From adba888bb2cf694496755cf80fe9dd40b4b72cad Mon Sep 17 00:00:00 2001 From: blade <8019068@qq.com> Date: Wed, 17 Sep 2025 19:33:37 +0800 Subject: [PATCH] 1. optimize binance ma quant strategy 2. support monitor 1m volume --- config.py | 22 + .../huge_volume_chart.cpython-312.pyc | Bin 23613 -> 23644 bytes .../db/__pycache__/db_manager.cpython-312.pyc | Bin 11888 -> 11919 bytes .../ma_break_statistics.cpython-312.pyc | Bin 35643 -> 40337 bytes core/trade/ma_break_statistics.py | 650 ++++++++++++------ market_monitor_main.py | 24 +- trade_ma_strategy_main.py | 58 +- 7 files changed, 515 insertions(+), 239 deletions(-) diff --git a/config.py b/config.py index cec1796..561d67b 100644 --- a/config.py +++ b/config.py @@ -71,6 +71,28 @@ OKX_MONITOR_CONFIG = { }, } +OKX_REALTIME_MONITOR_CONFIG = { + "volume_monitor": { + "symbols": [ + "XCH-USDT", + "BTC-USDT", + "SOL-USDT", + "ETH-USDT", + "DOGE-USDT", + ], + "bars": ["1m", "5m", "15m", "30m", "1H"], + "initial_date": "2025-05-15 00:00:00", + }, + "price_monitor": { + "symbols": ["XCH-USDT"], + "bats": [ + {"bar": "5m", "threshold": 0.025}, + {"bar": "15m", "threshold": 0.5}, + {"bar": "1H", "threshold": 0.1}, + ], + }, +} + BINANCE_MONITOR_CONFIG = { "volume_monitor": { "symbols": [ diff --git a/core/biz/__pycache__/huge_volume_chart.cpython-312.pyc b/core/biz/__pycache__/huge_volume_chart.cpython-312.pyc index e6231fd138d042421cb5313e3797b2327bff8fdd..29a3f92fb730736b266b2651af8a62a33cd3c4ee 100644 GIT binary patch delta 523 zcmdnHgYnJ|M&8rByj%=Gz@dIJqrBj4l-e_2UBn3**kDf~5@vl-?x*#H%ms7-Ekm6?3XUsO;a zMR1J}%w&+N$shd18HFcv1el6Lb--j98EQCdgi=H%hXgn>icg*vFqe^QvPqz~s%Q-x z&=+Dg!a(y`U^X%`L^7ms)e6@MPhjj>n8G!ANua&11Xv9drkb8Mh}v?-a)t`#NQQDo zP07syL5hsxJgSkZxvD9u9;&{o!J3S>*wXTgauZ878w7JQG9KM*6;jN|STT8Bs3zZu zxGM%R7xZH<#3o%3N#6V@)Qibo#NLCM>9DYXyBYgoWhX5l=eUfhhbhZ(Ggc1#7oE}E3CwQbibXia6vVz#AoSq8oC+s9VrP)tPvwA9QE{>>YV!Sx{bCfpY>B;iZ n){Ji_M@QRAeF6q%kr9Y60204AY;yBcN^?@}iWnzvitYvgE<}X+ delta 501 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b/core/trade/ma_break_statistics.py @@ -8,12 +8,19 @@ from datetime import datetime, timedelta, timezone from core.utils import get_current_date_time import re import json +import math from openpyxl import Workbook from openpyxl.drawing.image import Image import openpyxl from openpyxl.styles import Font from PIL import Image as PILImage -from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE, BINANCE_MONITOR_CONFIG +from config import ( + OKX_MONITOR_CONFIG, + US_STOCK_MONITOR_CONFIG, + MYSQL_CONFIG, + WINDOW_SIZE, + BINANCE_MONITOR_CONFIG, +) from core.db.db_market_data import DBMarketData from core.db.db_binance_data import DBBinanceData from core.db.db_huge_volume_data import DBHugeVolumeData @@ -34,7 +41,12 @@ class MaBreakStatistics: 之间的涨跌幅 """ - def __init__(self, is_us_stock: bool = False, is_binance: bool = False): + def __init__( + self, + is_us_stock: bool = False, + is_binance: bool = False, + commission_per_share: float = 0.0008, + ): mysql_user = MYSQL_CONFIG.get("user", "xch") mysql_password = MYSQL_CONFIG.get("password", "") if not mysql_password: @@ -44,7 +56,7 @@ class MaBreakStatistics: mysql_database = MYSQL_CONFIG.get("database", "okx") self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}" - + self.db_huge_volume_data = DBHugeVolumeData(self.db_url) self.is_us_stock = is_us_stock self.is_binance = is_binance @@ -65,16 +77,16 @@ class MaBreakStatistics: "symbols", ["BTC-USDT"] ) self.bars = ["30m", "1H"] - self.initial_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get( - "initial_date", "2017-08-16 00:00:00" - ) + self.initial_date = BINANCE_MONITOR_CONFIG.get( + "volume_monitor", {} + ).get("initial_date", "2017-08-16 00:00:00") self.db_market_data = DBBinanceData(self.db_url) else: self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get( - "symbols", ["XCH-USDT"] + "symbols", ["XCH-USDT"] ) self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get( - "bars", ["5m", "15m", "30m", "1H"] + "bars", ["5m", "15m", "30m", "1H"] ) self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get( "initial_date", "2025-05-15 00:00:00" @@ -82,7 +94,8 @@ class MaBreakStatistics: self.db_market_data = DBMarketData(self.db_url) if len(self.initial_date) > 10: self.initial_date = self.initial_date[:10] - self.end_date = get_current_date_time() + self.end_date = get_current_date_time(format="%Y-%m-%d") + self.commission_per_share = commission_per_share self.trade_strategy_config = self.get_trade_strategy_config() self.main_strategy = self.trade_strategy_config.get("均线系统策略", None) @@ -93,14 +106,26 @@ class MaBreakStatistics: def batch_statistics(self, strategy_name: str = "全均线策略"): if self.is_us_stock: - self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/" - self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/" + self.stats_output_dir = ( + f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/" + ) + self.stats_chart_dir = ( + f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/" + ) elif self.is_binance: - self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/" - self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/" + self.stats_output_dir = ( + f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/" + ) + self.stats_chart_dir = ( + f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/" + ) else: - self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/" - self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/" + self.stats_output_dir = ( + f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/" + ) + self.stats_chart_dir = ( + f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/" + ) os.makedirs(self.stats_output_dir, exist_ok=True) os.makedirs(self.stats_chart_dir, exist_ok=True) @@ -111,7 +136,7 @@ class MaBreakStatistics: for symbol in self.symbols: for bar in self.bars: logger.info( - f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}" + f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}，交易费率：{self.commission_per_share}" ) ma_break_market_data, market_data_pct_chg = self.trade_simulate( symbol, bar, strategy_name @@ -126,40 +151,19 @@ class MaBreakStatistics: f"{symbol} {bar} 的市场价格变化, {market_data_pct_chg.get('pct_chg', 0)}%" ) market_data_pct_chg_list.append(market_data_pct_chg) - + if len(ma_break_market_data_list) > 0: ma_break_market_data = pd.concat(ma_break_market_data_list) market_data_pct_chg_df = pd.DataFrame(market_data_pct_chg_list) # 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count - ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True) + ma_break_market_data.sort_values( + by="begin_timestamp", ascending=True, inplace=True + ) ma_break_market_data.reset_index(drop=True, inplace=True) - pct_chg_df = ( - ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"] - .agg( - pct_chg_sum="sum", - pct_chg_max="max", - pct_chg_min="min", - pct_chg_mean="mean", - pct_chg_std="std", - pct_chg_median="median", - pct_chg_count="count", - ) - .reset_index() - ) - pct_chg_df["strategy_name"] = strategy_name - pct_chg_df["pct_chg_total"] = 0 - pct_chg_df["market_pct_chg"] = 0 - # 将pct_chg_total与market_pct_chg的值类型转换为float - pct_chg_df["pct_chg_total"] = pct_chg_df["pct_chg_total"].astype(float) - pct_chg_df["market_pct_chg"] = pct_chg_df["market_pct_chg"].astype(float) - # 统计pct_chg_total - # 算法要求，ma_break_market_data，然后pct_chg/100 + 1 - ma_break_market_data["pct_chg_total"] = ( - ma_break_market_data["pct_chg"] / 100 + 1 - ) - # 遍历symbol和bar，按照end_timestamp排序，计算pct_chg_total的值，然后相乘 - for symbol in pct_chg_df["symbol"].unique(): - for bar in pct_chg_df["bar"].unique(): + + account_value_chg_list = [] + for symbol in market_data_pct_chg_df["symbol"].unique(): + for bar in market_data_pct_chg_df["bar"].unique(): symbol_bar_data = ma_break_market_data[ (ma_break_market_data["symbol"] == symbol) & (ma_break_market_data["bar"] == bar) @@ -169,51 +173,80 @@ class MaBreakStatistics: by="end_timestamp", ascending=True, inplace=True ) symbol_bar_data.reset_index(drop=True, inplace=True) - symbol_bar_data["pct_chg_total"] = symbol_bar_data[ - "pct_chg_total" - ].cumprod() - - # 将更新后的pct_chg_total数据同步更新到ma_break_market_data的对应数据行中 - for idx, row in symbol_bar_data.iterrows(): - mask = (ma_break_market_data["symbol"] == symbol) & \ - (ma_break_market_data["bar"] == bar) & \ - (ma_break_market_data["end_timestamp"] == row["end_timestamp"]) - ma_break_market_data.loc[mask, "pct_chg_total"] = row["pct_chg_total"] - - last_pct_chg_total = symbol_bar_data["pct_chg_total"].iloc[-1] - last_pct_chg_total = (last_pct_chg_total - 1) * 100 - pct_chg_df.loc[ - (pct_chg_df["symbol"] == symbol) - & (pct_chg_df["bar"] == bar), - "pct_chg_total", - ] = last_pct_chg_total + initial_capital = int(market_data_pct_chg_df.loc[ + (market_data_pct_chg_df["symbol"] == symbol) + & (market_data_pct_chg_df["bar"] == bar), + "initial_capital", + ].values[0]) + final_account_value = float(symbol_bar_data["end_account_value"].iloc[-1]) + account_value_chg = (final_account_value - initial_capital) / initial_capital * 100 + account_value_chg = round(account_value_chg, 4) market_pct_chg = market_data_pct_chg_df.loc[ (market_data_pct_chg_df["symbol"] == symbol) & (market_data_pct_chg_df["bar"] == bar), "pct_chg", ].values[0] - pct_chg_df.loc[ - (pct_chg_df["symbol"] == symbol) - & (pct_chg_df["bar"] == bar), - "market_pct_chg", - ] = market_pct_chg - - pct_chg_df = pct_chg_df[ + total_buy_commission = float(symbol_bar_data["buy_commission"].sum()) + total_sell_commission = float(symbol_bar_data["sell_commission"].sum()) + total_commission = total_buy_commission + total_sell_commission + total_commission = round(total_commission, 4) + total_buy_commission = round(total_buy_commission, 4) + total_sell_commission = round(total_sell_commission, 4) + account_value_chg_list.append({ + "strategy_name": strategy_name, + "symbol": symbol, + "bar": bar, + "total_buy_commission": total_buy_commission, + "total_sell_commission": total_sell_commission, + "total_commission": total_commission, + "initial_account_value": initial_capital, + "final_account_value": final_account_value, + "account_value_chg": account_value_chg, + "market_pct_chg": market_pct_chg, + }) + account_value_chg_df = pd.DataFrame(account_value_chg_list) + account_value_chg_df = account_value_chg_df[ [ "strategy_name", "symbol", "bar", + "total_buy_commission", + "total_sell_commission", + "total_commission", + "initial_account_value", + "final_account_value", + "account_value_chg", "market_pct_chg", - "pct_chg_total", - "pct_chg_sum", - "pct_chg_max", - "pct_chg_min", - "pct_chg_mean", - "pct_chg_std", - "pct_chg_median", - "pct_chg_count", ] ] + + account_value_statistics_df = ( + ma_break_market_data.groupby(["symbol", "bar"])["end_account_value"] + .agg( + account_value_max="max", + account_value_min="min", + account_value_mean="mean", + account_value_std="std", + account_value_median="median", + account_value_count="count", + ) + .reset_index() + ) + account_value_statistics_df["strategy_name"] = strategy_name + account_value_statistics_df = account_value_statistics_df[ + [ + "strategy_name", + "symbol", + "bar", + "account_value_max", + "account_value_min", + "account_value_mean", + "account_value_std", + "account_value_median", + "account_value_count", + ] + ] + # 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count interval_minutes_df = ( ma_break_market_data.groupby(["symbol", "bar"])["interval_minutes"] @@ -227,6 +260,20 @@ class MaBreakStatistics: ) .reset_index() ) + interval_minutes_df["strategy_name"] = strategy_name + interval_minutes_df = interval_minutes_df[ + [ + "strategy_name", + "symbol", + "bar", + "interval_minutes_max", + "interval_minutes_min", + "interval_minutes_mean", + "interval_minutes_std", + "interval_minutes_median", + "interval_minutes_count", + ] + ] earliest_market_date_time = ma_break_market_data["begin_date_time"].min() earliest_market_date_time = re.sub( @@ -238,7 +285,10 @@ class MaBreakStatistics: latest_market_date_time = re.sub( r"[\:\-\s]", "", str(latest_market_date_time) ) - output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}.xlsx" + if self.commission_per_share > 0: + output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}_with_commission.xlsx" + else: + output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}_without_commission.xlsx" output_file_path = os.path.join(self.stats_output_dir, output_file_name) logger.info(f"导出{output_file_path}") strategy_info_df = self.get_strategy_info(strategy_name) @@ -247,16 +297,21 @@ class MaBreakStatistics: ma_break_market_data.to_excel( writer, sheet_name="买卖记录明细", index=False ) - pct_chg_df.to_excel(writer, sheet_name="买卖涨跌幅统计", index=False) + account_value_chg_df.to_excel(writer, sheet_name="资产价值变化", index=False) + account_value_statistics_df.to_excel( + writer, sheet_name="买卖账户价值统计", index=False + ) interval_minutes_df.to_excel( writer, sheet_name="买卖时间间隔统计", index=False ) - chart_dict = self.draw_quant_pct_chg_bar_chart(pct_chg_df, strategy_name) + chart_dict = self.draw_quant_pct_chg_bar_chart(account_value_chg_df, strategy_name) self.output_chart_to_excel(output_file_path, chart_dict) - chart_dict = self.draw_quant_line_chart(ma_break_market_data, strategy_name) + chart_dict = self.draw_quant_line_chart( + ma_break_market_data, market_data_pct_chg_df, strategy_name + ) self.output_chart_to_excel(output_file_path, chart_dict) - return pct_chg_df + return account_value_chg_df else: return None @@ -333,6 +388,14 @@ class MaBreakStatistics: date_time_field = "date_time_us" else: date_time_field = "date_time" + close_mean = market_data["close"].mean() + self.update_initial_capital(close_mean) + logger.info( + f"成功获取{symbol}数据：{len(market_data)}根{bar}K线,开始日期={market_data[date_time_field].min()}，结束日期={market_data[date_time_field].max()}" + ) + + account_value = self.initial_capital + for index, row in market_data.iterrows(): ma_cross = row["ma_cross"] timestamp = row["timestamp"] @@ -345,7 +408,6 @@ class MaBreakStatistics: macd_diff = float(row["dif"]) macd_dea = float(row["dea"]) macd = float(row["macd"]) - if ma_break_market_data_pair.get("begin_timestamp", None) is None: buy_condition = self.fit_strategy( strategy_name=strategy_name, @@ -355,6 +417,18 @@ class MaBreakStatistics: ) if buy_condition: + entry_price = close + # 计算交易股数 + shares, account_value = self.calculate_shares( + account_value, entry_price + ) + if shares == 0: + # 股数为0→不交易 + continue + + # 计算佣金 + buy_commission = shares * close * self.commission_per_share + ma_break_market_data_pair = {} ma_break_market_data_pair["symbol"] = symbol ma_break_market_data_pair["bar"] = bar @@ -368,6 +442,9 @@ class MaBreakStatistics: ma_break_market_data_pair["begin_macd_diff"] = macd_diff ma_break_market_data_pair["begin_macd_dea"] = macd_dea ma_break_market_data_pair["begin_macd"] = macd + ma_break_market_data_pair["shares"] = shares + ma_break_market_data_pair["buy_commission"] = buy_commission + ma_break_market_data_pair["begin_account_value"] = account_value continue else: sell_condition = self.fit_strategy( @@ -378,6 +455,20 @@ class MaBreakStatistics: ) if sell_condition: + shares = ma_break_market_data_pair["shares"] + entry_price = ma_break_market_data_pair["begin_close"] + exit_price = close + sell_commission = ( + shares * exit_price * self.commission_per_share + ) + profit_loss = (exit_price - entry_price) * shares + begin_account_value = ma_break_market_data_pair[ + "begin_account_value" + ] + account_value = ( + begin_account_value + profit_loss - sell_commission + ) + ma_break_market_data_pair["end_timestamp"] = timestamp ma_break_market_data_pair["end_date_time"] = date_time ma_break_market_data_pair["end_close"] = close @@ -389,11 +480,14 @@ class MaBreakStatistics: ma_break_market_data_pair["end_macd_dea"] = macd_dea ma_break_market_data_pair["end_macd"] = macd ma_break_market_data_pair["pct_chg"] = ( - close - ma_break_market_data_pair["begin_close"] - ) / ma_break_market_data_pair["begin_close"] + exit_price - entry_price + ) / entry_price ma_break_market_data_pair["pct_chg"] = round( ma_break_market_data_pair["pct_chg"] * 100, 4 ) + ma_break_market_data_pair["profit_loss"] = profit_loss + ma_break_market_data_pair["sell_commission"] = sell_commission + ma_break_market_data_pair["end_account_value"] = account_value ma_break_market_data_pair["interval_seconds"] = ( timestamp - ma_break_market_data_pair["begin_timestamp"] ) / 1000 @@ -413,7 +507,9 @@ class MaBreakStatistics: if len(ma_break_market_data_pair_list) > 0: ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list) # sort by end_timestamp - ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True) + ma_break_market_data.sort_values( + by="begin_timestamp", ascending=True, inplace=True + ) ma_break_market_data.reset_index(drop=True, inplace=True) logger.info( f"获取{symbol} {bar} 的买卖记录明细成功, 买卖次数: {len(ma_break_market_data)}" @@ -421,16 +517,85 @@ class MaBreakStatistics: # 量化期间，市场的波动率： # ma_break_market_data(最后一条数据的end_close - 第一条数据的begin_close) / 第一条数据的begin_close * 100 pct_chg = ( - (ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0]) + ( + ma_break_market_data["end_close"].iloc[-1] + - ma_break_market_data["begin_close"].iloc[0] + ) / ma_break_market_data["begin_close"].iloc[0] * 100 ) pct_chg = round(pct_chg, 4) - market_data_pct_chg = {"symbol": symbol, "bar": bar, "pct_chg": pct_chg} + market_data_pct_chg = { + "symbol": symbol, + "bar": bar, + "pct_chg": pct_chg, + "initial_capital": self.initial_capital, + } return ma_break_market_data, market_data_pct_chg else: return None, None - + + def update_initial_capital(self, close_mean: float): + self.initial_capital = 25000 + if close_mean > 10000: + self.initial_capital = self.initial_capital * 10000 + elif close_mean > 5000: + self.initial_capital = self.initial_capital * 5000 + elif close_mean > 1000: + self.initial_capital = self.initial_capital * 1000 + elif close_mean > 500: + self.initial_capital = self.initial_capital * 500 + elif close_mean > 100: + self.initial_capital = self.initial_capital * 100 + else: + pass + logger.info(f"收盘价均值：{close_mean}") + logger.info(f"初始资金调整为：{self.initial_capital}") + + def calculate_shares(self, account_value, entry_price): + """ + 根据ORB公式计算交易股数 + :param account_value: 当前账户价值（美元） + :param entry_price: 交易买入价格 + :param commission_per_share: 交易佣金, 默认为0.0008 + :return: 整数股数（Shares） + """ + logger.info( + f"开始计算交易股数：账户价值={account_value}，买入价格={entry_price}" + ) + try: + # 验证输入参数 + if account_value <= 0 or entry_price <= 0 or self.commission_per_share < 0: + logger.error("账户价值、买入价格或佣金不能为负或零") + return 0, account_value # 返回0股，账户价值不变 + + # 计算考虑手续费后的每单位BTC总成本 + total_cost_per_share = entry_price * (1 + self.commission_per_share) + + # 计算可购买的BTC数量（向下取整） + shares = math.floor(account_value / total_cost_per_share) + + # 计算总成本（含手续费） + total_cost = shares * total_cost_per_share + + # 计算剩余现金 + remaining_cash = account_value - total_cost + + # 计算总资产价值 = (购买的BTC数量 × 买入价格) + 剩余现金 + remaining_value = (shares * entry_price) + remaining_cash + + # 记录计算结果 + logger.info( + f"计算结果：可购买股数={shares}，总成本={total_cost:.2f}美元，" + f"剩余现金={remaining_cash:.2f}美元，总资产价值={remaining_value:.2f}美元" + ) + + return shares, remaining_value + + except Exception as e: + logger.error(f"计算股数或账户价值时出错：{str(e)}") + return 0, account_value # 出错时返回0股，账户价值不变 + def get_full_data(self, symbol: str, bar: str = "5m"): """ 分段获取数据，并将数据合并为完整数据 @@ -464,9 +629,7 @@ class MaBreakStatistics: current_end_date = min(start_date + timedelta(days=180), end_date) start_date_str = start_date.strftime("%Y-%m-%d") current_end_date_str = current_end_date.strftime("%Y-%m-%d") - logger.info( - f"获取{symbol}数据：{start_date_str}至{current_end_date_str}" - ) + logger.info(f"获取{symbol}数据：{start_date_str}至{current_end_date_str}") current_data = self.db_market_data.query_market_data_by_symbol_bar( symbol, bar, fields, start=start_date_str, end=current_end_date_str ) @@ -483,7 +646,6 @@ class MaBreakStatistics: data.reset_index(drop=True, inplace=True) return data - def fit_strategy( self, strategy_name: str = "全均线策略", @@ -626,103 +788,122 @@ class MaBreakStatistics: plt.rcParams["font.size"] = 11 # 设置字体大小 plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题 chart_dict = {} - column_name_dict = {"pct_chg_total": "量化策略涨跌", "pct_chg_mean": "量化策略涨跌均值"} + column_name_dict = { + "account_value_chg": "量化策略涨跌", + } for column_name, column_name_text in column_name_dict.items(): for bar in data["bar"].unique(): bar_data = data[data["bar"] == bar].copy() # 一次筛选即可 if bar_data.empty: continue bar_data.rename(columns={column_name: column_name_text}, inplace=True) - if column_name == "pct_chg_total": - bar_data.rename(columns={"market_pct_chg": "市场自然涨跌"}, inplace=True) + bar_data.rename( + columns={"market_pct_chg": "市场自然涨跌"}, inplace=True + ) # 可选：按均值排序 bar_data.sort_values(by=column_name_text, ascending=False, inplace=True) bar_data.reset_index(drop=True, inplace=True) # 如果column_name_text是"量化策略涨跌"，则柱状图，同时绘制量化策略涨跌与市场自然涨跌的柱状图，并绘制在同一个图表中 - if column_name == "pct_chg_total": - plt.figure(figsize=(12, 7)) - - # 设置x轴位置，为并列柱状图做准备 - x = np.arange(len(bar_data)) - width = 0.35 # 柱状图宽度 - - # 确保symbol列是字符串类型，避免matplotlib警告 - bar_data["symbol"] = bar_data["symbol"].astype(str) - - # 绘制量化策略涨跌柱状图（蓝色渐变色） - bars1 = plt.bar(x - width/2, bar_data[column_name_text], width, - label=column_name_text, - color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data)))) - - # 绘制市场自然涨跌柱状图（绿色渐变色） - bars2 = plt.bar(x + width/2, bar_data["市场自然涨跌"], width, - label="市场自然涨跌", - color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data)))) - - # 设置图表标题和标签 - plt.title(f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", fontsize=14, fontweight='bold') - plt.xlabel("Symbol", fontsize=12) - plt.ylabel("涨跌幅(%)", fontsize=12) - plt.xticks(x, bar_data['symbol'], rotation=45, ha='right') - plt.legend() - plt.grid(True, alpha=0.3) - - # 在量化策略涨跌柱状图上方添加数值标签 - for i, (bar1, value1) in enumerate(zip(bars1, bar_data[column_name_text])): - plt.text(bar1.get_x() + bar1.get_width()/2, value1 + (0.01 if value1 >= 0 else -0.01), - f'{value1:.3f}%', ha='center', va='bottom' if value1 >= 0 else 'top', - fontsize=9, fontweight='bold', color='darkblue') - - # 在市场自然涨跌柱状图上方添加数值标签 - for i, (bar2, value2) in enumerate(zip(bars2, bar_data["市场自然涨跌"])): - plt.text(bar2.get_x() + bar2.get_width()/2, value2 + (0.01 if value2 >= 0 else -0.01), - f'{value2:.3f}%', ha='center', va='bottom' if value2 >= 0 else 'top', - fontsize=9, fontweight='bold', color='darkgreen') + plt.figure(figsize=(12, 7)) - else: - plt.figure(figsize=(10, 6)) - - # 确保symbol列是字符串类型，避免matplotlib警告 - bar_data["symbol"] = bar_data["symbol"].astype(str) - - ax = sns.barplot( - x="symbol", y=column_name_text, data=bar_data, palette="Blues_d" + # 设置x轴位置，为并列柱状图做准备 + x = np.arange(len(bar_data)) + width = 0.35 # 柱状图宽度 + + # 确保symbol列是字符串类型，避免matplotlib警告 + bar_data["symbol"] = bar_data["symbol"].astype(str) + + # 绘制量化策略涨跌柱状图（蓝色渐变色） + bars1 = plt.bar( + x - width / 2, + bar_data[column_name_text], + width, + label=column_name_text, + color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))), + ) + + # 绘制市场自然涨跌柱状图（绿色渐变色） + bars2 = plt.bar( + x + width / 2, + bar_data["市场自然涨跌"], + width, + label="市场自然涨跌", + color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))), + ) + + # 设置图表标题和标签 + plt.title( + f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", + fontsize=14, + fontweight="bold", + ) + plt.xlabel("Symbol", fontsize=12) + plt.ylabel("涨跌幅(%)", fontsize=12) + plt.xticks(x, bar_data["symbol"], rotation=45, ha="right") + plt.legend() + plt.grid(True, alpha=0.3) + + # 在量化策略涨跌柱状图上方添加数值标签 + for i, (bar1, value1) in enumerate( + zip(bars1, bar_data[column_name_text]) + ): + plt.text( + bar1.get_x() + bar1.get_width() / 2, + value1 + (0.01 if value1 >= 0 else -0.01), + f"{value1:.3f}%", + ha="center", + va="bottom" if value1 >= 0 else "top", + fontsize=9, + fontweight="bold", + color="darkblue", ) - plt.title(f"{bar}趋势{column_name_text}(%)") - plt.xlabel("symbol") - plt.ylabel(column_name_text) - plt.xticks(rotation=45, ha="right") - # 在柱状图上添加数值标签 - for i, v in enumerate(bar_data[column_name_text]): - ax.text( - i, - v, - f"{v:.3f}", - ha="center", - va="bottom", - fontsize=10, - fontweight="bold", - ) + # 在市场自然涨跌柱状图上方添加数值标签 + for i, (bar2, value2) in enumerate( + zip(bars2, bar_data["市场自然涨跌"]) + ): + plt.text( + bar2.get_x() + bar2.get_width() / 2, + value2 + (0.01 if value2 >= 0 else -0.01), + f"{value2:.3f}%", + ha="center", + va="bottom" if value2 >= 0 else "top", + fontsize=9, + fontweight="bold", + color="darkgreen", + ) plt.tight_layout() - save_path = os.path.join( - self.stats_chart_dir, - f"{bar}_bar_chart_{column_name}_{strategy_name}.png", - ) + if self.commission_per_share > 0: + save_path = os.path.join( + self.stats_chart_dir, + f"{bar}_bar_chart_{column_name}_{strategy_name}_with_commission.png", + ) + else: + save_path = os.path.join( + self.stats_chart_dir, + f"{bar}_bar_chart_{column_name}_{strategy_name}_without_commission.png", + ) + plt.savefig(save_path, dpi=150) plt.close() sheet_name = f"{bar}_趋势{column_name_text}柱状图_{strategy_name}" chart_dict[sheet_name] = save_path return chart_dict - - def draw_quant_line_chart(self, data: pd.DataFrame, strategy_name: str = "全均线策略"): + + def draw_quant_line_chart( + self, + data: pd.DataFrame, + market_data_pct_chg_df: pd.DataFrame, + strategy_name: str = "全均线策略", + ): """ 根据量化策略买卖明细记录，绘制量化策略涨跌与市场自然涨跌的折线图 :param data: 量化策略买卖明细记录 + :param market_data_pct_chg_df: 市场自然涨跌记录 :param strategy_name: 策略名称 :return: None """ @@ -731,16 +912,27 @@ class MaBreakStatistics: chart_dict = {} for symbol in symbols: for bar in bars: - symbol_bar_data = data[(data["symbol"] == symbol) & (data["bar"] == bar)] + symbol_bar_data = data[ + (data["symbol"] == symbol) & (data["bar"] == bar) + ] if symbol_bar_data.empty: continue - + # 获取第一行数据作为基准 first_row = symbol_bar_data.iloc[0].copy() - + initial_capital = int( + market_data_pct_chg_df.loc[ + (market_data_pct_chg_df["symbol"] == symbol) + & (market_data_pct_chg_df["bar"] == bar), + "initial_capital", + ].values[0] + ) # 创建初始化行，设置基准值 init_row = first_row.copy() - init_row.loc["pct_chg_total"] = 1.0 # 量化策略初始值为1 + init_row.loc["profit_loss"] = 0 + init_row.loc["end_account_value"] = ( + initial_capital # 量化策略初始值为初始资金 + ) init_row.loc["end_timestamp"] = first_row["begin_timestamp"] init_row.loc["end_date_time"] = first_row["begin_date_time"] init_row.loc["end_close"] = first_row["begin_close"] @@ -756,73 +948,121 @@ class MaBreakStatistics: init_row.loc["interval_minutes"] = 0 init_row.loc["interval_hours"] = 0 init_row.loc["interval_days"] = 0 - + # 将初始化行添加到数据开头 symbol_bar_data = pd.concat([pd.DataFrame([init_row]), symbol_bar_data]) - symbol_bar_data.sort_values(by="end_timestamp", ascending=True, inplace=True) + symbol_bar_data.sort_values( + by="end_timestamp", ascending=True, inplace=True + ) symbol_bar_data.reset_index(drop=True, inplace=True) - + # 确保时间列是datetime类型，避免matplotlib警告 - symbol_bar_data["end_date_time"] = pd.to_datetime(symbol_bar_data["end_date_time"]) - + symbol_bar_data["end_date_time"] = pd.to_datetime( + symbol_bar_data["end_date_time"] + ) + + # 计算持仓价值归一化数据（相对于初始价格） + symbol_bar_data["end_account_value_to_1"] = ( + symbol_bar_data["end_account_value"] / initial_capital + ) + symbol_bar_data["end_account_value_to_1"] = symbol_bar_data[ + "end_account_value_to_1" + ].round(4) + # 计算市场价位归一化数据（相对于初始价格） - symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close"] / init_row["end_close"] - symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close_to_1"].round(4) - + symbol_bar_data["end_close_to_1"] = ( + symbol_bar_data["end_close"] / init_row["end_close"] + ) + symbol_bar_data["end_close_to_1"] = symbol_bar_data[ + "end_close_to_1" + ].round(4) + # 绘制折线图 plt.figure(figsize=(12, 7)) - + # 绘制量化策略涨跌线（蓝色） - plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["pct_chg_total"], - label="量化策略涨跌", color='blue', linewidth=2, marker='o', markersize=4) - + plt.plot( + symbol_bar_data["end_date_time"], + symbol_bar_data["end_account_value_to_1"], + label="量化策略涨跌", + color="blue", + linewidth=2, + marker="o", + markersize=4, + ) + # 绘制市场自然涨跌线（绿色） - plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["end_close_to_1"], - label="市场自然涨跌", color='green', linewidth=2, marker='s', markersize=4) - - plt.title(f"{symbol} {bar} 量化与市场折线图_{strategy_name}", - fontsize=14, fontweight='bold') + plt.plot( + symbol_bar_data["end_date_time"], + symbol_bar_data["end_close_to_1"], + label="市场自然涨跌", + color="green", + linewidth=2, + marker="s", + markersize=4, + ) + + plt.title( + f"{symbol} {bar} 量化与市场折线图_{strategy_name}", + fontsize=14, + fontweight="bold", + ) plt.xlabel("时间", fontsize=12) plt.ylabel("涨跌变化", fontsize=12) plt.legend(fontsize=11) plt.grid(True, alpha=0.3) - + # 设置x轴标签，避免matplotlib警告 # 选择合适的时间间隔显示标签，避免过于密集 if len(symbol_bar_data) > 30: # 如果数据点较多，选择间隔显示，但确保第一条和最后一条始终显示 step = max(1, len(symbol_bar_data) // 30) - + # 创建标签索引列表，确保包含首尾数据 label_indices = [0] # 第一条 - + # 添加中间间隔的标签 for i in range(step, len(symbol_bar_data) - 1, step): label_indices.append(i) - + # 添加最后一条（如果还没有包含的话） if len(symbol_bar_data) - 1 not in label_indices: label_indices.append(len(symbol_bar_data) - 1) - + # 设置x轴标签 - plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices], - symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%Y%m%d %H:%M'), - rotation=45, ha='right') + plt.xticks( + symbol_bar_data["end_date_time"].iloc[label_indices], + symbol_bar_data["end_date_time"] + .iloc[label_indices] + .dt.strftime("%Y%m%d %H:%M"), + rotation=45, + ha="right", + ) else: # 如果数据点较少，全部显示 - plt.xticks(symbol_bar_data["end_date_time"], - symbol_bar_data["end_date_time"].dt.strftime('%Y%m%d %H:%M'), - rotation=45, ha='right') - + plt.xticks( + symbol_bar_data["end_date_time"], + symbol_bar_data["end_date_time"].dt.strftime("%Y%m%d %H:%M"), + rotation=45, + ha="right", + ) + plt.tight_layout() - - save_path = os.path.join( - self.stats_chart_dir, - f"{symbol}_{bar}_line_chart_{strategy_name}.png", - ) - plt.savefig(save_path, dpi=150, bbox_inches='tight') + + if self.commission_per_share > 0: + save_path = os.path.join( + self.stats_chart_dir, + f"{symbol}_{bar}_line_chart_{strategy_name}_with_commission.png", + ) + else: + save_path = os.path.join( + self.stats_chart_dir, + f"{symbol}_{bar}_line_chart_{strategy_name}_without_commission.png", + ) + + plt.savefig(save_path, dpi=150, bbox_inches="tight") plt.close() - + sheet_name = f"{symbol}_{bar}_折线图_{strategy_name}" chart_dict[sheet_name] = save_path return chart_dict diff --git a/market_monitor_main.py b/market_monitor_main.py index dd7af5e..a67d615 100644 --- a/market_monitor_main.py +++ b/market_monitor_main.py @@ -4,7 +4,7 @@ from huge_volume_main import HugeVolumeMain from core.biz.market_monitor import create_metrics_report from core.db.db_market_monitor import DBMarketMonitor from core.wechat import Wechat -from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WECHAT_CONFIG +from config import OKX_MONITOR_CONFIG, OKX_REALTIME_MONITOR_CONFIG, MYSQL_CONFIG, WECHAT_CONFIG from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp import core.logger as logging @@ -52,6 +52,14 @@ class MarketMonitorMain: else: self.is_binance = True + + self.symbols = OKX_REALTIME_MONITOR_CONFIG.get("volume_monitor", {}).get( + "symbols", ["XCH-USDT"] + ) + self.bars = OKX_REALTIME_MONITOR_CONFIG.get("volume_monitor", {}).get( + "bars", ["1m", "5m", "15m", "30m", "1H"] + ) + def get_latest_record(self): """ 获取最新记录 @@ -252,13 +260,13 @@ class MarketMonitorMain: 获取下一个长周期实时数据 """ if next: - # 获得bar在self.market_data_main.bars中的索引 - bar_index = self.market_data_main.bars.index(bar) - if bar_index == len(self.market_data_main.bars) - 1: + # 获得bar在self.bars中的索引 + bar_index = self.bars.index(bar) + if bar_index == len(self.bars) - 1: logger.error(f"已经是最后一个bar: {bar}") return None # 获得下一个bar - bar = self.market_data_main.bars[bar_index + 1] + bar = self.bars[bar_index + 1] # 获得下一个bar的实时数据 data = self.market_data_main.market_data.get_realtime_kline_data( symbol=symbol, bar=bar, end_time=end_time, limit=100 @@ -278,11 +286,11 @@ class MarketMonitorMain: only_output_over_mean_volume: bool = True, only_output_rise: bool = True, ): - for symbol in self.market_data_main.symbols: + for symbol in self.symbols: if self.is_binance and symbol == "XCH-USDT": logger.info(f"币安交易所无: {symbol}") continue - for bar in self.market_data_main.bars: + for bar in self.bars: logger.info( f"开始监控: {symbol} {bar} 窗口大小: {self.window_size} 行情数据" ) @@ -305,7 +313,7 @@ if __name__ == "__main__": market_monitor_main = MarketMonitorMain() market_monitor_main.monitor_realtime_market( symbol="BTC-USDT", - bar="5m", + bar="1m", only_output_huge_volume=False, only_output_rise=False, ) diff --git a/trade_ma_strategy_main.py b/trade_ma_strategy_main.py index 338c2fa..b7df31c 100644 --- a/trade_ma_strategy_main.py +++ b/trade_ma_strategy_main.py @@ -24,42 +24,48 @@ from config import ( logger = logging.logger + class TradeMaStrategyMain: - def __init__(self, is_us_stock: bool = False, is_binance: bool = False): - self.ma_break_statistics = MaBreakStatistics(is_us_stock=is_us_stock, is_binance=is_binance) - + def __init__( + self, + is_us_stock: bool = False, + is_binance: bool = False, + commission_per_share: float = 0, + ): + self.ma_break_statistics = MaBreakStatistics( + is_us_stock=is_us_stock, + is_binance=is_binance, + commission_per_share=commission_per_share, + ) + 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 - pct_chg_df_list = [] + 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 - pct_chg_df = self.ma_break_statistics.batch_statistics(strategy_name=strategy_name) - pct_chg_df_list.append(pct_chg_df) - - pct_chg_df = pd.concat(pct_chg_df_list) - - def statistics_pct_chg(self, pct_chg_df: pd.DataFrame): - """ - 1. 将各个symbol, 各个bar, 各个策略的pct_chg_total构建为新的数据结构,如： - symbol, bar, stratege_name_1, stratege_name_2, stratege_name_3, ... - stratege_name_1的值, 为该策略的pct_chg_total的值 - 2. 构建新的数据结构: symbol, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name - 如: BCT-USDT, 15m, 均线macd结合策略2, 全均线策略 - 3. 构建新的数据结构, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name - 如: 15m, 均线macd结合策略2, 全均线策略 - 4. 构建新的数据结构, symbol, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name - 如: BCT-USDT, 均线macd结合策略2, 全均线策略 - """ - logger.info("开始统计pct_chg") - - + 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") if __name__ == "__main__": - trade_ma_strategy_main = TradeMaStrategyMain(is_us_stock=False, is_binance=True) - trade_ma_strategy_main.batch_ma_break_statistics() \ No newline at end of file + commission_per_share_list = [0, 0.0008] + for commission_per_share in commission_per_share_list: + trade_ma_strategy_main = TradeMaStrategyMain( + is_us_stock=False, + is_binance=True, + commission_per_share=commission_per_share, + ) + trade_ma_strategy_main.batch_ma_break_statistics()