional[str] = None + stock_name: Optional[str] = None + stock_sector: Optional[List[str]] = None + stock_type: Optional[List[str]] = None + time_start: Optional[str] = None + time_end: Optional[str] = None + time_expire: Optional[str] = None + valuation_market_percentile: Optional[float] = None + valuation_market_TTM: Optional[float] = None + valuation_PB_percentile: Optional[float] = None + valuation_PB_TTM: Optional[float] = None + valuation_PEG_percentile: Optional[float] = None + valuation_PEGTTM_ratio: Optional[float] = None + valuation_PTS_percentile: Optional[float] = None + valuation_PTS_TTM: Optional[float] = None + gt: Optional[Dict[str, float]] = None # 大于 + lt: Optional[Dict[str, float]] = None # 小于 + gte: Optional[Dict[str, float]] = None # 大于等于 + lte: Optional[Dict[str, float]] = None # 小于等于 + between: Optional[Dict[str, Dict[str, float]]] = None # 在某两个值之间 + +""" +# 这里是因子pydantic的定义 + +class FactorRequest(BaseModel): + financial_asset_value: Optional[Dict[str, Any]] = Field(None, description='每股净资产') + financial_cash_flow: Optional[Dict[str, Any]] = Field(None, description='每股现金流') + financial_dividend: Optional[Dict[str, Any]] = Field(None, description='分红率') + financial_ex_gratia: Optional[Dict[str, Any]] = Field(None, description='扣非后每股收益') + financial_reserve_per: Optional[Dict[str, Any]] = Field(None, description='每股资本公积金') + financial_undistributed_profit: Optional[Dict[str, Any]] = Field(None, description='每股未分配利润') + growth_attributable_rate: Optional[Dict[str, Any]] = Field(None, description='归母净利润同比增长率') + growth_growth_rate: Optional[Dict[str, Any]] = Field(None, description='营业利润同比增长率') + growth_Income_rate: Optional[Dict[str, Any]] = Field(None, description='营业总收入同比增长率') + growth_nonnet_profit: Optional[Dict[str, Any]] = Field(None, description='扣非净利润增长率') + market_indicator: Optional[Dict[str, Any]] = Field(None, description='行情指标') + market_sector: Optional[str] = Field(None, description='所属市场') + profit_asset_value: Optional[Dict[str, Any]] = Field(None, description='净资产收益率') + profit_business_increase: Optional[Dict[str, Any]] = Field(None, description='营业收入增长率') + profit_dividend_rate: Optional[Dict[str, Any]] = Field(None, description='股息率') + profit_gross_rate: Optional[Dict[str, Any]] = Field(None, description='销售毛利率') + profit_sale_ratio: Optional[Dict[str, Any]] = Field(None, description='盈利销售净利率') + stock_code: Optional[str] = Field(None, description='股票代码') + stock_name: Optional[str] = Field(None, description='股票名称') + stock_sector: Optional[List[str]] = Field(None, description='股票板块') + stock_type: Optional[dict] = Field(None, description='股票类型') + time_end: Optional[str] = Field(None, description='上一次回测结束时间') + time_expire: Optional[str] = Field(None, description='退市时间') + time_start: Optional[Dict[str, Any]] = Field(None, description='上市时间') + valuation_market_percentile: Optional[Dict[str, Any]] = Field(None, description='市现率百分比') + valuation_market_TTM: Optional[Dict[str, Any]] = Field(None, description='市现率TTM') + valuation_PB_percentile: Optional[Dict[str, Any]] = Field(None, description='市净率百分位') + valuation_PB_TTM: Optional[Dict[str, Any]] = Field(None, description='市净率TTM') + valuation_PEG_percentile: Optional[Dict[str, Any]] = Field(None, description='市盈率百分位') + valuation_PEGTTM_ratio: Optional[Dict[str, Any]] = Field(None, description='市盈率TTM') + valuation_PTS_percentile: Optional[Dict[str, Any]] = Field(None, description='市销率百分位') + valuation_PTS_TTM: Optional[Dict[str, Any]] = Field(None, description='市销率TTM') +""" + +""" +使用示例： + factor_request = FactorRequest( + { + "financial_asset_value": 10.7169, + "financial_cash_flow": -0.42982, + "gt": { + "profit_gross_rate": 4.0, + "valuation_PB_TTM": 0.8 + }, + "lt": { + "market_indicator": -0.6 + }, + "gte": { + "valuation_market_percentile":5.38793 + }, + "lte": { + "valuation_PEGTTM_ratio": 15.2773 + }, + "between": { + "financial_dividend": { + "min": 1.0, + "max": 2.0 + }, + "growth_Income_rate": { + "min": -0.6, + "max": 1.0 + } + }, + "stock_code": "600051.SH", + "stock_name": "宁波联合", + "stock_sector": ["Shanghai A-shares", "Shenzhen A-shares"], + "stock_type": ["stock"], + "time_start": "0", + "time_end": "20240911", + "valuation_PEG_percentile": 15.2222, + "valuation_PTS_TTM": 2.3077 + } + ) + + result = await WanceDataStock.dynamic_query(**factor_request.dict(exclude_none=True)) + print(result) + + +比较大小字段说明： + 'gt' 对应 __gt（大于） + 'lt' 对应 __lt（小于） + 'gte' 对应 __gte（大于等于） + 'lte' 对应 __lte（小于等于） + 'between' 对应在某个值之间的条件，使用 __gt 和 __lt 组合实现 + +时间字段说明： + time_start={'recent_years': 2} 近2年 + time_start={'recent_months': 2} 近2月 + +""" diff --git a/src/pydantic/request_data.py b/src/pydantic/request_data.py new file mode 100644 index 0000000..381048c --- /dev/null +++ b/src/pydantic/request_data.py @@ -0,0 +1,21 @@ +from typing import List, Optional + +from pydantic import BaseModel, Field + + +class DataRequest(BaseModel): + field_list: List[str] = Field(default_factory=list, description="字段列表，用于指定获取哪些数据字段") + stock_list: List[str] = Field(default_factory=list, description="股票列表，用于指定获取哪些股票的数据") + stock_code: str = Field(default="000300.SH", description="股票代码，用于指定获取哪些股票的数据") + period: str = Field(default='1d', description="数据周期，如 '1d' 表示日线数据") + start_time: Optional[str] = Field(default='', description="开始时间，格式为 'YYYY-MM-DD'，默认为空字符串") + end_time: Optional[str] = Field(default='', description="结束时间，格式为 'YYYY-MM-DD'，默认为空字符串") + count: int = Field(default=-1, description="数据条数，默认为 -1 表示获取所有数据") + dividend_type: str = Field(default='none', description="分红类型，默认值为 'none'") + fill_data: bool = Field(default=True, description="是否填充数据，默认为 True") + incrementally: bool = Field(default=True, description="是否增量下载") + callback: bool = Field(default=True, description="是否开启回调函数") + data_dir: str = Field(default="D:\\e海方舟-量化交易版\\userdata_mini\\datadir", description="数据存储路径") + sector_name:str = Field(default="沪深指数", description="板块名称") + iscomplete: bool = Field(default=False, description="是否获取全部字段") + diff --git a/src/responses.py b/src/responses.py new file mode 100644 index 0000000..b607ca6 --- /dev/null +++ b/src/responses.py @@ -0,0 +1,68 @@ +import typing +from typing import Generic, Sequence, TypeVar + +from fastapi import status +from fastapi.encoders import jsonable_encoder +from fastapi.responses import JSONResponse +from pydantic import BaseModel + +T = TypeVar("T") + + +class EntityResponse(BaseModel, Generic[T]): + """实体数据""" + status_code: int = 200 + message: str = "Success" + data: T + + +class ListResponse(BaseModel, Generic[T]): + """列表数据""" + status_code: int = 200 + message: str = "Success" + data: Sequence[T] + + +# 包装响应的 Pydantic 模型 +# class EntityPageResponse(BaseModel, Generic[T]): +# status_code: int +# message: str +# data: PageData + + +def response_entity_response(data, status_code=200, message="Success") -> EntityResponse: + """普通实体类""" + return EntityResponse(data=data, status_code=status_code, message=message) + + +# def response_page_response(data, status_code=200, message="Success") -> EntityPageResponse: +# """普通分页类""" +# return EntityPageResponse(data=data, status_code=status_code, message=message) + + +def response_list_response(data, status_code=200, message="Success") -> ListResponse: + """普通列表数据""" + return ListResponse(data=data, status_code=status_code, message=message) + + +def response_success(message: str = 'Success', + headers: typing.Optional[typing.Dict[str, str]] = None) -> JSONResponse: + """成功返回""" + return JSONResponse( + status_code=status.HTTP_200_OK, + headers=headers, + content=jsonable_encoder({ + "status_code": status.HTTP_200_OK, + "message": message, + })) + + +def response_unauthorized() -> JSONResponse: + """未登录""" + return JSONResponse( + status_code=status.HTTP_401_UNAUTHORIZED, + headers={"WWW-Authenticate": "Bearer"}, + content=jsonable_encoder({ + "status_code": status.HTTP_401_UNAUTHORIZED, + "message": '用户认证失败', + })) diff --git a/src/settings/__init__.py b/src/settings/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/settings/__pycache__/__init__.cpython-311.pyc b/src/settings/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c285bd87ec4539dbd51149bdfbfaf00b9e566805 GIT binary patch literal 168 zcmZ3^%ge<81hWp!Oasx6K?DpiLK&agfQ;!3DGb33nv8xc8H$*I{LdiCU)Ii6F`>n& 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app_configs["root_path"] = f"/v{settings.APP_VERSION}" +# +# if not settings.ENVIRONMENT.is_debug: +# app_configs["openapi_url"] = None # hide docs diff --git a/src/tests/__init__.py b/src/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/stock_query.py b/src/tests/stock_query.py new file mode 100644 index 0000000..49a6b1c --- /dev/null +++ b/src/tests/stock_query.py @@ -0,0 +1,37 @@ +import asyncio +import json + +from src.models import wance_data_stock +from src.tortoises_orm_config import init_tortoise + + +async def init_test(): + await init_tortoise() + + await wance_data_stock.WanceDataStock.create( + stock_code='600051.SH', + stock_name='宁波联合', + stock_sector=['stockA'], # 确保这是一个列表格式 + stock_type=['stock'], + time_start='2010-01-01', + time_end='20240911', + time_expire='2024-09-11', + market_sector='SH' + ) + filters = { + 'stock_sector__contains': ["上证A股"] # 列表形式用于 JSON 数组匹配 + } + # filters = { + # 'stock_sector__contains': ['stockA'] # 列表形式用于 JSON 数组匹配 + # } + # stocks = await wance_data_stock.WanceDataStock.filter(stock_sector__contains=["上证A股"]).all() + # stocks = await wance_data_stock.WanceDataStock.filter(stock_sector__contains=["上证A股", "沪深A股", "TGN共同富裕示范区", "TGN旅游概念", "TGN煤炭概念", "TGN物业管理", "TGN跨境电商", "THY1综合", "THY2综合", "THY3综合", "DY1浙江省", "DY2浙江省宁波市", "上证指数", "上证收益", "上证流通", "中型综指", "中证全指", "中证民企", "中证流通", "国证Ａ指", "新综指", "民企成长", "浙企综指", "浙江民企", "综合指数", "Ａ股指数", "GN共同富裕示范区", "GN婚庆", "GN小盘", "GN微盘股", "GN房地产", "GN批发业", "GN旅游", "GN浙江", "GN煤炭", "GN物业管理", "GN环杭州湾大湾区", "GN电力改革", "GN破净股", "GN舟山自贸区", "GN跨境电商", "GN进口博览会", "SW1综合", "SW1综合加权", "SW2综合", "SW2综合加权", "SW3综合", "SW3综合加权", "A股非科创等权", "上海A股等权", "上海主板等权", "上海全市场等权", "上证指数等权", "上证收益等权", "上证流通等权", "中型综指等权", "中证全指等权", "国证Ａ指等权", "新综指等权", "沪深全市场等权", "综合指数等权", "Ａ股指数等权", "CSRC1批发和零售业", "CSRC2批发业"]).all() + # stocks = await wance_data_stock.WanceDataStock.filter(stock_sector__contains=["stock"]).all() + stocks = await wance_data_stock.WanceDataStock.filter(**filters).all() + + + print(stocks) + + +if __name__ == '__main__': + asyncio.run(init_test()) diff --git a/src/tests/xtquan_data_test.py b/src/tests/xtquan_data_test.py new file mode 100644 index 0000000..bd0c0fa --- /dev/null +++ b/src/tests/xtquan_data_test.py @@ -0,0 +1,78 @@ +# 用前须知 + +## xtdata提供和MiniQmt的交互接口，本质是和MiniQmt建立连接，由MiniQmt处理行情数据请求，再把结果回传返回到python层。使用的行情服务器以及能获取到的行情数据和MiniQmt是一致的，要检查数据或者切换连接时直接操作MiniQmt即可。 + +## 对于数据获取接口，使用时需要先确保MiniQmt已有所需要的数据，如果不足可以通过补充数据接口补充，再调用数据获取接口获取。 + +## 对于订阅接口，直接设置数据回调，数据到来时会由回调返回。订阅接收到的数据一般会保存下来，同种数据不需要再单独补充。 + +# 代码讲解 + +# 从本地python导入xtquant库，如果出现报错则说明安装失败 +from xtquant import xtdata +import time + +# 设定一个标的列表 +code_list = ["000001.SZ"] +# 设定获取数据的周期 +period = "1d" + +# 下载标的行情数据 +if 1: + ## 为了方便用户进行数据管理，xtquant的大部分历史数据都是以压缩形式存储在本地的 + ## 比如行情数据，需要通过download_history_data下载，财务数据需要通过 + ## 所以在取历史数据之前，我们需要调用数据下载接口，将数据下载到本地 + for i in code_list: + xtdata.download_history_data(i, period=period, incrementally=True) # 增量下载行情数据（开高低收,等等）到本地 + + xtdata.download_financial_data(code_list) # 下载财务数据到本地 + xtdata.download_sector_data() # 下载板块数据到本地 + # 更多数据的下载方式可以通过数据字典查询 + +# 读取本地历史行情数据 +history_data = xtdata.get_market_data_ex([], code_list, period=period, count=-1) +print(history_data) +print("=" * 20) + +# 如果需要盘中的实时行情，需要向服务器进行订阅后才能获取 +# 订阅后，get_market_data函数于get_market_data_ex函数将会自动拼接本地历史行情与服务器实时行情 + +# 向服务器订阅数据 +for i in code_list: + xtdata.subscribe_quote(i, period=period, count=-1) # 设置count = -1来取到当天所有实时行情 + +# 等待订阅完成 +time.sleep(1) + +# 获取订阅后的行情 +kline_data = xtdata.get_market_data_ex([], code_list, period=period) +print(kline_data) + +# 获取订阅后的行情，并以固定间隔进行刷新,预期会循环打印10次 +for i in range(10): + # 这边做演示，就用for来循环了，实际使用中可以用while True + kline_data = xtdata.get_market_data_ex([], code_list, period=period) + print(kline_data) + time.sleep(3) # 三秒后再次获取行情 + + +# 如果不想用固定间隔触发，可以以用订阅后的回调来执行 +# 这种模式下当订阅的callback回调函数将会异步的执行，每当订阅的标的tick发生变化更新，callback回调函数就会被调用一次 +# 本地已有的数据不会触发callback + +# 定义的回测函数 +## 回调函数中，data是本次触发回调的数据，只有一条 +def f(data): + # print(data) + + code_list = list(data.keys()) # 获取到本次触发的标的代码 + + kline_in_callabck = xtdata.get_market_data_ex([], code_list, period=period) # 在回调中获取klines数据 + print(kline_in_callabck) + + +for i in code_list: + xtdata.subscribe_quote(i, period=period, count=-1, callback=f) # 订阅时设定回调函数 + +# 使用回调时，必须要同时使用xtdata.run()来阻塞程序，否则程序运行到最后一行就直接结束退出了。 +xtdata.run() \ No newline at end of file diff --git a/src/tortoises.py b/src/tortoises.py new file mode 100644 index 0000000..a9db0fe --- /dev/null +++ b/src/tortoises.py @@ -0,0 +1,185 @@ +import logging +from fastapi import FastAPI +from tortoise.contrib.fastapi import register_tortoise +from src.settings.config import settings + +DATABASE_URL = str(settings.DATABASE_URL) +DATABASE_CREATE_URL = str(settings.DATABASE_CREATE_URL) + + +# 定义不同日志级别的颜色 +class ColoredFormatter(logging.Formatter): + COLORS = { + "DEBUG": "\033[94m", # 蓝色 + "INFO": "\033[92m", # 绿色 + "WARNING": "\033[93m", # 黄色 + "ERROR": "\033[91m", # 红色 + "CRITICAL": "\033[41m", # 红色背景 + } + RESET = "\033[0m" # 重置颜色 + + def format(self, record): + color = self.COLORS.get(record.levelname, self.RESET) + log_message = super().format(record) + return f"{color}{log_message}{self.RESET}" + + +# 配置日志 +logger_db_client = logging.getLogger("tortoise.db_client") +logger_db_client.setLevel(logging.DEBUG) + +# 创建一个控制台处理程序 +ch = logging.StreamHandler() +ch.setLevel(logging.DEBUG) + +# 创建并设置格式化器 +formatter = ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") +ch.setFormatter(formatter) + +# 将处理程序添加到日志记录器中 +logger_db_client.addHandler(ch) + +# 定义模型 +models = [ + "src.models.test_table", + "src.models.stock", + "src.models.security_account", + "src.models.order", + "src.models.snowball", + "src.models.backtest", + "src.models.strategy", + "src.models.stock_details", + "src.models.transaction", + "src.models.position", + "src.models.trand_info", + "src.models.tran_observer_data", + "src.models.tran_orders", + "src.models.tran_return", + "src.models.tran_trade_info", + "src.models.back_observed_data", + "src.models.back_observed_data_detail", + "src.models.back_position", + "src.models.back_result_indicator", + "src.models.back_trand_info", + "src.models.tran_position", + "src.models.stock_bt_history", + "src.models.stock_history", + "src.models.stock_data_processing", + "src.models.wance_data_stock", + "src.models.wance_data_storage_backtest" + +] + +aerich_models = models +aerich_models.append("aerich.models") + +# Tortoise ORM 配置 +TORTOISE_ORM = { + "connections": {"default": DATABASE_CREATE_URL, + }, + "apps": { + "models": { + "models": aerich_models, + "default_connection": "default", + }, + }, +} + +config = { + 'connections': { + 'default': DATABASE_URL + }, + 'apps': { + 'models': { + 'models': models, + 'default_connection': 'default', + } + }, + 'use_tz': False, + 'timezone': 'Asia/Shanghai' +} + + +def register_tortoise_orm(app: FastAPI): + # 注册 Tortoise ORM 配置 + register_tortoise( + app, + config=config, + generate_schemas=False, + add_exception_handlers=True, + ) + + +""" +********** +以下内容对原有无改色配置文件进行注释，上述内容为修改后日志为蓝色 +********** +""" + +# import logging +# +# from fastapi import FastAPI +# from tortoise.contrib.fastapi import register_tortoise +# +# from src.settings.config import settings +# +# DATABASE_URL = str(settings.DATABASE_URL) +# DATABASE_CREATE_URL = str(settings.DATABASE_CREATE_URL) +# +# # will print debug sql +# logging.basicConfig() +# logger_db_client = logging.getLogger("tortoise.db_client") +# logger_db_client.setLevel(logging.DEBUG) +# +# models = [ +# "src.models.test_table", +# "src.models.stock", +# "src.models.security_account", +# "src.models.order", +# "src.models.snowball", +# "src.models.backtest", +# "src.models.strategy", +# "src.models.stock_details", +# "src.models.transaction", +# ] +# +# aerich_models = models +# aerich_models.append("aerich.models") +# +# TORTOISE_ORM = { +# +# "connections": {"default": DATABASE_CREATE_URL}, +# "apps": { +# "models": { +# "models": aerich_models, +# "default_connection": "default", +# }, +# }, +# } +# +# config = { +# 'connections': { +# # Using a DB_URL string +# 'default': DATABASE_URL +# }, +# 'apps': { +# 'models': { +# 'models': models, +# 'default_connection': 'default', +# } +# }, +# 'use_tz': False, +# 'timezone': 'Asia/Shanghai' +# } +# +# +# def register_tortoise_orm(app: FastAPI): +# register_tortoise( +# app, +# config=config, +# # db_url=DATABASE_URL, +# # db_url="mysql://adams:adams@127.0.0.1:3306/adams", +# # modules={"models": models}, +# generate_schemas=False, +# add_exception_handlers=True, +# ) diff --git a/src/tortoises_orm_config.py b/src/tortoises_orm_config.py new file mode 100644 index 0000000..b09b70c --- /dev/null +++ b/src/tortoises_orm_config.py @@ -0,0 +1,30 @@ +from tortoise import Tortoise +from tortoise.exceptions import OperationalError +from src.tortoises import TORTOISE_ORM +import asyncio + + +async def init_tortoise(): + """ + 初始化 Tortoise-ORM 数据库连接 + """ + try: + await Tortoise.init(config=TORTOISE_ORM) + # print("Tortoise-ORM initialized successfully.") + except OperationalError as e: + print(f"OperationalError during initialization: {e}") + except Exception as e: + print(f"Error during initialization: {e}") + + +async def close_tortoise(): + """ + 关闭 Tortoise-ORM 数据库连接 + """ + try: + await Tortoise.close_connections() + # print("Tortoise-ORM connections closed successfully.") + except Exception as e: + print(f"Error during closing connections: {e}") + + diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 0000000..679558b --- /dev/null +++ b/src/utils/__init__.py @@ -0,0 +1,37 @@ +import sqlite3 +from collections import deque + +import redis.asyncio as aioredis +from fastapi import FastAPI + +from src.settings.config import settings +from src.utils import redis + + +import akshare + +from pypinyin import lazy_pinyin + +from src.models import StockType +from src.models.stock import Stock, StockResponse +from 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condition=lambda x: x % 2 == 0) + 2 + >>> first(range(3, 100)) + 3 + >>> first( () ) + Traceback (most recent call last): + ... + StopIteration + >>> first([], default=1) + 1 + >>> first([], default=1, condition=lambda x: x % 2 == 0) + Traceback (most recent call last): + ... + StopIteration + >>> first([1,3,5], default=1, condition=lambda x: x % 2 == 0) + Traceback (most recent call last): + ... + StopIteration + """ + + try: + return next(x for x in iterable if condition(x)) + except StopIteration: + if default is not None and condition(default): + return default + else: + raise diff --git a/src/utils/history_data_processing_utils.py b/src/utils/history_data_processing_utils.py new file mode 100644 index 0000000..c155b23 --- /dev/null +++ b/src/utils/history_data_processing_utils.py @@ -0,0 +1,249 @@ +import difflib +import re + +import pandas as pd + + +def arrays_to_dict(Chinese: list, English: list) -> dict: + # 使用 zip 将两个数组配对，然后转换为字典 + return dict(zip(Chinese, English)) + + +# 中文数组 +Chinese = [ + "上证A股", "深证A股", "沪深300", "中证500", "中证1000", "国证2000", "农林牧渔基础", "化工钢铁", "有色金属", + "电子", "汽车", "家用电器", "食品饮料", "纺织服饰", "轻工制造", "医药生物", "公用事业", "交通运输", + "房地产", "商贸零售", "社会服务", "银行", "非银金融", "综合", "建筑材料", "建筑装饰", "电力设备", + "机械设备", "国防军工", "计算机", "传媒", "通信", "煤炭", "石油石化", "环保", "美容护理", "中字头股票", + "中特估100", "人形机器人", "低空经济", "数字货币", "工业互联网", "Web3.0", "网络直播", "跨境电商", + "消费电子概念", "网络游戏", "国产软件", "抖音概念", "手机游戏", "元宇宙", "区块链", "虚拟现实", "量子科技", + "智能穿戴", "机器视觉", "ChatGPT概念", "脑机接口", "人工智能", "机器人概念", "数字孪生", "算力租赁", "碳中和", + "风电", "光热发电", "光伏概念", "中俄贸易概念", "露营经济", "柔性屏", "宠物经济", "免税店", "预制菜", + "在线旅游", "减肥药", "体育产业", "无人机", "华为海思概念股", "先进封装（Chiplet）", "第三代半导体", "中芯国际概念", + "光刻机", "氟化工概念", "光刻胶", "汽车芯片", "传感器", "英伟达概念", "边缘计算", "存储芯片", "6G概念", + "石墨烯", "芯片概念", "特斯拉", "充电桩", "抽水蓄能", "固态电池", "一体化压铸", "动力电池回收", "新能源汽车", + "钠离子电池", "储能", "钙钛矿电池", "汽车电子", "稀土永磁", "华为汽车", "毫米波雷达", "锂电池", "阿尔茨海默概念", + "创新药", "仿制药一致性评价", "重组蛋白", "毛发医疗", "生物医药", "医美概念辅助生殖", "生物疫苗", "细胞免疫治疗", + "基因测序", "集成电路概念", "黄金概念", "新疆振兴", "一带一路", "养老概念", "新材料概念", "职业教育", "工业4.0", + "碳纤维", "军工", "航运概念", "独角兽概念", "国家大基金持股", "海南自贸区", "专精特新", "智能制造", "超超临界发电" +] + +# 英文数组（仅为示例，可以根据具体需求替换成正确的英文） +English = [ + "Shanghai A-shares", "Shenzhen A-shares", "CSI 300", "CSI 500", "CSI 1000", "CSI 2000", "Agriculture and Forestry", + "Chemical and Steel", "Non-ferrous Metals", "Electronics", "Automobile", "Household Appliances", + "Food and Beverage", + "Textiles and Apparel", "Light Industry Manufacturing", "Pharmaceuticals", "Public Utilities", "Transportation", + "Real Estate", "Retail", "Social Services", "Banking", "Non-bank Finance", "Comprehensive", "Building Materials", + "Building Decoration", "Electric Power Equipment", "Machinery", "Defense", "Computer", "Media", "Telecom", "Coal", + "Petroleum", "Environmental Protection", "Beauty Care", "State-owned Enterprises", "Special Valuation 100", + "Humanoid Robot", + "Low-altitude Economy", "Digital Currency", "Industrial Internet", "Web3.0", "Live Streaming", + "Cross-border E-commerce", + "Consumer Electronics", "Online Games", "Domestic Software", "Douyin Concept", "Mobile Games", "Metaverse", + "Blockchain", + "Virtual Reality", "Quantum Technology", "Wearable Devices", "Machine Vision", "ChatGPT Concept", + "Brain-computer Interface", + "Artificial Intelligence", "Robotics", "Digital Twin", "Computing Power Leasing", "Carbon Neutrality", "Wind Power", + "Solar Thermal", "Photovoltaic", "China-Russia Trade", "Camping Economy", "Flexible Screen", "Pet Economy", + "Duty-free", + "Prepared Foods", "Online Travel", "Weight Loss Drugs", "Sports Industry", "Drone", "Huawei HiSilicon", "Chiplet", + "Third-generation Semiconductors", "SMIC Concept", "Lithography Machine", "Fluorine Chemicals", "Photoresist", + "Automotive Chips", "Sensors", "NVIDIA Concept", "Edge Computing", "Memory Chips", "6G Concept", "Graphene", + "Chip Concept", + "Tesla", "Charging Pile", "Pumped Storage", "Solid-state Battery", "Integrated Die-casting", "Battery Recycling", + "New Energy Vehicles", "Sodium-ion Battery", "Energy Storage", "Perovskite Battery", "Automotive Electronics", + "Rare Earth", + "Huawei Vehicles", "Millimeter Wave Radar", "Lithium Battery", "Alzheimer's Concept", "Innovative Drugs", + "Generic Drugs", + "Recombinant Protein", "Hair Medical", "Biomedical", "Aesthetic Medicine", "Reproductive Assistance", "Biovaccine", + "Cell Immunotherapy", "Gene Sequencing", "IC Concept", "Gold Concept", "Xinjiang Development", "Belt and Road", + "Elderly Care Concept", "New Materials", "Vocational Education", "Industry 4.0", "Carbon Fiber", "Military", + "Shipping", + "Unicorn Concept", "National Fund Holdings", "Hainan Free Trade Zone", "Specialized and New", "Smart Manufacturing", + "Ultra-supercritical Power" +] + +# 调用方法 +result_dict = arrays_to_dict(Chinese, English) + + +translation_dict = { + "上证A股": "Shanghai A-shares", + "深证A股": "Shenzhen A-shares", + "沪深300": "CSI 300", + "中证500": "CSI 500", + "中证1000": "CSI 1000", + "国证2000": "CSI 2000", + "农林牧渔基础": "Agriculture and Forestry", + "化工钢铁": "Chemical and Steel", + "有色金属": "Non-ferrous Metals", + "电子": "Electronics", + "汽车": "Automobile", + "家用电器": "Household Appliances", + "食品饮料": "Food and Beverage", + "纺织服饰": "Textiles and Apparel", + "轻工制造": "Light Industry Manufacturing", + "医药生物": "Pharmaceuticals", + "公用事业": "Public Utilities", + "交通运输": "Transportation", + "房地产": "Real Estate", + "商贸零售": "Retail", + "社会服务": "Social Services", + "银行": "Banking", + "非银金融": "Non-bank Finance", + "综合": "Comprehensive", + "建筑材料": "Building Materials", + "建筑装饰": "Building Decoration", + "电力设备": "Electric Power Equipment", + "机械设备": "Machinery", + "国防军工": "Defense", + "计算机": "Computer", + "传媒": "Media", + "通信": "Telecom", + "煤炭": "Coal", + "石油石化": "Petroleum", + "环保": "Environmental Protection", + "美容护理": "Beauty Care", + "中字头股票": "State-owned Enterprises", + "中特估100": "Special Valuation 100", + "人形机器人": "Humanoid Robot", + "低空经济": "Low-altitude Economy", + "数字货币": "Digital Currency", + "工业互联网": "Industrial Internet", + "Web3.0": "Web3.0", + "网络直播": "Live Streaming", + "跨境电商": "Cross-border E-commerce", + "消费电子概念": "Consumer Electronics", + "网络游戏": "Online Games", + "国产软件": "Domestic Software", + "抖音概念": "Douyin Concept", + "手机游戏": "Mobile Games", + "元宇宙": "Metaverse", + "区块链": "Blockchain", + "虚拟现实": "Virtual Reality", + "量子科技": "Quantum Technology", + "智能穿戴": "Wearable Devices", + "机器视觉": "Machine Vision", + "ChatGPT概念": "ChatGPT Concept", + "脑机接口": "Brain-computer Interface", + "人工智能": "Artificial Intelligence", + "机器人概念": "Robotics", + "数字孪生": "Digital Twin", + "算力租赁": "Computing Power Leasing", + "碳中和": "Carbon Neutrality", + "风电": "Wind Power", + "光热发电": "Solar Thermal", + "光伏概念": "Photovoltaic", + "中俄贸易概念": "China-Russia Trade", + "露营经济": "Camping Economy", + "柔性屏": "Flexible Screen", + "宠物经济": "Pet Economy", + "免税店": "Duty-free", + "预制菜": "Prepared Foods", + "在线旅游": "Online Travel", + "减肥药": "Weight Loss Drugs", + "体育产业": "Sports Industry", + "无人机": "Drone", + "华为海思概念股": "Huawei HiSilicon", + "先进封装（Chiplet）": "Chiplet", + "第三代半导体": "Third-generation Semiconductors", + "中芯国际概念": "SMIC Concept", + "光刻机": "Lithography Machine", + "氟化工概念": "Fluorine Chemicals", + "光刻胶": "Photoresist", + "汽车芯片": "Automotive Chips", + "传感器": "Sensors", + "英伟达概念": "NVIDIA Concept", + "边缘计算": "Edge Computing", + "存储芯片": "Memory Chips", + "6G概念": "6G Concept", + "石墨烯": "Graphene", + "芯片概念": "Chip Concept", + "特斯拉": "Tesla", + "充电桩": "Charging Pile", + "抽水蓄能": "Pumped Storage", + "固态电池": "Solid-state Battery", + "一体化压铸": "Integrated Die-casting", + "动力电池回收": "Battery Recycling", + "新能源汽车": "New Energy Vehicles", + "钠离子电池": "Sodium-ion Battery", + "储能": "Energy Storage", + "钙钛矿电池": "Perovskite Battery", + "汽车电子": "Automotive Electronics", + "稀土永磁": "Rare Earth", + "华为汽车": "Huawei Vehicles", + "毫米波雷达": "Millimeter Wave Radar", + "锂电池": "Lithium Battery", + "阿尔茨海默概念": "Alzheimer's Concept", + "创新药": "Innovative Drugs", + "仿制药一致性评价": "Generic Drugs", + "重组蛋白": "Recombinant Protein", + "毛发医疗": "Hair Medical", + "生物医药": "Biomedical", + "医美概念辅助生殖": "Aesthetic Medicine", + "生物疫苗": "Biovaccine", + "细胞免疫治疗": "Cell Immunotherapy", + "基因测序": "Gene Sequencing", + "集成电路概念": "IC Concept", + "黄金概念": "Gold Concept", + "新疆振兴": "Xinjiang Development", + "一带一路": "Belt and Road", + "养老概念": "Elderly Care Concept", + "新材料概念": "New Materials", + "职业教育": "Vocational Education", + "工业4.0": "Industry 4.0", + "碳纤维": "Carbon Fiber", + "军工": "Military", + "航运概念": "Shipping", + "独角兽概念": "Unicorn Concept", + "国家大基金持股": "National Fund Holdings", + "海南自贸区": "Hainan Free Trade Zone", + "专精特新": "Specialized and New", + "智能制造": "Smart Manufacturing", + "超超临界发电": "Ultra-supercritical Power" +} + + +def chinese_to_english(chinese_term): + return translation_dict.get(chinese_term, "Translation not found") + + +def english_to_chinese(english_term): + # Invert the dictionary for reverse lookup + inverted_dict = {v: k for k, v in translation_dict.items()} + return inverted_dict.get(english_term, "Translation not found") + + +def fuzzy_search(pattern, string_list): + """ + 使用正则表达式在字符串列表中进行模糊搜索。 + + :param pattern: 要搜索的模式（字符串） + :param string_list: 字符串列表 + :return: 匹配的字符串列表 + """ + regex = re.compile(pattern) + return [s for s in string_list if regex.search(s)] + + +def get_best_match(sector, translation_dict): + """ + 模糊匹配获取最佳匹配的中文板块。 + @param sector: 需要匹配的中文板块名 + @param translation_dict: 中文板块到英文的映射字典 + @return: 最佳匹配的中文板块名（如果找到），否则返回None + """ + matches = difflib.get_close_matches(sector, translation_dict.keys(), n=1, cutoff=0.6) + return matches[0] if matches else None + + +def on_progress(data): + print("这里是回执数据", data) + + +def safe_get_value(value): + """检查值是否为 None、NaN 或 False，如果是，则返回 0，否则返回值本身""" + if value is None or pd.isna(value) or value is False: + return 0 + return value diff --git a/src/utils/history_stock.py b/src/utils/history_stock.py new file mode 100644 index 0000000..fc9b596 --- /dev/null +++ b/src/utils/history_stock.py @@ -0,0 +1,40 @@ +from datetime import datetime + +import akshare +from pypinyin import lazy_pinyin + +from src.models import StockType +from src.models.snowball import Snowball +from src.models.stock_details import StockDetails +from src.tortoises_orm_config import init_tortoise, close_tortoise + + +async def job(): + await init_tortoise() + print(f"执行定时任务: 当前时间是 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + + await StockDetails.all().delete() + stock_name_df = akshare.stock_sh_a_spot_em() + stock_list = stock_name_df[['代码', '名称', '最新价', '涨跌幅']].values.tolist() + + for stock in stock_list: + # 将文字转化为拼音列表 + pinyin_list = lazy_pinyin(stock[1]) + + # 取每个拼音的首字母并连接成字符串 + initial_pinyin = ''.join([pinyin[0] for pinyin in pinyin_list]) + await StockDetails.create(stock_code=stock[0], stock_name=stock[1], type=StockType.SH, + stock_pinyin=initial_pinyin, latest_price=stock[2], rise_fall=stock[3]) + stock_name_df = akshare.stock_sz_a_spot_em() + stock_list = stock_name_df[['代码', '名称', '最新价', '涨跌幅']].values.tolist() + + for stock in stock_list: + # 将文字转化为拼音列表 + pinyin_list = lazy_pinyin(stock[1]) + + # 取每个拼音的首字母并连接成字符串 + initial_pinyin = ''.join([pinyin[0] for pinyin in pinyin_list]) + await StockDetails.create(stock_code=stock[0], stock_name=stock[1], type=StockType.SZ, + stock_pinyin=initial_pinyin, latest_price=stock[2], rise_fall=stock[3]) + + await close_tortoise() \ No newline at end of file diff --git a/src/utils/jsonformatter.py b/src/utils/jsonformatter.py new file mode 100644 index 0000000..32ae936 --- /dev/null +++ b/src/utils/jsonformatter.py @@ -0,0 +1,15 @@ +import json +import logging + + +class JsonFormatter(logging.Formatter): + """日志文件转为json格式""" + + def format(self, record): + obj = { + 'timestamp': self.formatTime(record, self.datefmt), + 'name': record.name, + 'level': record.levelname, + 'message': record.getMessage() + } + return json.dumps(obj) diff --git a/src/utils/models.py b/src/utils/models.py new file mode 100644 index 0000000..587efbc --- /dev/null +++ b/src/utils/models.py @@ -0,0 +1,37 @@ +from datetime import datetime +from typing import Any +from zoneinfo import ZoneInfo + +from fastapi.encoders import jsonable_encoder +from pydantic import BaseModel, ConfigDict, model_validator + + +def convert_datetime_to_gmt(dt: datetime) -> str: + if not dt.tzinfo: + dt = dt.replace(tzinfo=ZoneInfo("UTC")) + + return dt.strftime("%Y-%m-%dT%H:%M:%S%z") + + +class CustomModel(BaseModel): + model_config = ConfigDict( + json_encoders={datetime: convert_datetime_to_gmt}, + populate_by_name=True, + ) + + @model_validator(mode="before") + @classmethod + def set_null_microseconds(cls, data: dict[str, Any]) -> dict[str, Any]: + datetime_fields = { + k: v.replace(microsecond=0) + for k, v in data.items() + if isinstance(k, datetime) + } + + return {**data, **datetime_fields} + + def serializable_dict(self, **kwargs): + """Return a dict which contains only serializable fields.""" + default_dict = self.model_dump() + + return jsonable_encoder(default_dict) diff --git a/src/utils/paginations.py b/src/utils/paginations.py new file mode 100644 index 0000000..21cf264 --- /dev/null +++ b/src/utils/paginations.py @@ -0,0 +1,74 @@ +from typing import Generic, Optional, Sequence, TypeVar + +import math +from fastapi import Query +from pydantic import BaseModel +from tortoise.queryset import QuerySet + +T = TypeVar("T") + + +class PaginationPydantic(BaseModel, Generic[T]): + """分页模型""" + status_code: int = 200 + message: str = "Success" + total: int + page: int + size: int + total_pages: int + data: Sequence[T] + + +class Params(BaseModel): + """传参""" + # 设置默认值为1，不能够小于1 + page: int = Query(1, ge=1, description="Page number") + + # 设置默认值为10，最大为100 + size: int = Query(10, gt=0, le=200, description="Page size") + + # 默认值None表示选传 + order_by: Optional[str] = Query(None, max_length=32, description="Sort key") + + +async def pagination(pydantic_model, query_set: QuerySet, params: Params, callback=None): + """分页响应""" + """ + pydantic_model: Pydantic model + query_set: QuerySet + params: Params + callback: if you want to do something for query_set,it will be useful. + """ + page: int = params.page + size: int = params.size + order_by: str = params.order_by + total = await query_set.count() + + # 通过总数和每页数量计算出总页数 + total_pages = math.ceil(total / size) + + if page > total_pages and total: # 排除查询集为空时报错，即total=0时 + raise ValueError("页数输入有误") + + # 排序后分页 + if order_by: + order_by = order_by.split(',') + query_set = query_set.order_by(*order_by) + + # 分页 + query_set = query_set.offset((page - 1) * size) # 页数 * 页面大小=偏移量 + query_set = query_set.limit(size) + + if callback: + """对查询集操作""" + query_set = await callback(query_set) + + # query_set = await query_set + data = await pydantic_model.from_queryset(query_set) + return PaginationPydantic(**{ + "total": total, + "page": page, + "size": size, + "total_pages": total_pages, + "data": data, + }) diff --git a/src/utils/redis.py b/src/utils/redis.py new file mode 100644 index 0000000..0e7f139 --- /dev/null +++ b/src/utils/redis.py @@ -0,0 +1,31 @@ +from datetime import timedelta +from typing import Optional + +import redis.asyncio as aioredis + +from src.utils.models import CustomModel + +redis_client: aioredis = None # type: ignore + + +class RedisData(CustomModel): + key: bytes | str + value: bytes | str + ttl: Optional[int | timedelta] = None + + +async def set_redis_key(redis_data: RedisData, *, is_transaction: bool = False) -> None: + async with redis_client.pipeline(transaction=is_transaction) as pipe: + await pipe.set(redis_data.key, redis_data.value) + if redis_data.ttl: + await pipe.expire(redis_data.key, redis_data.ttl) + + await pipe.execute() + + +async def get_by_key(key: str) -> Optional[str]: + return await redis_client.get(key) + + +async def delete_by_key(key: str) -> None: + return await redis_client.delete(key) diff --git a/src/utils/remove_duplicates_databases.py b/src/utils/remove_duplicates_databases.py new file mode 100644 index 0000000..972fb04 --- /dev/null +++ b/src/utils/remove_duplicates_databases.py @@ -0,0 +1,40 @@ +from tortoise.models import Model +from tortoise import fields +from tortoise.functions import Count + +# 调用异步方法 +import asyncio + +from src.models.wance_data_stock import WanceDataStock +from src.tortoises_orm_config import init_tortoise + + +async def remove_duplicates(): + await init_tortoise() + + # 获取重复的股票代码和时间 + duplicates = await WanceDataStock.all().group_by('stock_code', 'time_end').annotate(count=Count('id')).filter( + count__gt=1) + + if not duplicates: + print("No duplicates found.") + return + + print(f"Found {len(duplicates)} duplicates.") + + for record in duplicates: + # 查询相同 stock_code 和 time_end 的所有记录，并按 id 逆序排序 + duplicate_records = await WanceDataStock.filter(stock_code=record.stock_code, + time_end=record.time_end).order_by('-id') + + # 输出当前处理的记录 + print( + f"Processing {len(duplicate_records)} records for stock_code={record.stock_code} and time_end={record.time_end}.") + + if duplicate_records: + # 删除最新的一条记录，保留其余的 + await WanceDataStock.filter(id=duplicate_records[0].id).delete() + print(f"Deleted record with id={duplicate_records[0].id}") + + +asyncio.run(remove_duplicates()) diff --git a/src/utils/split_stock_utils.py b/src/utils/split_stock_utils.py new file mode 100644 index 0000000..945aa1f --- /dev/null +++ b/src/utils/split_stock_utils.py @@ -0,0 +1,41 @@ +""" + +通用工具区域 + +""" + + +def split_stock_code(stock_code): + """ + 将股票代码按点（"."）切割成两部分。 + + Args: + stock_code (str): 原始股票代码（如 "000300.SH"）。 + + Returns: + tuple: 返回切割后的两部分（如 ('000300', 'SH')）。 + """ + return stock_code.split('.') + + +def join_stock_code(parts): + """ + 将切割后的股票代码部分按指定格式拼接。 + + Args: + parts (tuple): 切割后的股票代码部分（如 ('000300', 'SH')）。 + + Returns: + str: 返回拼接后的字符串（如 "SH000300"）。 + """ + part1, part2 = parts # 解构切割后的结果 + return f"{part2}{part1}" + + +def percent_to_float(percent_string): + if 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APIRouter，用于创建 API 路由器 + +import src.xtdata.service as service # 导入服务模块，该模块包含各种服务的实现 +from src.pydantic.codelistrequest import CodeListRequest # 导入 Pydantic 模型，用于验证请求参数 +from src.pydantic.factor_request import StockQuery +from src.pydantic.request_data import DataRequest # 导入 Pydantic 模型，用于验证请求参数 +from src.responses import response_entity_response, response_list_response # 导入自定义响应格式化函数 + +router = APIRouter() # 创建一个 FastAPI 路由器实例 + + +# 获取完整的逐笔成交键 +@router.get("/get_full_tick_keys") +async def get_full_tick_keys(request: CodeListRequest): + """ + 获取完整的逐笔成交键。 + """ + result = await service.get_full_tick_keys_service(request.code_list) # 调用服务获取逐笔成交键 + return result # 返回获取的结果 + + +# 获取完整的逐笔成交数据 +@router.get("/get_full_tick") +async def get_full_tick(request: CodeListRequest): + """ + 获取完整的逐笔成交数据。 + """ + result = await service.get_full_tick_service(request.code_list) # 调用服务获取逐笔成交数据 + return result # 返回获取的结果 + + +# 获取市场数据 +@router.get("/get_market_data") +async def get_market_data(request: DataRequest): + """ + 获取市场数据。 + """ + # 调用服务获取市场数据 + market_data = await service.get_market_data_service(field_list=request.field_list, + stock_list=request.stock_list, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + count=request.count, + dividend_type=request.dividend_type, + fill_data=request.fill_data) + return market_data # 返回市场数据 + + +# 获取完整的 K 线数据 +@router.get("/get_full_kline") +async def get_full_kline(request: DataRequest): + """ + 获取完整的 K 线数据。 + """ + # 调用服务获取完整的 K 线数据 + result = await service.get_full_kline_service(field_list=request.field_list, + stock_list=request.stock_list, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + count=request.count, + dividend_type=request.dividend_type, + fill_data=request.fill_data) + return result # 返回 K 线数据 + + +# 获取合约详情 +@router.get("/get_instrument_detail") +async def get_instrument_detail(request: DataRequest): + """ + 获取合约的详细信息。 + """ + # 调用服务获取合约详情数据 + result = await service.get_instrument_detail_service(stock_code=request.stock_code, + iscomplete=request.iscomplete) + return result # 返回合约详情 + + +@router.get("/get_stock_factor") +async def get_stock_factor(query_params: StockQuery): + stocks = await service.get_stock_factor_service(query_params) + return stocks + + +# 获取本地数据 +@router.get("/get_local_data") +async def get_local_data(request: DataRequest): + """ + 获取本地存储的股票数据。 + """ + # 调用服务获取本地数据 + result = await service.get_local_data_service(field_list=request.field_list, stock_list=request.stock_list, + period=request.period, start_time=request.start_time, + end_time=request.end_time, count=request.count, + dividend_type=request.dividend_type, fill_data=request.fill_data, + data_dir=request.data_dir) + return result + + +# 获取板块列表 +@router.post("/get_sector_list") +async def get_sector_list(): + """ + 获取所有板块的列表。 + """ + # 调用服务获取板块列表 + result = await service.get_sector_list_service() + return result # 返回板块列表 + + +# 获取某板块中的股票列表 +@router.get("/get_stock_list_in_sector") +async def get_stock_list_in_sector(request: DataRequest): + """ + 获取指定板块中的股票列表。 + """ + # 调用服务获取板块中的股票列表 + result = await service.get_stock_list_in_sector_service(sector_name=request.sector_name) + return result # 返回股票列表 + + +# 下载板块数据 +@router.post("/download_sector_data") +async def download_sector_data(): + """ + 下载板块的详细数据。 + """ + # 调用服务下载板块数据 + result = await service.download_sector_data_service() + return result # 返回下载结果 + + +# 订阅股票报价 +@router.post("/subscribe_quote") +async def subscribe_quote(request: DataRequest): + """ + 订阅单只股票的报价。 + """ + # 调用服务订阅股票报价 + result = await service.subscribe_quote_service(stock_code=request.stock_code, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + count=request.count, + callback=request.callback) + return response_list_response(data=result, status_code=200, message="Success") # 返回响应 + + +# 订阅所有股票报价 +@router.post("/subscribe_whole_quote") +async def subscribe_whole_quote(request: DataRequest): + """ + 订阅所有股票的报价。 + """ + # 调用服务订阅所有股票的报价 + result = await service.subscribe_whole_quote_service(code_list=request.code_list, + callback=request.callback) + return response_list_response(data=result, status_code=200, message="Success") # 返回响应 + + +# 下载历史数据 +@router.post("/download_history_data") +async def download_history_data(request: DataRequest): + """ + 下载指定股票的历史数据。 + """ + # 调用服务下载指定股票的历史数据 + await service.download_history_data_service(stock_code=request.stock_code, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + incrementally=request.incrementally) + return response_list_response(data=[], status_code=200, message="Success") # 返回响应 + + +# 批量下载历史数据 +@router.post("/batch_download_history_data") +async def download_history_data(request: DataRequest): + """ + 批量下载多个股票的历史数据。 + """ + # 调用服务批量下载历史数据 + await service.download_history_data2_service(stock_list=request.stock_list, + period=request.period, + start_time=request.start_time, + end_time=request.end_time, + callback=request.callback) + return response_list_response(data=[], status_code=200, message="Success") # 返回响应 + + +# 批量下载历史数据 +@router.post("/update_stock_data") +async def update_stock_data(): + """ + 批量下载多个股票的历史数据。 + """ + # 调用服务批量下载历史数据 + await service.update_stock_data_service() + return response_list_response(data=[], status_code=200, message="Success") # 返回响应 diff --git a/src/xtdata/service.py b/src/xtdata/service.py new file mode 100644 index 0000000..76708be --- /dev/null +++ b/src/xtdata/service.py @@ -0,0 +1,363 @@ +import asyncio +import json +from datetime import datetime, timedelta + +import numpy as np # 导入 numpy 库 +from tortoise.expressions import Q +from xtquant import xtdata # 导入 xtquant 库的 xtdata 模块 + +from src.backtest.until import convert_pandas_to_json_serializable +from src.models.wance_data_stock import WanceDataStock +from src.pydantic.factor_request import StockQuery +from src.utils.history_data_processing_utils import translation_dict + + +# 获取股票池中的所有股票的股票代码 +async def get_full_tick_keys_service(code_list: list): + """ + 获取所有股票的逐笔成交键。若未提供股票列表，则默认使用 ['SH', 'SZ']。 + """ + if len(code_list) == 0: # 如果股票代码列表为空 + code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池 + result = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 调用 xtdata 库的函数获取逐笔成交数据 + return list(result.keys()) # 返回所有股票代码的列表 + + +# 获取股票池中的所有股票的逐笔成交数据 +async def get_full_tick_service(code_list: list): + """ + 获取所有股票的逐笔成交数据。若未提供股票列表，则默认使用 ['SH', 'SZ']。 + """ + if len(code_list) == 0: # 如果股票代码列表为空 + code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池 + result = xtdata.get_full_tick(code_list=code_list) # 获取逐笔成交数据 + return result # 返回逐笔成交数据 + + +# 获取板块信息 +async def get_sector_list_service(): + """ + 获取所有板块的列表。 + """ + result = xtdata.get_sector_list() # 调用 xtdata 库的函数获取板块信息 + return result # 返回板块列表 + + +# 通过板块名称获取股票列表 +async def get_stock_list_in_sector_service(sector_name): + """ + 获取指定板块中的所有股票名称。 + """ + result = xtdata.get_stock_list_in_sector(sector_name=sector_name) # 根据板块名称获取股票列表 + return result # 返回股票列表 + + +# 下载板块信息 +async def download_sector_data_service(): + """ + 下载板块的详细数据。 + """ + result = xtdata.download_sector_data() # 调用 xtdata 库的函数下载板块数据 + return result # 返回下载结果 + + +# 获取股票的详细信息 +async def get_instrument_detail_service(stock_code: str, iscomplete: bool): + """ + 获取股票的详细信息。 + """ + result = xtdata.get_instrument_detail(stock_code=stock_code, iscomplete=iscomplete) # 获取股票的详细信息 + return result # 返回股票的详细信息 + + +# 获取市场行情数据 +async def get_market_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, dividend_type: str, fill_data: bool): + """ + 获取指定条件下的市场行情数据。 + """ + result = xtdata.get_market_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, + fill_data=fill_data) # 获取市场数据 + return result # 返回市场数据 + + +# 获取最新的 K 线数据 +async def get_full_kline_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, + dividend_type: str, fill_data: bool): + """ + 获取指定条件下的完整 K 线数据。 + """ + result = xtdata.get_full_kline(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, + fill_data=fill_data) # 获取完整的 K 线数据 + return result # 返回 K 线数据 + + +# 下载历史数据 +async def download_history_data_service(stock_code: str, period: str, start_time: str, end_time: str, + incrementally: bool): + """ + 下载指定股票的历史数据。 + """ + xtdata.download_history_data(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time, + incrementally=incrementally) # 下载股票历史数据 + + +# 获取本地数据 +async def get_local_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str, + count: int, dividend_type: str, fill_data: bool, data_dir: str): + """ + @param field_list: + @type field_list: + @param stock_list: + @type stock_list: + @param period: + @type period: + @param start_time: + @type start_time: + @param end_time: + @type end_time: + @param count: + @type count: + @param dividend_type: + @type dividend_type: + @param fill_data: + @type fill_data: + @param data_dir: + @type data_dir: + @return: + @rtype: + """ + """ + 获取本地存储的股票数据。 + """ + return_list = [] + result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time, + end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data, + data_dir=data_dir) # 获取本地数据 + for i in stock_list: + return_list.append({ + i : convert_pandas_to_json_serializable(result.get(i)) + }) + return return_list + + +# 订阅全市场行情推送 +async def subscribe_whole_quote_service(code_list, callback=None): + """ + 订阅全市场行情推送。 + """ + if callback: + result = xtdata.subscribe_whole_quote(code_list, callback=on_data) # 有回调函数时调用 + else: + result = xtdata.subscribe_whole_quote(code_list, callback=None) # 无回调函数时调用 + return result # 返回订阅结果 + + +# 订阅单只股票行情推送 +async def subscribe_quote_service(stock_code: str, period: str, start_time: str, end_time: str, count: int, + callback: bool): + """ + 订阅单只股票的行情推送。 + """ + if callback: + result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time, + count=count, callback=on_data) # 有回调函数时调用 + else: + result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time, + count=count, callback=None) # 无回调函数时调用 + return result # 返回订阅结果 + + +def on_data(datas): + """ + 行情数据推送的回调函数，处理并打印接收到的行情数据。 + """ + for stock_code in datas: + print(stock_code, datas[stock_code]) # 打印接收到的行情数据 + + +# 批量增量下载或批量下载 +async def download_history_data2_service(stock_list: list, period: str, start_time: str, end_time: str, callback: bool): + """ + 批量下载股票历史数据，支持进度回调。 + """ + if callback: + xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time, + callback=on_progress) # 有回调函数时调用 + else: + xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time, + callback=None) # 无回调函数时调用 + + +def on_progress(data): + """ + 数据下载进度的回调函数，实时显示进度。 + """ + print(data) # 打印下载进度 + + +# 单只股票下载示例 +def download_data(): + """ + 测试函数：下载单只股票的历史数据并获取相关信息。 + """ + xtdata.download_history_data("000300.SH", "1d", start_time='', end_time='', incrementally=None) # 下载历史数据 + a = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 获取逐笔成交数据 + print(list(a.keys())) # 打印获取的股票代码 + + +async def update_stock_data_service(): + """ + # 用于本地xtdata数据更新 + @return: + @rtype: + """ + stock_list = await get_full_tick_keys_service(['SH', 'SZ']) + for stock in stock_list: + await download_history_data_service(stock_code=stock, + period='1d', + start_time='', + end_time='', + incrementally=True) + + +async def get_stock_factor_service(query_params: StockQuery): + """ + 动态查询方法，根据传入的字段和值进行条件构造 + :param kwargs: 查询条件，键为字段名，值为查询值 + :return: 符合条件的记录列表 + """ + """ + 使用示例： + { + "financial_asset_value": 10.7169, + "financial_cash_flow": -0.42982, + "gt": { + "profit_gross_rate": 4.0, + "valuation_PB_TTM": 0.8 + }, + "lt": { + "market_indicator": -0.6 + }, + "gte": { + "valuation_market_percentile":5.38793 + }, + "lte": { + "valuation_PEGTTM_ratio": 15.2773 + }, + "between": { + "financial_dividend": { + "min": 1.0, + "max": 2.0 + }, + "growth_Income_rate": { + "min": -0.6, + "max": 1.0 + } + }, + "stock_code": "600051.SH", + "stock_name": "宁波联合", + "stock_sector": ["上证A股", "沪深A股"], + "stock_type": ["stock"], + "time_start": "0", + "time_end": "20240911", + "valuation_PEG_percentile": 15.2222, + "valuation_PTS_TTM": 2.3077 + } + + + financial_asset_value 和 financial_cash_flow 字段为普通等于条件。 + gt 字段用于指定字段大于某个值的条件，例如 profit_gross_rate 大于 15.0。 + lt 字段用于指定字段小于某个值的条件，例如 market_indicator 小于 30.0。 + gte 字段用于指定字段大于等于某个值的条件，例如 valuation_market_percentile 大于等于 0.5。 + lte 字段用于指定字段小于等于某个值的条件，例如 valuation_PEGTTM_ratio 小于等于 2.0。 + between 字段用于指定字段在两个值之间的范围，例如 financial_dividend 在 1.0 和 5.0 之间，growth_Income_rate 在 10.0 和 25.0 之间。 + stock_code 和 stock_name 字段为普通等于条件。 + stock_sector 和 stock_type 字段为列表类型的条件，可能会进行包含匹配。 + time_start 和 time_end 字段为普通等于条件。 + valuation_PEG_percentile 和 valuation_PTS_TTM 字段为普通等于条件。 + + 比较大小字段说明： + 'gt' 对应 __gt（大于） + 'lt' 对应 __lt（小于） + 'gte' 对应 __gte（大于等于） + 'lte' 对应 __lte（小于等于） + 'between' 对应在某个值之间的条件，使用 __gt 和 __lt 组合实现 + + 时间字段说明： + time_start={'recent_years': 2} 近2年 + time_start={'recent_months': 2} 近2月 + """ + + filters = {} + + # 处理大于、小于等操作 + if query_params.gt: + for field, value in query_params.gt.items(): + filters[f"{field}__gt"] = value + if query_params.lt: + for field, value in query_params.lt.items(): + filters[f"{field}__lt"] = value + if query_params.gte: + for field, value in query_params.gte.items(): + filters[f"{field}__gte"] = value + if query_params.lte: + for field, value in query_params.lte.items(): + filters[f"{field}__lte"] = value + + # 处理范围查询 + if query_params.between: + for field, range_values in query_params.between.items(): + if 'min' in range_values and 'max' in range_values: + filters[f"{field}__gte"] = range_values['min'] + filters[f"{field}__lte"] = range_values['max'] + + # 处理普通字段 + for field, value in query_params.model_dump(exclude_unset=True).items(): + if field not in ['gt', 'lt', 'gte', 'lte', 'between']: + if isinstance(value, float): + filters[f"{field}__gte"] = value + elif isinstance(value, str): + filters[f"{field}__icontains"] = value + elif isinstance(value, list): + if field == "stock_sector": + # 转换 `stock_sector` 中的中文板块名到英文 + translated_sectors = [translation_dict.get(sector, sector) for sector in value] + filters[f"{field}__contains"] = translated_sectors + else: + filters[f"{field}__contains"] = value + + try: + stocks = await WanceDataStock.filter(**filters).all() + return stocks + except Exception as e: + print(f"Error occurred when querying stocks: {e}") + return [] + + +# 将 numpy 类型转换为原生 Python 类型 +async def convert_numpy_to_native(obj): + """ + 递归地将 numpy 类型转换为原生 Python 类型。 + """ + if isinstance(obj, np.generic): # 如果是 numpy 类型 + return obj.item() # 转换为 Python 原生类型 + elif isinstance(obj, dict): # 如果是字典类型 + return {k: convert_numpy_to_native(v) for k, v in obj.items()} # 递归转换字典中的值 + elif isinstance(obj, list): # 如果是列表类型 + return [convert_numpy_to_native(i) for i in obj] # 递归转换列表中的值 + elif isinstance(obj, tuple): # 如果是元组类型 + return tuple(convert_numpy_to_native(i) for i in obj) # 递归转换元组中的值 + else: + return obj # 如果是其他类型，直接返回 + + +if __name__ == '__main__': + # result = asyncio.run(get_instrument_detail_service("000300.SH",False)) + # print(result) + # resulta = asyncio.run(get_sector_list_service()) + # print(resulta) + pass