optimize orb strategy
This commit is contained in:
parent
f79834647d
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@ -4,3 +4,4 @@
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core/db/__pycache__/*.pyc
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core/biz/__pycache__/*.pyc
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core/statistics/__pycache__/*.pyc
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/data
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Binary file not shown.
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@ -38,6 +38,8 @@ class ORBStrategy:
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direction=None,
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by_sar=False,
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symbol_bar_data=None,
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price_range_mean_as_R=False,
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by_big_k=False,
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):
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"""
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初始化ORB策略参数
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@ -63,6 +65,8 @@ class ORBStrategy:
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:param is_us_stock: 是否是美股
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:param direction: 方向，None=自动，Long=多头，Short=空头
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:param by_sar: 是否根据SAR指标生成信号，True=是，False=否
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:param price_range_mean_as_R: 是否将价格振幅均值作为$R，True=是，False=否
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:param by_big_k: 是否根据K线实体部分，亦即abs(open-close)超过high-low的50%，True=是，False=否
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"""
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logger.info(
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f"初始化ORB策略参数：股票代码={symbol}，K线周期={bar}，开始日期={start_date}，结束日期={end_date}，初始账户资金={initial_capital}，最大杠杆倍数={max_leverage}，单次交易风险比例={risk_per_trade}，每股交易佣金={commission_per_share}"
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@ -101,12 +105,23 @@ class ORBStrategy:
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self.direction_desc = "做多"
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elif self.direction == "Short":
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self.direction_desc = "做空"
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self.sar_desc = "不考虑SAR"
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if self.by_sar:
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self.sar_desc = "考虑SAR"
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self.symbol_bar_data = symbol_bar_data
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self.price_range_mean_as_R = price_range_mean_as_R
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if self.price_range_mean_as_R:
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self.price_range_mean_as_R_desc = "R为振幅均值"
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else:
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self.price_range_mean_as_R_desc = "R为entry减stop"
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self.by_big_k = by_big_k
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if self.by_big_k:
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self.by_big_k_desc = "K线实体过50%"
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else:
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self.by_big_k_desc = "无K线要求"
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def run(self):
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"""
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运行ORB策略
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@ -127,7 +142,9 @@ class ORBStrategy:
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:param end_date: 结束日期（格式：YYYY-MM-DD）
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:param interval: K线周期（默认5分钟）
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"""
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logger.info(f"开始获取{self.symbol}数据：{self.start_date}至{self.end_date}，间隔{self.bar}")
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logger.info(
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f"开始获取{self.symbol}数据：{self.start_date}至{self.end_date}，间隔{self.bar}"
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)
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if self.symbol_bar_data is None or len(self.symbol_bar_data) == 0:
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data = self.db_market_data.query_market_data_by_symbol_bar(
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self.symbol, self.bar, start=self.start_date, end=self.end_date
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@ -153,7 +170,6 @@ class ORBStrategy:
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self.start_date = data["Date"].min().strftime("%Y-%m-%d")
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# 最大data["Date"]
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self.end_date = data["Date"].max().strftime("%Y-%m-%d")
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self.data = data[
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[
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"symbol",
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@ -169,6 +185,7 @@ class ORBStrategy:
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]
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].copy()
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self.data.rename(columns={date_time_field: "date_time"}, inplace=True)
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self.calculate_price_range_mean()
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self.symbol_bar_data = self.data.copy()
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else:
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self.data = self.symbol_bar_data.copy()
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@ -189,21 +206,24 @@ class ORBStrategy:
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pass
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logger.info(f"收盘价均值：{self.close_mean}")
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logger.info(f"初始资金调整为：{self.initial_capital}")
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logger.info(f"成功获取{self.symbol}数据：{len(self.data)}根{self.bar}K线,开始日期={self.start_date}，结束日期={self.end_date}")
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logger.info(
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f"成功获取{self.symbol}数据：{len(self.data)}根{self.bar}K线,开始日期={self.start_date}，结束日期={self.end_date}"
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)
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def calculate_shares(self, account_value, entry_price, stop_price):
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def calculate_shares(self, account_value, entry_price, stop_price, risk_assumed):
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"""
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根据ORB公式计算交易股数
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:param account_value: 当前账户价值（美元）
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:param entry_price: 交易entry价格（第二根5分钟K线开盘价）
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:param stop_price: 止损价格（多头=第一根K线最低价，空头=第一根K线最高价）
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:param risk_assumed: 风险金额（$R）,根据price_range_mean_as_R决定
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:return: 整数股数（Shares）
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"""
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logger.info(
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f"开始计算交易股数：账户价值={account_value}，entry价格={entry_price}，止损价格={stop_price}"
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)
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# 计算单交易风险金额（$R）
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risk_per_trade_dollar = abs(entry_price - stop_price) # 风险金额取绝对值
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risk_per_trade_dollar = risk_assumed # 风险金额取绝对值
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if risk_per_trade_dollar <= 0:
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return 0 # 无风险时不交易
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@ -247,6 +267,12 @@ class ORBStrategy:
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close1 = first_candle["Close"]
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sar_signal = first_candle["sar_signal"]
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if high1 == low1:
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continue
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if self.by_big_k:
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if (abs(open1 - close1) / (high1 - low1)) < 0.5:
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continue
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# 第二根5分钟K线（entry信号）
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second_candle = daily_data.iloc[1]
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entry_price = second_candle["Open"] # entry价格=第二根K线开盘价
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@ -303,6 +329,17 @@ class ORBStrategy:
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f"生成信号完成：共{len(signals_df)}个交易日，其中多头{sum(signals_df['Signal']=='Long')}次，空头{sum(signals_df['Signal']=='Short')}次"
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)
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def calculate_price_range_mean(self):
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"""
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计算价格振幅均值，振幅为最高价与最低价之差
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计算价格振幅标准差
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要求用滑动窗口: window_size=100计算均值，每次计算都包含当前行
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返回一个新列，列名为"PriceRangeMean"
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"""
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self.data["PriceRange"] = self.data["High"] - self.data["Low"]
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self.data["PriceRangeMean"] = self.data["PriceRange"].rolling(window=100).mean()
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self.data["PriceRangeStd"] = self.data["PriceRange"].rolling(window=100).std()
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def backtest(self):
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"""
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回测ORB策略
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@ -343,7 +380,18 @@ class ORBStrategy:
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stop_price = signal_row["StopPrice"]
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high1 = signal_row["High1"]
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low1 = signal_row["Low1"]
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risk_assumed = abs(entry_price - stop_price) # 计算$R
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price_range = signal_row["PriceRange"]
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price_range_mean = signal_row["PriceRangeMean"]
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price_range_std = signal_row["PriceRangeStd"]
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# 计算$R
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if (
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self.price_range_mean_as_R
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and price_range_mean is not None
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and price_range_mean > 0
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):
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risk_assumed = price_range_mean
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else:
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risk_assumed = abs(entry_price - stop_price)
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profit_target = (
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entry_price + (risk_assumed * self.profit_target_multiple)
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if signal == "Long"
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@ -351,7 +399,9 @@ class ORBStrategy:
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)
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# 计算交易股数
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shares = self.calculate_shares(account_value, entry_price, stop_price)
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shares = self.calculate_shares(
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account_value, entry_price, stop_price, risk_assumed
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)
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if shares == 0:
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# 股数为0→不交易
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equity_history.append(account_value)
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@ -425,6 +475,8 @@ class ORBStrategy:
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"TradeID": trade_id,
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"Direction": self.direction_desc,
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"BySar": self.sar_desc,
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"PriceRangeMeanAsR": self.price_range_mean_as_R_desc,
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"ByBigK": self.by_big_k_desc,
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"Symbol": self.symbol,
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"Bar": self.bar,
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"Date": date,
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@ -467,8 +519,12 @@ class ORBStrategy:
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else 0
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)
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# 计算盈亏比
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profit_sum = self.trades_df[self.trades_df["ProfitLoss"] > 0]["ProfitLoss"].sum()
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loss_sum = abs(self.trades_df[self.trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum())
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profit_sum = self.trades_df[self.trades_df["ProfitLoss"] > 0][
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"ProfitLoss"
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].sum()
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loss_sum = abs(
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self.trades_df[self.trades_df["ProfitLoss"] < 0]["ProfitLoss"].sum()
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)
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if loss_sum == 0:
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profit_loss_ratio = float("inf")
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else:
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@ -504,11 +560,13 @@ class ORBStrategy:
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logger.info(f"最大单笔盈利：${self.trades_df['ProfitLoss'].max():.2f}")
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self.trades_summary["最大单笔盈利$"] = self.trades_df["ProfitLoss"].max()
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logger.info(f"最大单笔亏损：${abs(self.trades_df['ProfitLoss'].min()):.2f}")
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self.trades_summary["最大单笔亏损$"] = abs(self.trades_df["ProfitLoss"].min())
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self.trades_summary["最大单笔亏损$"] = abs(
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self.trades_df["ProfitLoss"].min()
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)
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else:
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logger.info("没有交易")
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self.trades_summary_df = pd.DataFrame([self.trades_summary])
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def initial_trade_summary(self):
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"""
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初始化交易总结
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@ -516,6 +574,8 @@ class ORBStrategy:
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self.trades_summary = {}
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self.trades_summary["方向"] = self.direction_desc
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self.trades_summary["根据SAR"] = self.sar_desc
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self.trades_summary["R算法"] = self.price_range_mean_as_R_desc
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self.trades_summary["K线条件"] = self.by_big_k_desc
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self.trades_summary["股票代码"] = self.symbol
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self.trades_summary["K线周期"] = self.bar
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self.trades_summary["开始日期"] = self.start_date
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@ -532,7 +592,6 @@ class ORBStrategy:
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self.trades_summary["最大单笔盈利$"] = 0
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self.trades_summary["最大单笔亏损$"] = 0
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def create_equity_curve(self):
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"""
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创建账户净值曲线
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@ -600,7 +659,11 @@ class ORBStrategy:
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marker="s",
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markersize=4,
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)
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plt.title(f"ORB曲线 {symbol} {bar} {self.direction_desc} {self.sar_desc}", fontsize=14, fontweight="bold")
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plt.title(
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f"{symbol} {bar} {self.direction_desc} {self.sar_desc} {self.price_range_mean_as_R_desc} {self.by_big_k_desc}",
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fontsize=14,
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fontweight="bold",
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)
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plt.xlabel("时间", fontsize=12)
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plt.ylabel("涨跌变化", fontsize=12)
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plt.legend(fontsize=11)
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@ -641,30 +704,26 @@ class ORBStrategy:
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fontsize=10,
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)
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plt.tight_layout()
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self.chart_save_path = (
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f"{self.output_chart_folder}/{symbol}_{bar}_{self.direction_desc}_{self.sar_desc}_orb_strategy_equity_curve.png"
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)
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self.chart_save_path = f"{self.output_chart_folder}/{symbol}_{bar}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}_orb.png"
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plt.savefig(self.chart_save_path, dpi=150, bbox_inches="tight")
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plt.close()
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def output_trade_summary(self):
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"""
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输出交易明细，交易总结与Chart图片到Excel
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"""
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start_date = self.start_date.replace("-", "")
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end_date = self.end_date.replace("-", "")
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output_file_name = f"orb_{self.symbol}_{self.bar}_{start_date}_{end_date}_{self.direction_desc}_{self.sar_desc}.xlsx"
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output_file_name = f"orb_{self.symbol}_{self.bar}_{start_date}_{end_date}_{self.direction_desc}_{self.sar_desc}_{self.price_range_mean_as_R_desc}_{self.by_big_k_desc}.xlsx"
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output_file_path = os.path.join(self.output_excel_folder, output_file_name)
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logger.info(f"导出{output_file_path}")
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with pd.ExcelWriter(output_file_path) as writer:
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self.trades_df.to_excel(writer, sheet_name="交易明细", index=False)
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self.trades_summary_df.to_excel(writer, sheet_name="交易总结", index=False)
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if os.path.exists(self.chart_save_path):
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charts_dict = {
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"账户净值曲线": self.chart_save_path
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}
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charts_dict = {"账户净值曲线": self.chart_save_path}
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self.output_chart_to_excel(output_file_path, charts_dict)
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def output_chart_to_excel(self, excel_file_path: str, charts_dict: dict):
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"""
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输出Excel文件，包含所有图表
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@ -694,7 +753,7 @@ class ORBStrategy:
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# Save Excel file
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wb.save(excel_file_path)
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logger.info(f"图表已输出到{excel_file_path}")
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# ------------------- 策略示例：回测QQQ的ORB策略（2016-2023） -------------------
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if __name__ == "__main__":
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@ -16,6 +16,8 @@ def main():
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bar = "5m"
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direction_list = [None, "Long", "Short"]
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by_sar_list = [False, True]
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price_range_mean_as_R_list = [False, True]
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by_big_k_list = [False, True]
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start_date = "2024-01-01"
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end_date = datetime.now().strftime("%Y-%m-%d")
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profit_target_multiple = 10
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@ -30,55 +32,59 @@ def main():
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for is_us_stock in is_us_stock_list:
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for direction in direction_list:
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for by_sar in by_sar_list:
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if is_us_stock:
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symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["QQQ"]
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)
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else:
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symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["BTC-USDT"]
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)
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for symbol in symbols:
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logger.info(
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f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}"
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)
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symbol_bar_data = None
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found_symbol_bar_data = False
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for symbol_data_dict in symbol_data_cache:
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if (
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symbol_data_dict["symbol"] == symbol
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and symbol_data_dict["bar"] == bar
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):
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symbol_bar_data = symbol_data_dict["data"]
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found_symbol_bar_data = True
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break
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for price_range_mean_as_R in price_range_mean_as_R_list:
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for by_big_k in by_big_k_list:
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if is_us_stock:
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symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["QQQ"]
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)
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else:
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symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["BTC-USDT"]
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)
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for symbol in symbols:
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logger.info(
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f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}, 是否使用R为entry减stop:{price_range_mean_as_R}, 是否使用K线实体过50%:{by_big_k}"
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)
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symbol_bar_data = None
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found_symbol_bar_data = False
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for symbol_data_dict in symbol_data_cache:
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if (
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symbol_data_dict["symbol"] == symbol
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and symbol_data_dict["bar"] == bar
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):
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symbol_bar_data = symbol_data_dict["data"]
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found_symbol_bar_data = True
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break
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orb_strategy = ORBStrategy(
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symbol=symbol,
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bar=bar,
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start_date=start_date,
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end_date=end_date,
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is_us_stock=is_us_stock,
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direction=direction,
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by_sar=by_sar,
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profit_target_multiple=profit_target_multiple,
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initial_capital=initial_capital,
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max_leverage=max_leverage,
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risk_per_trade=risk_per_trade,
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commission_per_share=commission_per_share,
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symbol_bar_data=symbol_bar_data,
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)
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symbol_bar_data, trades_df, trades_summary_df = orb_strategy.run()
|
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if symbol_bar_data is None or len(symbol_bar_data) == 0:
|
||||
continue
|
||||
if not found_symbol_bar_data:
|
||||
symbol_data_cache.append(
|
||||
{"symbol": symbol, "bar": bar, "data": symbol_bar_data}
|
||||
)
|
||||
if trades_summary_df is None or len(trades_summary_df) == 0:
|
||||
continue
|
||||
trades_summary_df_list.append(trades_summary_df)
|
||||
trades_df_list.append(trades_df)
|
||||
orb_strategy = ORBStrategy(
|
||||
symbol=symbol,
|
||||
bar=bar,
|
||||
start_date=start_date,
|
||||
end_date=end_date,
|
||||
is_us_stock=is_us_stock,
|
||||
direction=direction,
|
||||
by_sar=by_sar,
|
||||
profit_target_multiple=profit_target_multiple,
|
||||
initial_capital=initial_capital,
|
||||
max_leverage=max_leverage,
|
||||
risk_per_trade=risk_per_trade,
|
||||
commission_per_share=commission_per_share,
|
||||
symbol_bar_data=symbol_bar_data,
|
||||
price_range_mean_as_R=price_range_mean_as_R,
|
||||
by_big_k=by_big_k,
|
||||
)
|
||||
symbol_bar_data, trades_df, trades_summary_df = orb_strategy.run()
|
||||
if symbol_bar_data is None or len(symbol_bar_data) == 0:
|
||||
continue
|
||||
if not found_symbol_bar_data:
|
||||
symbol_data_cache.append(
|
||||
{"symbol": symbol, "bar": bar, "data": symbol_bar_data}
|
||||
)
|
||||
if trades_summary_df is None or len(trades_summary_df) == 0:
|
||||
continue
|
||||
trades_summary_df_list.append(trades_summary_df)
|
||||
trades_df_list.append(trades_df)
|
||||
total_trades_df = pd.concat(trades_df_list)
|
||||
total_trades_summary_df = pd.concat(trades_summary_df_list)
|
||||
statitics_dict = statistics_summary(total_trades_summary_df)
|
||||
|
|
@ -97,13 +103,19 @@ def main():
|
|||
writer, sheet_name="最大总收益率记录", index=False
|
||||
)
|
||||
statitics_dict["max_total_return_record_df_grouped_count"].to_excel(
|
||||
writer, sheet_name="最大总收益率记录_方向和根据SAR的组合", index=False
|
||||
writer, sheet_name="最大总收益率_所有条件组合", index=False
|
||||
)
|
||||
statitics_dict["max_total_return_record_df_direction_count"].to_excel(
|
||||
writer, sheet_name="最大总收益率记录_方向", index=False
|
||||
writer, sheet_name="最大总收益率_方向", index=False
|
||||
)
|
||||
statitics_dict["max_total_return_record_df_sar_count"].to_excel(
|
||||
writer, sheet_name="最大总收益率记录_根据SAR", index=False
|
||||
writer, sheet_name="最大总收益率_根据SAR", index=False
|
||||
)
|
||||
statitics_dict["max_total_return_record_df_R_count"].to_excel(
|
||||
writer, sheet_name="最大总收益率_R算法", index=False
|
||||
)
|
||||
statitics_dict["max_total_return_record_df_K_count"].to_excel(
|
||||
writer, sheet_name="最大总收益率_K线条件", index=False
|
||||
)
|
||||
chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
|
||||
os.makedirs(chart_path, exist_ok=True)
|
||||
|
|
@ -156,6 +168,8 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
|
|||
summary["股票代码"] = symbol
|
||||
summary["方向"] = max_total_return_record["方向"]
|
||||
summary["根据SAR"] = max_total_return_record["根据SAR"]
|
||||
summary["R算法"] = max_total_return_record["R算法"]
|
||||
summary["K线条件"] = max_total_return_record["K线条件"]
|
||||
summary["总收益率%"] = max_total_return_record["总收益率%"]
|
||||
summary["自然收益率%"] = max_total_return_record["自然收益率%"]
|
||||
max_total_return_record_list.append(summary)
|
||||
|
|
@ -178,14 +192,14 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
|
|||
# 其它（如Series、list、dict、ndarray等）转字符串
|
||||
return str(v)
|
||||
|
||||
for key_col in ["方向", "根据SAR"]:
|
||||
for key_col in ["方向", "根据SAR", "R算法", "K线条件"]:
|
||||
if key_col in max_total_return_record_df.columns:
|
||||
max_total_return_record_df[key_col] = max_total_return_record_df[
|
||||
key_col
|
||||
].apply(_to_hashable_scalar)
|
||||
# 分组统计
|
||||
max_total_return_record_df_grouped_count = (
|
||||
max_total_return_record_df.groupby(["方向", "根据SAR"], dropna=False)
|
||||
max_total_return_record_df.groupby(["方向", "根据SAR", "R算法", "K线条件"], dropna=False)
|
||||
.size()
|
||||
.reset_index(name="数量")
|
||||
)
|
||||
|
|
@ -215,15 +229,39 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
|
|||
by="数量", ascending=False, inplace=True
|
||||
)
|
||||
max_total_return_record_df_sar_count.reset_index(drop=True, inplace=True)
|
||||
|
||||
# 统计R算法的记录数目
|
||||
max_total_return_record_df_R_count = (
|
||||
max_total_return_record_df.groupby(["R算法"], dropna=False)
|
||||
.size()
|
||||
.reset_index(name="数量")
|
||||
)
|
||||
max_total_return_record_df_R_count.sort_values(
|
||||
by="数量", ascending=False, inplace=True
|
||||
)
|
||||
max_total_return_record_df_R_count.reset_index(drop=True, inplace=True)
|
||||
|
||||
# 统计K线条件的记录数目
|
||||
max_total_return_record_df_K_count = (
|
||||
max_total_return_record_df.groupby(["K线条件"], dropna=False)
|
||||
.size()
|
||||
.reset_index(name="数量")
|
||||
)
|
||||
max_total_return_record_df_K_count.sort_values(
|
||||
by="数量", ascending=False, inplace=True
|
||||
)
|
||||
max_total_return_record_df_K_count.reset_index(drop=True, inplace=True)
|
||||
else:
|
||||
# 构造空结果，保证下游写入Excel不报错
|
||||
max_total_return_record_df_grouped_count = pd.DataFrame(
|
||||
columns=["方向", "根据SAR", "数量"]
|
||||
)
|
||||
columns=["方向", "根据SAR", "R算法", "K线条件", "数量"]
|
||||
)
|
||||
max_total_return_record_df_direction_count = pd.DataFrame(
|
||||
columns=["方向", "数量"]
|
||||
)
|
||||
max_total_return_record_df_sar_count = pd.DataFrame(columns=["根据SAR", "数量"])
|
||||
max_total_return_record_df_R_count = pd.DataFrame(columns=["R算法", "数量"])
|
||||
max_total_return_record_df_K_count = pd.DataFrame(columns=["K线条件", "数量"])
|
||||
|
||||
result = {
|
||||
"statistics_summary_df": statistics_summary_df,
|
||||
|
|
@ -231,6 +269,8 @@ def statistics_summary(trades_summary_df: pd.DataFrame):
|
|||
"max_total_return_record_df_grouped_count": max_total_return_record_df_grouped_count,
|
||||
"max_total_return_record_df_direction_count": max_total_return_record_df_direction_count,
|
||||
"max_total_return_record_df_sar_count": max_total_return_record_df_sar_count,
|
||||
"max_total_return_record_df_R_count": max_total_return_record_df_R_count,
|
||||
"max_total_return_record_df_K_count": max_total_return_record_df_K_count,
|
||||
}
|
||||
return result
|
||||
|
||||
|
|
@ -335,9 +375,5 @@ def test():
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# main()
|
||||
|
||||
chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
|
||||
excel_file_path = r"./output/trade_sandbox/orb_strategy/excel/summary/orb_strategy_summary_20250902174203.xlsx"
|
||||
copy_chart_to_excel(chart_path, excel_file_path)
|
||||
main()
|
||||
# test()
|
||||
|
|
|
|||
Loading…
Reference in New Issue