crypto_quant/trade_ma_strategy_main.py

import core.logger as logging
from datetime import datetime
from time import sleep
import pandas as pd
import os
from core.biz.market_data import MarketData
from core.trade.ma_break_statistics import MaBreakStatistics
from core.db.db_market_data import DBMarketData
from core.biz.metrics_calculation import MetricsCalculation
from core.utils import (
    datetime_to_timestamp,
    timestamp_to_datetime,
    transform_date_time_to_timestamp,
)
from trade_data_main import TradeDataMain
from config import (
    API_KEY,
    SECRET_KEY,
    PASSPHRASE,
    SANDBOX,
    OKX_MONITOR_CONFIG,
    BAR_THRESHOLD,
)

logger = logging.logger


class TradeMaStrategyMain:
    def __init__(
        self,
        is_us_stock: bool = False,
        is_astock: bool = False,
        is_aindex: bool = True,
        is_binance: bool = False,
        commission_per_share: float = 0,
        buy_by_long_period: dict = {"by_week": False, "by_month": False},
        long_period_condition: dict = {
            "ma5>ma10": False,
            "ma10>ma20": False,
            "macd_diff>0": False,
            "macd>0": False,
        },
        cut_loss_by_valleys_median: bool = False,
    ):
        self.ma_break_statistics = MaBreakStatistics(
            is_us_stock=is_us_stock,
            is_astock=is_astock,
            is_aindex=is_aindex,
            is_binance=is_binance,
            commission_per_share=commission_per_share,
            buy_by_long_period=buy_by_long_period,
            long_period_condition=long_period_condition,
            cut_loss_by_valleys_median=cut_loss_by_valleys_median,
        )

    def batch_ma_break_statistics(self):
        """
        批量计算MA突破统计
        """
        logger.info("开始批量计算MA突破统计")
        strategy_dict = self.ma_break_statistics.main_strategy
        account_value_chg_df_list = []
        for strategy_name, strategy_info in strategy_dict.items():
            if "macd" in strategy_name:
                # 只计算macd策略
                account_value_chg_df = self.ma_break_statistics.batch_statistics(
                    strategy_name=strategy_name
                )
                account_value_chg_df_list.append(account_value_chg_df)

        total_account_value_chg_df = pd.concat(account_value_chg_df_list)
        return total_account_value_chg_df

    def statistics_account_value_chg(self, account_value_chg_df: pd.DataFrame):
        logger.info("开始统计account_value_chg")


def test_single_symbol():
    ma_break_statistics = MaBreakStatistics(
        is_us_stock=False,
        is_astock=True,
        is_aindex=False,
        is_binance=False,
        commission_per_share=0,
    )
    symbol = "600111.SH"
    bar = "1D"
    ma_break_statistics.trade_simulate(
        symbol=symbol, bar=bar, strategy_name="均线macd结合策略2"
    )


def batch_run_strategy():
    commission_per_share_list = [0, 0.0008]
    # cut_loss_by_valleys_median_list = [True, False]
    cut_loss_by_valleys_median_list = [False]
    buy_by_long_period_list = [
        # {"by_week": True, "by_month": True, "buy_by_10_percentile": True},
        {"by_week": False, "by_month": True, "buy_by_10_percentile": True},
        {"by_week": True, "by_month": False, "buy_by_10_percentile": True},
        {"by_week": False, "by_month": False, "buy_by_10_percentile": False},
    ]
    # buy_by_long_period_list = [{"by_week": False, "by_month": False}]
    long_period_condition_list = [
        {"ma5>ma10": True, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
        {"ma5>ma10": True, "ma10>ma20": False, "macd_diff>0": True, "macd>0": True},
        {"ma5>ma10": False, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
    ]

    for commission_per_share in commission_per_share_list:
        for cut_loss_by_valleys_median in cut_loss_by_valleys_median_list:
            for buy_by_long_period in buy_by_long_period_list:
                for long_period_condition in long_period_condition_list:
                    logger.info(
                        f"开始计算, 主要参数：commission_per_share: {commission_per_share}, buy_by_long_period: {buy_by_long_period}, long_period_condition: {long_period_condition}"
                    )
                    trade_ma_strategy_main = TradeMaStrategyMain(
                        is_us_stock=False,
                        is_astock=False,
                        is_aindex=True,
                        is_binance=False,
                        commission_per_share=commission_per_share,
                        buy_by_long_period=buy_by_long_period,
                        long_period_condition=long_period_condition,
                        cut_loss_by_valleys_median=cut_loss_by_valleys_median,
                    )
                    trade_ma_strategy_main.batch_ma_break_statistics()

                    trade_ma_strategy_main = TradeMaStrategyMain(
                        is_us_stock=False,
                        is_astock=True,
                        is_aindex=False,
                        is_binance=False,
                        commission_per_share=commission_per_share,
                        buy_by_long_period=buy_by_long_period,
                        long_period_condition=long_period_condition,
                        cut_loss_by_valleys_median=cut_loss_by_valleys_median,
                    )
                    trade_ma_strategy_main.batch_ma_break_statistics()


def pickup_data_from_excel():
    main_path = r"./output/trade_sandbox/ma_strategy"
    sub_main_paths = ["aindex", "astock"]
    fix_sub_path = "no_cut_loss_by_valleys_median"
    sub_folder = r"excel/均线macd结合策略2/"
    file_feature_name = "with_commission"
    original_df_columns = [
        "strategy_name",
        "symbol",
        "symbol_name",
        "bar",
        "total_buy_commission",
        "total_sell_commission",
        "total_commission",
        "initial_account_value",
        "final_account_value",
        "account_value_chg",
        "market_pct_chg",
    ]
    original_df_list = []
    for sub_main_path in sub_main_paths:
        logger.info(f"开始读取{sub_main_path}数据")
        full_sub_main_path = os.path.join(main_path, sub_main_path, fix_sub_path)
        # 读取sub_main_path下的所有文件夹
        folder_list = os.listdir(full_sub_main_path)
        for folder in folder_list:
            logger.info(f"开始读取{folder}数据")
            folder_path = os.path.join(full_sub_main_path, folder)
            properties = get_properties_by_folder_name(folder, sub_main_path)
            logger.info(f"开始读取{folder}数据")
            # 读取folder_path的sub_folder下的所有文件
            sub_folder_path = os.path.join(folder_path, sub_folder)
            file_list = os.listdir(sub_folder_path)
            for file in file_list:
                logger.info(f"开始读取{file}数据")
                if file_feature_name in file:
                    file_path = os.path.join(sub_folder_path, file)
                    df = pd.read_excel(file_path, sheet_name="资产价值变化")
                    logger.info(f"开始读取{file}数据")
                    # 向df添加properties
                    df = df.assign(**properties)
                    df = df[list(properties.keys()) + original_df_columns]
                    # 将df添加到original_df_list
                    original_df_list.append(df)
    final_df = pd.concat(original_df_list)
    excel_folder_path = os.path.join(main_path, "aindex_astock_均线macd结合策略2")
    os.makedirs(excel_folder_path, exist_ok=True)
    excel_file_path = os.path.join(
        excel_folder_path, "all_strategy_with_commission.xlsx"
    )
    with pd.ExcelWriter(excel_file_path) as writer:
        final_df.to_excel(
            writer, sheet_name="all_strategy_with_commission", index=False
        )


def get_properties_by_folder_name(folder_name: str, symbol_type: str):
    properties = {}
    sub_properties = folder_name.split("_")
    properties["symbol_type"] = symbol_type
    properties["buy_by_long_period"] = "no_long_period"
    properties["long_period_ma5gtma10"] = False
    properties["long_period_ma10gtma20"] = False
    properties["long_period_macd_diffgt0"] = False
    properties["long_period_macdgt0"] = False
    properties["buy_by_long_period_10_percentile"] = False
    if "1M" in sub_properties:
        properties["buy_by_long_period"] = "1M"
    if "1W" in sub_properties:
        properties["buy_by_long_period"] = "1W"
    if "1W1M" in sub_properties:
        properties["buy_by_long_period"] = "1W1M"
    if "ma5gtma10" in sub_properties:
        properties["long_period_ma5gtma10"] = True
    if "ma10gtma20" in sub_properties:
        properties["long_period_ma10gtma20"] = True
    if "macd_diffgt0" in sub_properties:
        properties["long_period_macd_diffgt0"] = True
    if "macdgt0" in sub_properties:
        properties["long_period_macdgt0"] = True
    if "10percentile" in sub_properties:
        properties["buy_by_long_period_10_percentile"] = True
    return properties


def profit_loss_ratio():
    """
    计算利润损失比
    公式：盈利/盈利交易次数 : 亏损/亏损交易次数
    """
    folder = r"./output/trade_sandbox/ma_strategy/binance/excel/均线macd结合策略2/"
    prefix = ["无交易费用", "有交易费用"]
    for prefix in prefix:
        excel_file_path = os.path.join(
            folder, f"{prefix}_趋势投资_from_201708181600_to_202509020600.xlsx"
        )
        df = pd.read_excel(excel_file_path, sheet_name="买卖记录明细")
        symbol_list = list(df["symbol"].unique())
        bar_list = list(df["bar"].unique())
        data_list = []
        for symbol in symbol_list:
            for bar in bar_list:
                df_symbol_bar = df[df["symbol"] == symbol][df["bar"] == bar]
                start_date = df_symbol_bar["begin_date_time"].min()
                end_date = df_symbol_bar["end_date_time"].max()
                profit_df = df_symbol_bar[df_symbol_bar["profit_loss"] > 0]
                loss_df = df_symbol_bar[df_symbol_bar["profit_loss"] < 0]

                profit_amount = sum(profit_df["profit_loss"])
                loss_amount = abs(sum(loss_df["profit_loss"]))

                profit_count = len(profit_df)
                loss_count = len(loss_df)

                if profit_count == 0 or loss_count == 0:
                    continue

                profit_loss_ratio = round(
                    (profit_amount / profit_count) / (loss_amount / loss_count) * 100, 4
                )

                data_list.append(
                    {
                        "币种": symbol,
                        "交易周期": bar,
                        "开始时间": start_date,
                        "结束时间": end_date,
                        "盈利金额": profit_amount,
                        "盈利次数": profit_count,
                        "亏损金额": loss_amount,
                        "亏损次数": loss_count,
                        "盈亏比": profit_loss_ratio,
                        "盈亏比公式": f"盈利金额/盈利次数 : 亏损金额/亏损次数",
                    }
                )
        final_df = pd.DataFrame(data_list)
        final_df.to_excel(os.path.join(folder, f"{prefix}时虚拟货币利润损失比.xlsx"), index=False)


if __name__ == "__main__":
    # batch_run_strategy()
    profit_loss_ratio()