284 lines
11 KiB
Python
284 lines
11 KiB
Python
import core.logger as logging
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from datetime import datetime
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from time import sleep
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import pandas as pd
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import os
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from core.biz.market_data import MarketData
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from core.trade.ma_break_statistics import MaBreakStatistics
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from core.db.db_market_data import DBMarketData
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from core.biz.metrics_calculation import MetricsCalculation
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from core.utils import (
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datetime_to_timestamp,
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timestamp_to_datetime,
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transform_date_time_to_timestamp,
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)
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from trade_data_main import TradeDataMain
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from config import (
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API_KEY,
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SECRET_KEY,
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PASSPHRASE,
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SANDBOX,
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OKX_MONITOR_CONFIG,
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BAR_THRESHOLD,
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)
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logger = logging.logger
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class TradeMaStrategyMain:
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def __init__(
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self,
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is_us_stock: bool = False,
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is_astock: bool = False,
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is_aindex: bool = True,
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is_binance: bool = False,
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commission_per_share: float = 0,
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buy_by_long_period: dict = {"by_week": False, "by_month": False},
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long_period_condition: dict = {
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"ma5>ma10": False,
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"ma10>ma20": False,
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"macd_diff>0": False,
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"macd>0": False,
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},
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cut_loss_by_valleys_median: bool = False,
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):
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self.ma_break_statistics = MaBreakStatistics(
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is_us_stock=is_us_stock,
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is_astock=is_astock,
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is_aindex=is_aindex,
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is_binance=is_binance,
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commission_per_share=commission_per_share,
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buy_by_long_period=buy_by_long_period,
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long_period_condition=long_period_condition,
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cut_loss_by_valleys_median=cut_loss_by_valleys_median,
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)
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def batch_ma_break_statistics(self):
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"""
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批量计算MA突破统计
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"""
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logger.info("开始批量计算MA突破统计")
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strategy_dict = self.ma_break_statistics.main_strategy
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account_value_chg_df_list = []
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for strategy_name, strategy_info in strategy_dict.items():
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if "macd" in strategy_name:
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# 只计算macd策略
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account_value_chg_df = self.ma_break_statistics.batch_statistics(
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strategy_name=strategy_name
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)
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account_value_chg_df_list.append(account_value_chg_df)
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total_account_value_chg_df = pd.concat(account_value_chg_df_list)
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return total_account_value_chg_df
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def statistics_account_value_chg(self, account_value_chg_df: pd.DataFrame):
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logger.info("开始统计account_value_chg")
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def test_single_symbol():
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ma_break_statistics = MaBreakStatistics(
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is_us_stock=False,
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is_astock=True,
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is_aindex=False,
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is_binance=False,
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commission_per_share=0,
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)
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symbol = "600111.SH"
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bar = "1D"
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ma_break_statistics.trade_simulate(
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symbol=symbol, bar=bar, strategy_name="均线macd结合策略2"
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)
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def batch_run_strategy():
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commission_per_share_list = [0, 0.0008]
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# cut_loss_by_valleys_median_list = [True, False]
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cut_loss_by_valleys_median_list = [False]
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buy_by_long_period_list = [
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# {"by_week": True, "by_month": True, "buy_by_10_percentile": True},
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{"by_week": False, "by_month": True, "buy_by_10_percentile": True},
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{"by_week": True, "by_month": False, "buy_by_10_percentile": True},
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{"by_week": False, "by_month": False, "buy_by_10_percentile": False},
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]
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# buy_by_long_period_list = [{"by_week": False, "by_month": False}]
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long_period_condition_list = [
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{"ma5>ma10": True, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
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{"ma5>ma10": True, "ma10>ma20": False, "macd_diff>0": True, "macd>0": True},
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{"ma5>ma10": False, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
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]
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for commission_per_share in commission_per_share_list:
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for cut_loss_by_valleys_median in cut_loss_by_valleys_median_list:
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for buy_by_long_period in buy_by_long_period_list:
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for long_period_condition in long_period_condition_list:
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logger.info(
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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}"
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)
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trade_ma_strategy_main = TradeMaStrategyMain(
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is_us_stock=False,
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is_astock=False,
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is_aindex=True,
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is_binance=False,
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commission_per_share=commission_per_share,
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buy_by_long_period=buy_by_long_period,
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long_period_condition=long_period_condition,
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cut_loss_by_valleys_median=cut_loss_by_valleys_median,
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)
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trade_ma_strategy_main.batch_ma_break_statistics()
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trade_ma_strategy_main = TradeMaStrategyMain(
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is_us_stock=False,
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is_astock=True,
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is_aindex=False,
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is_binance=False,
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commission_per_share=commission_per_share,
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buy_by_long_period=buy_by_long_period,
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long_period_condition=long_period_condition,
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cut_loss_by_valleys_median=cut_loss_by_valleys_median,
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)
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trade_ma_strategy_main.batch_ma_break_statistics()
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def pickup_data_from_excel():
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main_path = r"./output/trade_sandbox/ma_strategy"
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sub_main_paths = ["aindex", "astock"]
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fix_sub_path = "no_cut_loss_by_valleys_median"
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sub_folder = r"excel/均线macd结合策略2/"
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file_feature_name = "with_commission"
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original_df_columns = [
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"strategy_name",
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"symbol",
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"symbol_name",
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"bar",
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"total_buy_commission",
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"total_sell_commission",
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"total_commission",
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"initial_account_value",
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"final_account_value",
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"account_value_chg",
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"market_pct_chg",
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]
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original_df_list = []
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for sub_main_path in sub_main_paths:
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logger.info(f"开始读取{sub_main_path}数据")
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full_sub_main_path = os.path.join(main_path, sub_main_path, fix_sub_path)
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# 读取sub_main_path下的所有文件夹
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folder_list = os.listdir(full_sub_main_path)
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for folder in folder_list:
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logger.info(f"开始读取{folder}数据")
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folder_path = os.path.join(full_sub_main_path, folder)
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properties = get_properties_by_folder_name(folder, sub_main_path)
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logger.info(f"开始读取{folder}数据")
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# 读取folder_path的sub_folder下的所有文件
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sub_folder_path = os.path.join(folder_path, sub_folder)
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file_list = os.listdir(sub_folder_path)
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for file in file_list:
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logger.info(f"开始读取{file}数据")
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if file_feature_name in file:
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file_path = os.path.join(sub_folder_path, file)
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df = pd.read_excel(file_path, sheet_name="资产价值变化")
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logger.info(f"开始读取{file}数据")
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# 向df添加properties
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df = df.assign(**properties)
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df = df[list(properties.keys()) + original_df_columns]
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# 将df添加到original_df_list
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original_df_list.append(df)
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final_df = pd.concat(original_df_list)
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excel_folder_path = os.path.join(main_path, "aindex_astock_均线macd结合策略2")
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os.makedirs(excel_folder_path, exist_ok=True)
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excel_file_path = os.path.join(
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excel_folder_path, "all_strategy_with_commission.xlsx"
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)
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with pd.ExcelWriter(excel_file_path) as writer:
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final_df.to_excel(
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writer, sheet_name="all_strategy_with_commission", index=False
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)
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def get_properties_by_folder_name(folder_name: str, symbol_type: str):
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properties = {}
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sub_properties = folder_name.split("_")
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properties["symbol_type"] = symbol_type
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properties["buy_by_long_period"] = "no_long_period"
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properties["long_period_ma5gtma10"] = False
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properties["long_period_ma10gtma20"] = False
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properties["long_period_macd_diffgt0"] = False
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properties["long_period_macdgt0"] = False
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properties["buy_by_long_period_10_percentile"] = False
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if "1M" in sub_properties:
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properties["buy_by_long_period"] = "1M"
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if "1W" in sub_properties:
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properties["buy_by_long_period"] = "1W"
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if "1W1M" in sub_properties:
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properties["buy_by_long_period"] = "1W1M"
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if "ma5gtma10" in sub_properties:
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properties["long_period_ma5gtma10"] = True
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if "ma10gtma20" in sub_properties:
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properties["long_period_ma10gtma20"] = True
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if "macd_diffgt0" in sub_properties:
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properties["long_period_macd_diffgt0"] = True
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if "macdgt0" in sub_properties:
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properties["long_period_macdgt0"] = True
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if "10percentile" in sub_properties:
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properties["buy_by_long_period_10_percentile"] = True
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return properties
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def profit_loss_ratio():
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"""
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计算利润损失比
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公式：盈利/盈利交易次数 : 亏损/亏损交易次数
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"""
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folder = r"./output/trade_sandbox/ma_strategy/binance/excel/均线macd结合策略2/"
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prefix = ["无交易费用", "有交易费用"]
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for prefix in prefix:
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excel_file_path = os.path.join(
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folder, f"{prefix}_趋势投资_from_201708181600_to_202509020600.xlsx"
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)
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df = pd.read_excel(excel_file_path, sheet_name="买卖记录明细")
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symbol_list = list(df["symbol"].unique())
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bar_list = list(df["bar"].unique())
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data_list = []
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for symbol in symbol_list:
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for bar in bar_list:
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df_symbol_bar = df[df["symbol"] == symbol][df["bar"] == bar]
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start_date = df_symbol_bar["begin_date_time"].min()
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end_date = df_symbol_bar["end_date_time"].max()
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profit_df = df_symbol_bar[df_symbol_bar["profit_loss"] > 0]
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loss_df = df_symbol_bar[df_symbol_bar["profit_loss"] < 0]
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profit_amount = sum(profit_df["profit_loss"])
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loss_amount = abs(sum(loss_df["profit_loss"]))
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profit_count = len(profit_df)
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loss_count = len(loss_df)
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if profit_count == 0 or loss_count == 0:
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continue
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profit_loss_ratio = round(
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(profit_amount / profit_count) / (loss_amount / loss_count) * 100, 4
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)
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data_list.append(
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{
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"币种": symbol,
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"交易周期": bar,
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"开始时间": start_date,
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"结束时间": end_date,
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"盈利金额": profit_amount,
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"盈利次数": profit_count,
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"亏损金额": loss_amount,
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"亏损次数": loss_count,
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"盈亏比": profit_loss_ratio,
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"盈亏比公式": f"盈利金额/盈利次数 : 亏损金额/亏损次数",
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}
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)
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final_df = pd.DataFrame(data_list)
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final_df.to_excel(os.path.join(folder, f"{prefix}时虚拟货币利润损失比.xlsx"), index=False)
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if __name__ == "__main__":
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# batch_run_strategy()
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profit_loss_ratio()
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