feat: 初始化量化交易知识库 v1.0
- 01_基础理论：量化交易基础概念、市场微观结构、加密货币特殊性 - 02_技术指标：完整指标体系（MA/EMA/MACD/RSI/KDJ/布林带/SuperTrend/DMI等） - 03_交易策略：趋势跟踪、均值回归、套利、动量策略详解 - 04_交易信号系统：多指标共振评分引擎（基于 tradehk 项目） - 05_市场品种：加密货币、XAUT黄金代币、代币化美股全览 - 06_数据流程：数据采集、清洗、存储、实时流处理 - 07_回测框架：回测方法论、偏差规避、绩效评估指标 - 08_风险管理：仓位管理、止损止盈、Kelly公式、杠杆管理 - 09_AI与机器学习：深度学习、强化学习、LLM在量化投资中的应用 - 10_链上数据分析：SOPR/MVRV/巨鲸监控/衍生品数据 - 11_参考文献：arXiv论文汇总、开源项目、数据平台资源 - samples/：Python信号计算器和回测样本代码 参考项目：tradehk（ssh://git@git.hk.hao.work:2222/hao/tradehk.git） 全部中文化，适用于加密货币（CEX/DEX）、XAUT黄金、代币化美股
这个提交包含在：
Manus Quant Agent
280
samples/backtest_sample.py
普通文件
280
samples/backtest_sample.py
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"""
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量化交易回测样本 - MACD + EWO 趋势跟踪策略
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基于 tradehk 项目的信号系统进行历史回测
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使用方法：
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pip install pandas numpy matplotlib requests
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python backtest_sample.py
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"""
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import pandas as pd
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import numpy as np
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import requests
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import json
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from datetime import datetime
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# ============================================================
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# 数据获取
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# ============================================================
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def fetch_binance_klines(symbol: str, interval: str, limit: int = 1000) -> pd.DataFrame:
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url = "https://api.binance.com/api/v3/klines"
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params = {"symbol": symbol, "interval": interval, "limit": limit}
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resp = requests.get(url, params=params, timeout=10)
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resp.raise_for_status()
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data = resp.json()
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df = pd.DataFrame(data, columns=[
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'timestamp', 'open', 'high', 'low', 'close', 'volume',
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'close_time', 'quote_volume', 'trades', 'taker_buy_base',
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'taker_buy_quote', 'ignore'
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])
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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for col in ['open', 'high', 'low', 'close', 'volume']:
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df[col] = df[col].astype(float)
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return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].set_index('timestamp')
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# ============================================================
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# 指标计算
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# ============================================================
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def calc_ema(s, n): return s.ewm(span=n, adjust=False).mean()
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def calc_sma(s, n): return s.rolling(n).mean()
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def calc_rma(s, n): return s.ewm(alpha=1/n, adjust=False).mean()
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def calc_macd(close, fast=10, slow=20, signal=10):
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macd = calc_ema(close, fast) - calc_ema(close, slow)
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sig = calc_ema(macd, signal)
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return macd, sig, macd - sig
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def calc_ewo(close):
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return calc_ema(close, 5) - calc_ema(close, 35)
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def calc_ao(df):
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mid = (df['high'] + df['low']) / 2
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return calc_sma(mid, 5) - calc_sma(mid, 34)
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def calc_rsi(close, period=14):
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delta = close.diff()
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gain = calc_rma(delta.clip(lower=0), period)
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loss = calc_rma((-delta).clip(lower=0), period)
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rs = gain / loss.replace(0, np.nan)
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return 100 - 100 / (1 + rs)
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def calc_atr(df, period=14):
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tr = pd.concat([
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df['high'] - df['low'],
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(df['high'] - df['close'].shift(1)).abs(),
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(df['low'] - df['close'].shift(1)).abs()
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], axis=1).max(axis=1)
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return calc_rma(tr, period)
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# ============================================================
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# 策略：MACD 金叉/死叉 + EWO 大方向过滤
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# ============================================================
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def generate_signals(df: pd.DataFrame) -> pd.DataFrame:
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"""
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策略逻辑：
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- 大方向：EWO > 0 看多，EWO < 0 看空
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- 入场：MACD 金叉（大方向看多时）或死叉（大方向看空时）
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- 止损：ATR 动态止损（2 × ATR）
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"""
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close = df['close']
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macd_line, signal_line, histogram = calc_macd(close)
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ewo = calc_ewo(close)
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ao = calc_ao(df)
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rsi = calc_rsi(close)
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atr = calc_atr(df)
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ma10 = calc_sma(close, 10)
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ma100 = calc_sma(close, 100)
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signals = pd.DataFrame(index=df.index)
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signals['close'] = close
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signals['macd'] = macd_line
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signals['signal'] = signal_line
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signals['histogram'] = histogram
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signals['ewo'] = ewo
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signals['ao'] = ao
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signals['rsi'] = rsi
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signals['atr'] = atr
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signals['ma10'] = ma10
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signals['ma100'] = ma100
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# MACD 金叉/死叉
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signals['macd_cross_up'] = (macd_line > signal_line) & (macd_line.shift(1) <= signal_line.shift(1))
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signals['macd_cross_down'] = (macd_line < signal_line) & (macd_line.shift(1) >= signal_line.shift(1))
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# 大方向过滤（EWO）
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signals['trend_bullish'] = ewo > 0
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signals['trend_bearish'] = ewo < 0
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# 最终信号
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signals['buy_signal'] = signals['macd_cross_up'] & signals['trend_bullish']
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signals['sell_signal'] = signals['macd_cross_down'] & signals['trend_bearish']
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return signals.dropna()
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# ============================================================
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# 回测引擎
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# ============================================================
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def backtest(df: pd.DataFrame, initial_capital: float = 10000, commission: float = 0.001) -> dict:
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"""
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简单回测引擎
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- 固定仓位：每次使用全部资金
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- 止损：2 × ATR
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- 止盈：4 × ATR（2:1 盈亏比）
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"""
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signals = generate_signals(df)
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capital = initial_capital
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position = 0 # 0=空仓, 1=多头
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entry_price = 0
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stop_loss = 0
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take_profit = 0
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trades = []
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equity_curve = [capital]
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for i in range(1, len(signals)):
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row = signals.iloc[i]
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prev_row = signals.iloc[i-1]
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# 检查止损/止盈
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if position == 1:
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if row['close'] <= stop_loss:
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# 止损出场
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pnl = (row['close'] - entry_price) * (capital / entry_price)
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pnl -= abs(pnl) * commission * 2
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capital += pnl
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trades.append({
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'type': '止损出场',
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'entry': entry_price,
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'exit': row['close'],
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'pnl': pnl,
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'pnl_pct': (row['close'] - entry_price) / entry_price * 100
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})
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position = 0
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elif row['close'] >= take_profit:
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# 止盈出场
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pnl = (row['close'] - entry_price) * (capital / entry_price)
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pnl -= abs(pnl) * commission * 2
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capital += pnl
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trades.append({
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'type': '止盈出场',
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'entry': entry_price,
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'exit': row['close'],
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'pnl': pnl,
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'pnl_pct': (row['close'] - entry_price) / entry_price * 100
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})
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position = 0
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elif row['sell_signal']:
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# 信号反转出场
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pnl = (row['close'] - entry_price) * (capital / entry_price)
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pnl -= abs(pnl) * commission * 2
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capital += pnl
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trades.append({
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'type': '信号出场',
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'entry': entry_price,
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'exit': row['close'],
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'pnl': pnl,
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'pnl_pct': (row['close'] - entry_price) / entry_price * 100
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})
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position = 0
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# 开仓
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if position == 0 and row['buy_signal']:
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entry_price = row['close']
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atr = row['atr']
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stop_loss = entry_price - 2 * atr
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take_profit = entry_price + 4 * atr
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position = 1
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capital -= capital * commission # 入场手续费
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# 记录净值
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if position == 1:
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unrealized = (row['close'] - entry_price) * (capital / entry_price)
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equity_curve.append(capital + unrealized)
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else:
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equity_curve.append(capital)
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# 计算绩效指标
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equity = pd.Series(equity_curve)
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returns = equity.pct_change().dropna()
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total_return = (equity.iloc[-1] / initial_capital - 1) * 100
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max_drawdown = ((equity.cummax() - equity) / equity.cummax()).max() * 100
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if returns.std() > 0:
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sharpe = returns.mean() / returns.std() * np.sqrt(365 * 24)
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else:
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sharpe = 0
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winning_trades = [t for t in trades if t['pnl'] > 0]
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losing_trades = [t for t in trades if t['pnl'] <= 0]
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win_rate = len(winning_trades) / len(trades) if trades else 0
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avg_win = np.mean([t['pnl_pct'] for t in winning_trades]) if winning_trades else 0
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avg_loss = np.mean([t['pnl_pct'] for t in losing_trades]) if losing_trades else 0
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profit_factor = abs(avg_win / avg_loss) if avg_loss != 0 else 0
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return {
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'初始资金': f"${initial_capital:,.2f}",
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'最终资金': f"${equity.iloc[-1]:,.2f}",
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'总收益率': f"{total_return:.2f}%",
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'最大回撤': f"{max_drawdown:.2f}%",
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'夏普比率': f"{sharpe:.2f}",
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'总交易次数': len(trades),
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'胜率': f"{win_rate:.2%}",
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'平均盈利': f"{avg_win:.2f}%",
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'平均亏损': f"{avg_loss:.2f}%",
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'盈亏比': f"{profit_factor:.2f}",
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'止损次数': len([t for t in trades if t['type'] == '止损出场']),
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'止盈次数': len([t for t in trades if t['type'] == '止盈出场']),
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'信号出场次数': len([t for t in trades if t['type'] == '信号出场']),
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}
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# ============================================================
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# 主程序
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# ============================================================
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if __name__ == '__main__':
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print("=" * 60)
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print("量化策略回测 - MACD + EWO 趋势跟踪")
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print("策略：MACD 金叉（EWO 多头时）做多，ATR 动态止损止盈")
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print("=" * 60)
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test_cases = [
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('BTCUSDT', '4h', 'BTC/USDT 4小时'),
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('ETHUSDT', '4h', 'ETH/USDT 4小时'),
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]
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for symbol, interval, label in test_cases:
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print(f"\n{'─' * 50}")
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print(f"回测品种：{label}")
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print(f"{'─' * 50}")
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try:
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df = fetch_binance_klines(symbol, interval, limit=1000)
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print(f"数据范围：{df.index[0].strftime('%Y-%m-%d')} 至 {df.index[-1].strftime('%Y-%m-%d')}")
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print(f"K 线数量：{len(df)}")
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results = backtest(df, initial_capital=10000)
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print("\n📊 回测结果：")
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for key, value in results.items():
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print(f" {key:12s}: {value}")
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except Exception as e:
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print(f"错误：{e}")
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print(f"\n{'=' * 60}")
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print("⚠️ 免责声明：以上回测结果仅供学习参考，不构成投资建议")
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print(" 历史表现不代表未来收益，实盘交易存在亏损风险")
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print("=" * 60)
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