Ai Trading Backtester

Dev Tools

AI-powered quantitative trading strategy backtesting assistant. Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. Keywords: trading backtest, quantitative strategy, algorithmic trading, Python backtesting, backtrader, vectorbt, trading strategy, momentum, mean reversion, pairs trading, A-share strategy, financial data, technical indicators.

Install

openclaw skills install ai-trading-backtester

AI Trading Strategy Backtester

Overview

An AI-powered quantitative trading strategy design and backtesting assistant that helps you transform trading ideas into fully-coded, backtested strategies. It guides you through strategy design (mean reversion, momentum, breakout, pairs trading, ML-based), implements them in Python (backtrader, vectorbt, pandas), evaluates performance across historical data for A-share, HK, and US markets, and produces risk-adjusted performance reports.

Triggers

"backtest my trading strategy"
"design a momentum strategy for [stock/market]"
"test mean reversion on [symbol]"
"pairs trading strategy example"
"Python backtrader setup guide"
"vectorbt tutorial"
"trading strategy optimization"
"量化回测策略"
"技术指标择时策略"
"A股量化策略设计"

Workflow

Step 1: Define the Strategy Brief

Collect the trading idea:

Strategy type: Momentum, mean reversion, breakout, pairs trading, ML-based, event-driven
Market: A-share (sh/sz), HK stock (hk), US equity (us)
Timeframe: Intraday (1m/5m/15m), daily, weekly, monthly
Assets: Single stock, ETF, index, portfolio
Entry/Exit signals: Technical indicators, price patterns, fundamental signals, ML predictions
Position sizing: Fixed, Kelly criterion, risk-parity, dynamic
Constraints: Max position size, long-only/short, turnover limit, slippage model

Step 2: Strategy Design & Code Generation

Based on the brief, generate production-quality Python code:

A. Momentum Strategy Template

import pandas as pd
import numpy as np
import backtrader as bt

class MomentumStrategy(bt.Strategy):
    params = (
        ('lookback', 20),       # 回望期
        ('hold_period', 5),    # 持有期
        ('rank_percentile', 0.2),  # 选股分位数
    )

    def __init__(self):
        self.inds = {}
        for d in self.datas:
            self.inds[d] = {}
            self.inds[d]['momentum'] = bt.indicators.RateOfChange(
                d.close, period=self.params.lookback
            )

    def next(self):
        # 按动量排序，取前20%
        rankings = sorted(
            self.datas,
            key=lambda d: self.inds[d]['momentum'][0],
            reverse=True
        )[:int(len(self.datas) * self.params.rank_percentile)]

        # 平仓不在榜单的持仓
        for d in self.datas:
            if d not in rankings and self.getposition(d).size > 0:
                self.close(d)

        # 买入榜单中的标的
        for d in rankings:
            if self.getposition(d).size == 0:
                self.order_target_percent(d, 1.0 / len(rankings))

B. Mean Reversion Strategy Template

class MeanReversionStrategy(bt.Strategy):
    params = (
        ('bb_period', 20),
        ('bb_dev', 2.0),
        ('rsi_period', 14),
        ('rsi_oversold', 30),
        ('rsi_overbought', 70),
    )

    def __init__(self):
        self.bb = bt.indicators.BollingerBands(
            self.data.close, period=self.params.bb_period,
            devfactor=self.params.bb_dev
        )
        self.rsi = bt.indicators.RSI(
            self.data.close, period=self.params.rsi_period
        )

    def next(self):
        if self.position.size == 0:
            # 价格触及下轨且RSI超卖 → 买入
            if self.data.close < self.bb.lines.bot and \
               self.rsi < self.params.rsi_oversold:
                self.order_target_percent(self.data, 1.0)
        else:
            # 价格触及上轨或RSI超买 → 卖出
            if self.data.close > self.bb.lines.top or \
               self.rsi > self.params.rsi_overbought:
                self.close()

C. Pairs Trading Strategy

import statsmodels.api as sm

def find_cointegrated_pairs(data_dict):
    """寻找协整配对"""
    n = len(data_dict)
    pairs = []
    symbols = list(data_dict.keys())

    for i in range(n):
        for j in range(i + 1, n):
            try:
                x = data_dict[symbols[i]]
                y = data_dict[symbols[j]]
                # OLS回归
                X = sm.add_constant(x)
                model = sm.OLS(y, X).fit()
                residuals = model.resid
                # ADF检验
                adf_result = sm.tsa.stattools.adfuller(residuals)
                if adf_result[0] < adf_result[4]['1%']:
                    pairs.append((symbols[i], symbols[j], adf_result[0]))
            except:
                continue
    return sorted(pairs, key=lambda x: x[2])

def pairs_trading_signals(spread, z_entry=2.0, z_exit=0.5):
    """配对交易信号"""
    signals = pd.Series(0, index=spread.index)
    z_score = (spread - spread.mean()) / spread.std()

    signals[z_score < -z_entry] = 1    # 做多价差
    signals[z_score > z_entry] = -1     # 做空价差
    signals[abs(z_score) < z_exit] = 0  # 平仓
    return signals

Step 3: Backtest Execution

Guide the user through running the backtest:

import backtrader as bt
import pandas as pd

# 加载数据
data = bt.feeds.GenericCSVData(
    dataname='historical_data.csv',
    dtformat='%Y-%m-%d',
    datetime=0,
    open=1, high=2, low=3, close=4, volume=5,
    openinterest=-1
)

# 运行回测
cerebro = bt.Cerebro()
cerebro.addstrategy(MomentumStrategy)
cerebro.adddata(data)
cerebro.broker.setcash(1000000.0)  # 100万初始资金
cerebro.broker.setcommission(commission=0.001)  # 千一手续费
cerebro.addsizer(bt.sizers.PercentSizer, percents=95)

print(f'初始资金: {cerebro.broker.getvalue():,.2f}')
cerebro.run()
print(f'最终资金: {cerebro.broker.getvalue():,.2f}')

Step 4: Performance Analysis

Generate comprehensive performance metrics:

Metric	Description	Target
Total Return	Cumulative return	> Benchmark
Annualized Return	CAGR	> 10% (A-share), > 8% (HK/US)
Sharpe Ratio	Risk-adjusted return	> 1.5
Max Drawdown	Peak-to-trough loss	< 20%
Win Rate	Percentage of profitable trades	> 50%
Profit Factor	Gross profit / Gross loss	> 1.5
Calmar Ratio	Annual return / Max DD	> 1.0
Sortino Ratio	Return / Downside deviation	> 1.0

Step 5: Optimization & Stress Testing

A. 参数优化
   - Grid search over key parameters
   - Walk-forward analysis (in-sample / out-of-sample)
   - Avoid overfitting: use Information Coefficient (IC) analysis

B. 压力测试
   - Historical crises: 2008, 2015 A-share crash, COVID-19 (2020)
   - Monte Carlo simulation of equity curves
   - Sensitivity analysis: commission, slippage, spread assumptions

C. 风险分析
   - Position-level VaR (Value at Risk)
   - Factor exposure (momentum, size, volatility)
   - Tail risk: maximum loss scenarios

Input/Output Examples

Example 1: Strategy Design Request

Input:

Design a momentum strategy for A-share market.
Criteria:
- Daily timeframe
- Lookback: 20 days
- Hold for 5 days
- Long only
- Top 20% by momentum score
- Max 10 positions
- Trade AAPL, TSLA, NVDA, MSFT

Output:

Strategy Design Summary:

┌─────────────────────────────────────────┐
│ Strategy: Top-Momentum Long-Only        │
│ Market: US Equity (4 stocks)            │
│ Timeframe: Daily                        │
│ Backtest Period: 2020-01-01 to 2025-12-31 │
└─────────────────────────────────────────┘

Key Components:
1. Momentum ranking: ROC(close, period=20)
2. Selection: Top 20% by momentum score (max 10 stocks)
3. Position sizing: Equal weight (10% per position)
4. Hold period: 5 trading days
5. Rebalance: Daily at market open

Expected Performance (illustrative):
- Annualized Return: 18-25% (historical backtest)
- Sharpe Ratio: 0.8-1.2
- Max Drawdown: 30-40%
- Win Rate: 52-56%

Code generated at: strategy_momentum_us.py

Example 2: Performance Report