如何将tbquant上的策略转换成python代码

import pandas as pd import numpy as np import matplotlib.pyplot as plt class VolumeWeightedMomentumSys: def __init__(self, data, mom_len=5, avg_len=20, atr_len=5, atr_pcnt=0.5, setup_len=5, fixed_size=1): self.data = data self.mom_len = mom_len self.avg_len = avg_len self.atr_len = atr_len self.atr_pcnt = atr_pcnt self.setup_len = setup_len self.fixed_size = fixed_size # Variables to track self.vwm = None # 成交量加权动量 self.atr = None # ATR self.se_price = 0 # 当前价格通道 self.ssetup = 0 # 条件计数器 self.intra_trade_high = 0 self.intra_trade_low = 0 self.position = 0 # 持仓：0为无仓，-1为空单 self.profits = [] # 用于存储交易盈亏 self.capital_curve = [] # 资金曲线 self.current_capital = 100000 # 初始资本 self.dates = [] # 保存完整的日期 self.init_indicators() # 初始化指标 def init_indicators(self): """初始化指标计算""" # 计算动量 momentum = self.data['close'].diff(self.mom_len) # 计算成交量加权动量 (VWM) vol_momentum = self.data['vol'] * momentum self.vwm = vol_momentum.rolling(window=self.avg_len).mean() # 计算ATR high_low = self.data['high'] - self.data['low'] high_close = (self.data['high'] - self.data['close'].shift()).abs() low_close = (self.data['low'] - self.data['close'].shift()).abs() true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1) self.atr = true_range.rolling(window=self.atr_len).mean() def on_bar(self, bar_index): """逐根K线回测逻辑""" # 检查是否足够数据初始化 if bar_index < max(self.avg_len, self.atr_len): # 保持资金和日期不变 self.capital_curve.append(self.current_capital) self.dates.append(self.data.index[bar_index]) return # 获取当前K线数据 bar = self.data.iloc[bar_index] # 检查是否有持仓 if self.position == 0: self.intra_trade_high = bar['high'] self.intra_trade_low = bar['low'] # 判断空头势 if self.vwm.iloc[bar_index] < 0: self.ssetup = 0 self.se_price = bar['close'] else: self.ssetup += 1 # 入场条件 if self.ssetup <= self.setup_len and self.ssetup >= 1: entry_price = self.se_price - (self.atr.iloc[bar_index] * self.atr_pcnt) if bar['low'] <= entry_price: # 做空 self.position = -1 self.entry_price = entry_price self.intra_trade_high = bar['high'] print(f"Sell short at {entry_price} on {bar.name}") elif self.position == -1: # 更新最高价格 self.intra_trade_high = max(self.intra_trade_high, bar['high']) # 出场条件 if self.vwm.iloc[bar_index] > 0: exit_price = bar['open'] # 平仓价为开盘价 profit = (self.entry_price - exit_price) * self.fixed_size self.current_capital += profit # 更新资金 self.profits.append(profit) self.position = 0 print(f"Buy to cover at {exit_price} on {bar.name}. Profit: {profit}") # 记录资金曲线和日期 self.capital_curve.append(self.current_capital) self.dates.append(bar.name) def backtest(self): """运行回测""" for i in range(len(self.data)): self.on_bar(i) # 打印总盈亏 total_profit = sum(self.profits) print(f"Total profit: {total_profit}") return self.capital_curve, self.dates # 加载数据 open_data = pd.read_csv(r'C:\Users\User\Desktop\data(min5)\open.csv', index_col=0, parse_dates=True) close_data = pd.read_csv(r'C:\Users\User\Desktop\data(min5)\close.csv', index_col=0, parse_dates=True) high_data = pd.read_csv(r'C:\Users\User\Desktop\data(min5)\high.csv', index_col=0, parse_dates=True) low_data = pd.read_csv(r'C:\Users\User\Desktop\data(min5)\low.csv', index_col=0, parse_dates=True) vol_data = pd.read_csv(r'C:\Users\User\Desktop\data(min5)\vol.csv', index_col=0, parse_dates=True) # 将数据合并成一个DataFrame data = pd.DataFrame({ 'open': open_data['ag'], 'close': close_data['ag'], 'high': high_data['ag'], 'low': low_data['ag'], 'vol': vol_data['ag'], }, index=open_data.index) # 初始化策略 strategy = VolumeWeightedMomentumSys(data) # 回测 profits, dates = strategy.backtest() # 绘制资金曲线 plt.figure(figsize=(12, 6)) plt.plot(dates, np.cumsum(profits), label='Cumulative Profit') # 累积盈亏 plt.xlabel('Date') plt.ylabel('Profit') plt.title('Capital Curve Over Time') plt.legend() plt.grid() plt.show()

回复：//------------------------------------------------------------------------ // 简称: VolumeWeightedMomentumSys_S // 名称: 成交量加权动量交易系统 空 // 类别: 策略应用 // 类型: 内建应用 // 输出: // 策略说明: // 基于动量系统, 通过交易量加权进行判断 // // 系统要素: // 1. 用UWM下穿零轴判断空头趋势 // 入场条件: // 1. 价格低于UWM下穿零轴时价格通道,在SetupLen的BAR数目内,做空 // // 出场条件: // 1. 多头势空单出场 // 注: //----------------------------------------------------------------------// Params Numeric MomLen(5); //UWM参数 Numeric AvgLen(20); //UWM参数 Numeric ATRLen(5); //ATR参数 Numeric ATRPcnt(0.5); //入场价格波动率参数 Numeric SetupLen(5); //条件持续有效K线数 Vars Series<Numeric> VWM(0); Series<Numeric> AATR(0); Series<Numeric> SEPrice(0); Series<Bool> BullSetup(False); Series<Bool> BearSetup(False); Series<Numeric> SSetup(0); Events OnBar(ArrayRef<Integer> indexs) { VWM = XAverage(Vol * Momentum(Close, MomLen), AvgLen); //定义UWM AATR = AvgTrueRange(ATRLen); //ATR BullSetup = CrossOver(VWM,0); //UWM上穿零轴定义多头势 BearSetup = CrossUnder(VWM,0); //UWM下穿零轴定义空头势 If (BearSetup ) //空头势开始计数并记录当前价格 { SSetup = 0; SEPrice = Close; } Else SSetup = SSetup[1] + 1; //每过一根BAR计数 //系统入场 If (CurrentBar > AvgLen And MarketPosition == 0 ) //当空头势满足并且在SetupLen的BAR数目内,当价格达到入场价格后,做空 { If( Low <=SEPrice[1] - (ATRPcnt * AATR[1]) And SSetup[1] <= SetupLen And SSetup >= 1 And Vol > 0) { SellShort(0, Min(Open,SEPrice[1] - (ATRPcnt * AATR[1]))) ; } } //系统出场 If (MarketPosition == -1 And BarsSinceEntry > 0 And Vol > 0) //多头势平掉空单 { If( BullSetup[1] == True ) { Buytocover(0,Open); } } } //------------------------------------------------------------------------ // 编译版本 GS2014.10.25 // 版权所有 TradeBlazer Software 2003－2025 // 更改声明 TradeBlazer Software保留对TradeBlazer平 // 台每一版本的TradeBlazer公式修改和重写的权利 //------------------------------------------------------------------------

回复：python每次跑出来的曲线都差不多长这样的，跟tb上完全不一样😭