{ "cells": [ { "cell_type": "markdown", "id": "fb3aacb0", "metadata": {}, "source": [ "# =============================================================================\n", "# Quantitative Trading — Strategy Development & Backtesting Demo\n", "# 量化交易 — 策略开发与回测演示\n", "\n", "# =============================================================================\n", "#\n", "# 本文件是数据管道 (quant_data_pipeline_demo.py) 的续集。\n", "# This file is the sequel to the data pipeline demo.\n", "#\n", "# Topics covered / 涵盖主题:\n", "# 1. Technical Indicators 技术指标 (MA, RSI, MACD, Bollinger Bands)\n", "# 2. Signal Generation 信号生成 (entry & exit rules)\n", "# 3. Two Demo Strategies 两个示范策略:\n", "# A. Dual Moving Average Crossover 双均线金叉死叉策略\n", "# B. RSI Mean Reversion RSI 均值回归策略\n", "# 4. Vectorized Backtest Engine 向量化回测引擎\n", "# 5. Performance Metrics 绩效指标\n", "# (Sharpe, Sortino, Max Drawdown, Win Rate …)\n", "# 6. Visualization 可视化\n", "#\n", "# Prerequisites / 前置条件:\n", "# pip install numpy pandas matplotlib scipy\n", "#\n", "# Running / 运行方式:\n", "# python quant_strategy_backtest_demo.py\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "156c36ec", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "from scipy import stats\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# 中文字体配置 / Chinese font config\n", "plt.rcParams['font.sans-serif'] = ['WenQuanYi Zen Hei', 'Arial Unicode MS', 'SimHei', 'DejaVu Sans']\n", "plt.rcParams['axes.unicode_minus'] = False\n", "\n", "np.random.seed(42)\n", "print(\"=\" * 70)\n", "print(\" 量化交易策略开发与回测演示\")\n", "print(\" Quantitative Trading: Strategy Development & Backtesting Demo\")\n", "print(\"=\" * 70)" ] }, { "cell_type": "markdown", "id": "62cbe290", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 0: Synthetic Price Data 合成价格数据\n", "\n", "# -----------------------------------------------------------------------------\n", "# We simulate a single stock using Geometric Brownian Motion (几何布朗运动),\n", "# the classical model that underlies the Black-Scholes formula.\n", "#\n", "# GBM formula:\n", "# dS = μ·S·dt + σ·S·dW\n", "#\n", "# Discrete form (what we actually compute each day):\n", "# S_t = S_{t-1} · exp( (μ - σ²/2)·dt + σ·√dt·ε )\n", "#\n", "# where:\n", "# μ = drift / 年化漂移率 (expected annual return)\n", "# σ = volatility / 年化波动率\n", "# dt = 1/252 (one trading day as a fraction of a year)\n", "# ε ~ N(0,1) (standard normal random shock / 标准正态随机扰动)\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "04ed6429", "metadata": {}, "outputs": [], "source": [ "def generate_price_series(\n", " n_days: int = 1500,\n", " mu: float = 0.10, # 年化预期收益率 / annual expected return\n", " sigma: float = 0.25, # 年化波动率 / annual volatility\n", " s0: float = 100.0, # 初始价格 / initial price\n", " seed: int = 42,\n", ") -> pd.Series:\n", " \"\"\"\n", " Generate a synthetic daily price series via GBM.\n", " 用几何布朗运动生成合成日线价格序列。\n", " \"\"\"\n", " np.random.seed(seed)\n", " dt = 1.0 / 252 # 每个交易日占一年的比例\n", " epsilon = np.random.randn(n_days) # 每日随机冲击\n", " log_returns = (mu - 0.5 * sigma ** 2) * dt + sigma * np.sqrt(dt) * epsilon\n", " prices = s0 * np.exp(np.cumsum(log_returns)) # 累积乘积 → 价格路径\n", "\n", " # 生成工作日日期序列 / generate business-day date index\n", " dates = pd.bdate_range(start=\"2019-01-02\", periods=n_days)\n", " return pd.Series(prices, index=dates, name=\"close\")\n", "\n", "\n", "price = generate_price_series()\n", "print(f\"\\n[数据] 生成模拟股票价格: {len(price)} 个交易日\")\n", "print(f\" 价格区间: {price.min():.2f} ~ {price.max():.2f}\")" ] }, { "cell_type": "markdown", "id": "ccda8a1f", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 1: Technical Indicators 技术指标\n", "\n", "# -----------------------------------------------------------------------------\n", "# Technical indicators transform raw price/volume data into signals.\n", "# 技术指标将原始价格/成交量数据转化为交易信号。\n", "#\n", "# They are divided into two broad families:\n", "# 主要分为两大类:\n", "#\n", "# ① Trend-following indicators 趋势跟随指标\n", "# → Moving Averages (MA), MACD\n", "# → Work well in trending markets (趋势市中效果好)\n", "#\n", "# ② Oscillators / Mean-reversion indicators 震荡/均值回归指标\n", "# → RSI, Bollinger Bands\n", "# → Work well in range-bound / choppy markets (震荡市中效果好)\n", "\n", "# =============================================================================\n", "\n", "# ── 1-A Simple Moving Average 简单移动平均线 (SMA) ──────────────────────────\n", "#\n", "# SMA_n(t) = (P_{t} + P_{t-1} + … + P_{t-n+1}) / n\n", "#\n", "# The SMA smooths out daily noise to reveal the underlying trend.\n", "# SMA 平滑日内噪音，揭示潜在趋势。\n", "# A longer window → smoother, but lags more behind recent price action.\n", "# 窗口越长 → 越平滑，但对价格变化的反应越滞后。" ] }, { "cell_type": "code", "execution_count": null, "id": "8ec5acb4", "metadata": {}, "outputs": [], "source": [ "def sma(prices: pd.Series, window: int) -> pd.Series:\n", " \"\"\"Simple Moving Average / 简单移动平均线\"\"\"\n", " return prices.rolling(window=window).mean()\n", "\n", "\n", "# ── 1-B Exponential Moving Average 指数移动平均线 (EMA) ───────────────────\n", "#\n", "# EMA gives MORE weight to recent prices (recent data matters more).\n", "# EMA 给予近期价格更高权重（近期数据更重要）。\n", "#\n", "# EMA_t = α · P_t + (1 - α) · EMA_{t-1}\n", "# where α = 2 / (n + 1) (smoothing factor / 平滑因子)\n", "#\n", "# EMA reacts faster than SMA to price changes.\n", "# EMA 对价格变动的反应比 SMA 更灵敏。\n", "\n", "def ema(prices: pd.Series, span: int) -> pd.Series:\n", " \"\"\"Exponential Moving Average / 指数移动平均线\"\"\"\n", " return prices.ewm(span=span, adjust=False).mean()\n", "\n", "\n", "# ── 1-C RSI 相对强弱指数 (Relative Strength Index) ─────────────────────────\n", "#\n", "# RSI measures the speed and magnitude of recent price changes.\n", "# RSI 衡量近期价格变动的速度和幅度。\n", "#\n", "# Formula:\n", "# RS = average_gain / average_loss (over last n days)\n", "# RSI = 100 - 100 / (1 + RS)\n", "#\n", "# Interpretation / 指标解读:\n", "# RSI > 70 → Overbought 超买 (price may be due for a pullback / 价格可能回调)\n", "# RSI < 30 → Oversold 超卖 (price may be due for a bounce / 价格可能反弹)\n", "# RSI = 50 → Neutral 中性\n", "\n", "def rsi(prices: pd.Series, window: int = 14) -> pd.Series:\n", " \"\"\"\n", " Compute Wilder's RSI.\n", " 计算 Wilder 平滑法 RSI。\n", " \"\"\"\n", " delta = prices.diff() # 每日价格变化 / daily price change\n", " gain = delta.clip(lower=0) # 只保留上涨部分 / keep only up-days\n", " loss = -delta.clip(upper=0) # 只保留下跌部分 / keep only down-days\n", "\n", " # Wilder uses EMA with span = 2*n - 1 (equivalent to 1/n smoothing)\n", " avg_gain = gain.ewm(alpha=1.0 / window, adjust=False).mean()\n", " avg_loss = loss.ewm(alpha=1.0 / window, adjust=False).mean()\n", "\n", " rs = avg_gain / avg_loss # 相对强弱值 / relative strength\n", " return 100 - (100 / (1 + rs)) # 转换为 0~100 范围\n", "\n", "\n", "# ── 1-D MACD 指数平滑异同移动平均线 ────────────────────────────────────────\n", "#\n", "# MACD reveals the relationship between two EMAs.\n", "# MACD 揭示两条 EMA 之间的关系。\n", "#\n", "# Components / 构成:\n", "# MACD Line MACD线 = EMA(12) - EMA(26) (fast minus slow / 快线减慢线)\n", "# Signal Line 信号线 = EMA(9) of MACD Line (trigger line / 触发线)\n", "# Histogram 柱状图 = MACD Line - Signal Line\n", "#\n", "# Trading rules / 交易规则:\n", "# MACD crosses above Signal → Bullish (金叉, buy signal / 买入信号)\n", "# MACD crosses below Signal → Bearish (死叉, sell signal / 卖出信号)\n", "\n", "def macd(prices: pd.Series,\n", " fast: int = 12, slow: int = 26, signal: int = 9\n", " ) -> pd.DataFrame:\n", " \"\"\"\n", " Compute MACD, Signal line, and Histogram.\n", " 计算 MACD线、信号线和柱状图。\n", " \"\"\"\n", " ema_fast = ema(prices, fast)\n", " ema_slow = ema(prices, slow)\n", " macd_line = ema_fast - ema_slow # MACD 线\n", " signal_line = ema(macd_line, signal) # 信号线 (DIF的EMA)\n", " histogram = macd_line - signal_line # 柱状图 (MACD Bar)\n", " return pd.DataFrame({\n", " \"macd\": macd_line,\n", " \"signal\": signal_line,\n", " \"histogram\": histogram,\n", " })\n", "\n", "\n", "# ── 1-E Bollinger Bands 布林带 ─────────────────────────────────────────────\n", "#\n", "# Bollinger Bands place upper/lower envelopes around a moving average.\n", "# 布林带在移动平均线上下各画一条\"包络线\"。\n", "#\n", "# Formula:\n", "# Middle Band 中轨 = SMA(n)\n", "# Upper Band 上轨 = SMA(n) + k·σ_n (k = 2 by default / 默认 k=2)\n", "# Lower Band 下轨 = SMA(n) - k·σ_n\n", "#\n", "# where σ_n is the rolling standard deviation / 滚动标准差\n", "#\n", "# When price touches the lower band → oversold area (超卖区域)\n", "# When price touches the upper band → overbought area (超买区域)\n", "# Band width (带宽) contracts before explosive moves (波动收窄常预示突破)\n", "\n", "def bollinger_bands(prices: pd.Series, window: int = 20, k: float = 2.0\n", " ) -> pd.DataFrame:\n", " \"\"\"\n", " Compute Bollinger Bands.\n", " 计算布林带（上轨、中轨、下轨）。\n", " \"\"\"\n", " mid = sma(prices, window) # 中轨 (SMA)\n", " std = prices.rolling(window).std() # 滚动标准差\n", " upper = mid + k * std # 上轨\n", " lower = mid - k * std # 下轨\n", " # %B indicator: where is the current price within the band?\n", " # %B 指标：当前价格在带宽中的位置 (0=下轨, 1=上轨)\n", " pct_b = (prices - lower) / (upper - lower)\n", " return pd.DataFrame({\n", " \"upper\": upper, \"mid\": mid, \"lower\": lower, \"pct_b\": pct_b\n", " })\n", "\n", "\n", "# Compute all indicators on our simulated price series\n", "# 对模拟价格序列计算所有指标\n", "sma20 = sma(price, 20) # 20日均线 / 20-day SMA\n", "sma60 = sma(price, 60) # 60日均线 / 60-day SMA (longer trend)\n", "rsi14 = rsi(price, 14) # 14日RSI / 14-day RSI\n", "macd_df = macd(price) # MACD (12/26/9)\n", "bb = bollinger_bands(price, window=20, k=2.0)\n", "\n", "print(\"\\n[指标] 技术指标计算完成:\")\n", "print(f\" SMA20 — 首个有效值日期: {sma20.first_valid_index().date()}\")\n", "print(f\" SMA60 — 首个有效值日期: {sma60.first_valid_index().date()}\")\n", "print(f\" RSI14 — 首个有效值日期: {rsi14.first_valid_index().date()}\")\n", "print(f\" MACD — 首个有效值日期: {macd_df['macd'].first_valid_index().date()}\")\n", "print(f\" BollingerBands — 首个有效值日期: {bb['mid'].first_valid_index().date()}\")" ] }, { "cell_type": "markdown", "id": "739084bb", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 2: Strategy A — Dual Moving Average Crossover\n", "# 策略 A — 双均线金叉/死叉策略\n", "\n", "# -----------------------------------------------------------------------------\n", "# One of the oldest and most intuitive trend-following strategies.\n", "# 最古老也最直观的趋势跟随策略之一。\n", "#\n", "# Logic / 逻辑:\n", "# Golden Cross (金叉): short MA crosses ABOVE long MA → BUY (做多)\n", "# Death Cross (死叉): short MA crosses BELOW long MA → SELL (平仓)\n", "#\n", "# Rationale / 原理:\n", "# When the short-term average rises above the long-term average, it signals\n", "# that recent momentum is stronger than the historical trend → bullish.\n", "# 短期均线上穿长期均线，意味着近期动能强于历史趋势 → 看涨。\n", "#\n", "# Parameters / 参数:\n", "# SHORT_WINDOW = 20 (fast line / 快线)\n", "# LONG_WINDOW = 60 (slow line / 慢线)\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "a99d5899", "metadata": {}, "outputs": [], "source": [ "SHORT_WIN = 20 # 短期均线窗口 / short-term MA window\n", "LONG_WIN = 60 # 长期均线窗口 / long-term MA window\n", "\n", "ma_short = sma(price, SHORT_WIN)\n", "ma_long = sma(price, LONG_WIN)\n", "\n", "# ── Signal generation 信号生成 ───────────────────────────────────────────────\n", "#\n", "# Signal (信号) = +1 when we should be LONG (持多仓), 0 when out of market (空仓)\n", "#\n", "# Step 1: raw_signal = 1 whenever short MA > long MA (short MA above long MA)\n", "# Step 2: detect crossovers (cross = today's signal ≠ yesterday's signal)\n", "#\n", "# We use a \"position\" approach — hold the position until it reverses.\n", "# 使用\"持仓\"方式 — 持有直到信号翻转。\n", "\n", "# raw_signal: 1 = short above long (看多区域), 0 = short below long (看空区域)\n", "raw_signal = (ma_short > ma_long).astype(int)\n", "\n", "# Align signals: use yesterday's signal to trade today (avoid lookahead bias)\n", "# 用昨天的信号决定今天的仓位，避免\"未来数据偷窥\" (前视偏差 / lookahead bias)\n", "ma_signal = raw_signal.shift(1).fillna(0)\n", "\n", "print(\"\\n[策略A] 双均线信号生成完成\")\n", "print(f\" 多头持仓天数 (Signal=1): {int(ma_signal.sum())} 天\")\n", "print(f\" 空仓天数 (Signal=0): {int((ma_signal == 0).sum())} 天\")" ] }, { "cell_type": "markdown", "id": "dd6312ac", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 3: Strategy B — RSI Mean Reversion\n", "# 策略 B — RSI 均值回归策略\n", "\n", "# -----------------------------------------------------------------------------\n", "# This is a contrarian strategy: buy when the market seems \"too weak\",\n", "# sell when it seems \"too strong\".\n", "# 这是一个逆势策略：市场\"跌过头\"时买入，\"涨过头\"时卖出。\n", "#\n", "# Logic / 逻辑:\n", "# RSI drops below oversold level (超卖线, default 30) → BUY signal\n", "# RSI rises above overbought level (超买线, default 70) → SELL signal\n", "#\n", "# This exploits mean reversion (均值回归): extreme prices tend to revert.\n", "# 利用均值回归特性：极端价格倾向于回归均值。\n", "#\n", "# Risk / 风险:\n", "# In a strong trend, RSI can stay oversold/overbought for long stretches.\n", "# 在强趋势中，RSI 可以长时间停留在超卖/超买区域，造成连续亏损。\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "c428d624", "metadata": {}, "outputs": [], "source": [ "RSI_OVERSOLD = 30 # 超卖线 / oversold threshold\n", "RSI_OVERBOUGHT = 70 # 超买线 / overbought threshold\n", "\n", "def rsi_signal(rsi_series: pd.Series,\n", " oversold: float = 30,\n", " overbought: float = 70) -> pd.Series:\n", " \"\"\"\n", " Generate long/short/flat signals from RSI.\n", " 根据 RSI 生成多空平信号。\n", "\n", " Returns a Series of:\n", " +1 → Long (做多)\n", " -1 → Short (做空)\n", " 0 → Flat (空仓, no position)\n", " \"\"\"\n", " position = pd.Series(0, index=rsi_series.index, dtype=float)\n", " current_pos = 0 # 当前持仓状态 / current position state\n", "\n", " for i in range(1, len(rsi_series)):\n", " r = rsi_series.iloc[i]\n", " if pd.isna(r):\n", " position.iloc[i] = 0\n", " continue\n", "\n", " # Entry rules / 入场规则\n", " if r < oversold and current_pos == 0:\n", " current_pos = 1 # 超卖 → 做多 / oversold → go long\n", "\n", " elif r > overbought and current_pos == 0:\n", " current_pos = -1 # 超买 → 做空 / overbought → go short\n", "\n", " # Exit rules / 出场规则\n", " # Exit long when RSI recovers above 50 (回到中性区域 / back to neutral)\n", " elif current_pos == 1 and r > 50:\n", " current_pos = 0\n", "\n", " # Exit short when RSI falls below 50\n", " elif current_pos == -1 and r < 50:\n", " current_pos = 0\n", "\n", " position.iloc[i] = current_pos\n", "\n", " return position\n", "\n", "\n", "rsi_pos = rsi_signal(rsi14, RSI_OVERSOLD, RSI_OVERBOUGHT)\n", "\n", "# Shift by 1 day to avoid lookahead bias / 前移一天避免前视偏差\n", "rsi_signal_shifted = rsi_pos.shift(1).fillna(0)\n", "\n", "print(\"\\n[策略B] RSI信号生成完成\")\n", "print(f\" 多头持仓天数 (Signal=+1): {int((rsi_signal_shifted == 1).sum())} 天\")\n", "print(f\" 空头持仓天数 (Signal=-1): {int((rsi_signal_shifted == -1).sum())} 天\")\n", "print(f\" 空仓天数 (Signal= 0): {int((rsi_signal_shifted == 0).sum())} 天\")" ] }, { "cell_type": "markdown", "id": "3bb7e0d4", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 4: Vectorized Backtest Engine 向量化回测引擎\n", "\n", "# -----------------------------------------------------------------------------\n", "# A backtest (回测) simulates how a strategy would have performed\n", "# on historical data. It is the primary tool for validating a strategy\n", "# before risking real money.\n", "# 回测是在历史数据上模拟策略表现的工具，是真实投资前验证策略的主要手段。\n", "#\n", "# Two main backtest styles / 两种主要回测方式:\n", "#\n", "# ① Vectorized backtest 向量化回测\n", "# - Compute all positions & P&L as array operations at once (numpy/pandas)\n", "# - Very fast; good for strategy exploration\n", "# - 所有仓位和盈亏一次性用数组运算计算，速度极快，适合策略探索\n", "#\n", "# ② Event-driven backtest 事件驱动回测\n", "# - Simulate time step-by-step, reacting to each market event\n", "# - More realistic (handles fills, slippage, latency, order queuing)\n", "# - 逐笔模拟市场事件，更真实（考虑成交、滑点、延迟等），速度较慢\n", "#\n", "# We use the vectorized approach here for clarity and speed.\n", "# 此处使用向量化方式，兼顾清晰度和速度。\n", "#\n", "# Cost model 交易成本模型:\n", "# - Commission (佣金): charged each time you trade (per trade)\n", "# - Slippage (滑点): the difference between the expected fill price and\n", "# the actual fill price (price moves against you)\n", "# We approximate both as a percentage of the trade value.\n", "# 两者合并近似为交易金额的固定比例。\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "1d6b6a55", "metadata": {}, "outputs": [], "source": [ "class VectorizedBacktester:\n", " \"\"\"\n", " A simple vectorized backtesting engine.\n", " 简单的向量化回测引擎。\n", "\n", " Assumptions / 假设:\n", " • Long-only or long/short positions\n", " • Trade at next-day's open (用下一天开盘价成交) — conservative assumption\n", " We approximate this by using the same day's close shifted by 1 day.\n", " • Round-trip cost (单次交易成本) = 2 × cost_per_trade\n", " (pay cost on entry AND exit / 进出各收一次)\n", " • No leverage (无杠杆), position size is 100% of capital when in trade\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " prices: pd.Series,\n", " signal: pd.Series,\n", " cost_per_trade: float = 0.001, # 0.1% one-way / 单向 0.1% (含佣金+滑点)\n", " initial_capital: float = 1_000_000.0, # 初始资金 / initial capital\n", " name: str = \"Strategy\",\n", " ):\n", " self.prices = prices\n", " self.signal = signal.reindex(prices.index).fillna(0)\n", " self.cost_per_trade = cost_per_trade\n", " self.initial_capital = initial_capital\n", " self.name = name\n", " self._run()\n", "\n", " def _run(self):\n", " \"\"\"Core backtesting logic. 核心回测逻辑。\"\"\"\n", " prices = self.prices\n", " signal = self.signal\n", "\n", " # ── Daily price return 日收益率 ────────────────────────────────────\n", " daily_ret = prices.pct_change().fillna(0)\n", "\n", " # ── Strategy return (before costs) 策略日收益率（扣除成本前）─────────\n", " # Strategy return = signal × market return\n", " # 策略当日收益率 = 持仓方向 × 市场当日收益率\n", " strat_ret_gross = signal * daily_ret\n", "\n", " # ── Transaction cost 交易成本 ──────────────────────────────────────\n", " # Detect position changes (signal changes from one day to the next)\n", " # 检测仓位变化（信号从一天到下一天发生变化）\n", " position_change = signal.diff().fillna(0).abs() # >0 means we traded\n", " # Cost is charged each time position changes\n", " # 每次仓位变化时扣除成本\n", " cost = position_change * self.cost_per_trade\n", "\n", " # ── Net strategy return 策略净收益率 ───────────────────────────────\n", " strat_ret_net = strat_ret_gross - cost\n", "\n", " # ── Equity curve 净值曲线 ───────────────────────────────────────────\n", " # The equity curve tracks how 1 unit of capital grows over time.\n", " # 净值曲线追踪单位资本随时间的增长。\n", " # (1 + daily_net_return) compounded every day\n", " equity = self.initial_capital * (1 + strat_ret_net).cumprod()\n", " equity_bh = self.initial_capital * (1 + daily_ret).cumprod() # Buy & Hold benchmark\n", "\n", " # ── Drawdown 回撤 ──────────────────────────────────────────────────\n", " # Drawdown measures how far we are from the peak at any point in time.\n", " # 回撤衡量当前净值距离历史最高点的跌幅。\n", " rolling_max = equity.cummax()\n", " drawdown = (equity - rolling_max) / rolling_max # always <= 0\n", "\n", " # Store results for later analysis\n", " self.daily_ret = daily_ret\n", " self.strat_ret = strat_ret_net\n", " self.equity = equity\n", " self.equity_bh = equity_bh\n", " self.drawdown = drawdown\n", " self.n_trades = int((position_change > 0).sum())\n", " self.total_cost = cost.sum()\n", "\n", " # ── Performance metrics 绩效指标 ──────────────────────────────────────────\n", " #\n", " # A well-rounded strategy evaluation uses multiple metrics, because\n", " # no single number captures the full picture.\n", " # 全面的策略评估需要多个指标，因为单一数字无法描述全貌。\n", " #\n", " # Key metrics / 关键指标:\n", " # Total Return 总收益率 — how much did we make in total?\n", " # CAGR 年化复合增长率 — annualized compounded growth rate\n", " # Sharpe Ratio 夏普比率 — return per unit of total risk (risk-adjusted)\n", " # Sortino Ratio 索提诺比率 — return per unit of DOWNSIDE risk only\n", " # Max Drawdown 最大回撤 — worst peak-to-trough decline\n", " # Calmar Ratio 卡玛比率 — CAGR / Max Drawdown (reward vs worst loss)\n", " # Win Rate 胜率 — fraction of days (or trades) with positive P&L\n", " # Profit Factor 盈亏比 — total profit / total loss\n", "\n", " def metrics(self) -> dict:\n", " \"\"\"Compute and return a dictionary of performance metrics.\n", " 计算并返回绩效指标字典。\"\"\"\n", " r = self.strat_ret\n", " eq = self.equity\n", " n = len(r)\n", " years = n / 252.0 # approximate years in sample / 样本年数估算\n", "\n", " # Total return / 总收益率\n", " total_return = (eq.iloc[-1] / self.initial_capital) - 1\n", "\n", " # CAGR 年化复合增长率\n", " # CAGR = (EndValue / StartValue)^(1/years) - 1\n", " cagr = (1 + total_return) ** (1 / years) - 1\n", "\n", " # Annualized volatility 年化波动率\n", " ann_vol = r.std() * np.sqrt(252)\n", "\n", " # Sharpe Ratio 夏普比率\n", " # Sharpe = (Mean excess return) / StdDev(return) × √252\n", " # Excess return = strategy return - risk-free rate\n", " # 超额收益率 = 策略收益率 - 无风险利率\n", " # We use 0 as risk-free rate for simplicity (or assume it's netted out)\n", " risk_free = 0.0\n", " sharpe = (r.mean() - risk_free / 252) / r.std() * np.sqrt(252) if r.std() > 0 else 0\n", "\n", " # Sortino Ratio 索提诺比率\n", " # Like Sharpe but only penalizes DOWNSIDE volatility\n", " # 类似夏普，但只惩罚下行波动率（亏损波动率）\n", " downside = r[r < 0]\n", " downside_std = downside.std() * np.sqrt(252) if len(downside) > 0 else 1e-9\n", " sortino = (cagr - risk_free) / downside_std if downside_std > 0 else 0\n", "\n", " # Maximum Drawdown 最大回撤\n", " max_dd = self.drawdown.min() # most negative value (最大负值)\n", "\n", " # Calmar Ratio 卡玛比率\n", " # Calmar = CAGR / |Max Drawdown|\n", " calmar = cagr / abs(max_dd) if max_dd != 0 else 0\n", "\n", " # Win rate 胜率 (fraction of trading days with positive return)\n", " win_rate = (r > 0).mean()\n", "\n", " # Profit factor 盈亏比\n", " # = Sum of positive returns / |Sum of negative returns|\n", " gross_profit = r[r > 0].sum()\n", " gross_loss = abs(r[r < 0].sum())\n", " profit_factor = gross_profit / gross_loss if gross_loss > 0 else np.inf\n", "\n", " return {\n", " \"总收益率 Total Return\": f\"{total_return:.2%}\",\n", " \"年化收益率 CAGR\": f\"{cagr:.2%}\",\n", " \"年化波动率 Ann. Volatility\": f\"{ann_vol:.2%}\",\n", " \"夏普比率 Sharpe Ratio\": f\"{sharpe:.3f}\",\n", " \"索提诺比率 Sortino Ratio\": f\"{sortino:.3f}\",\n", " \"最大回撤 Max Drawdown\": f\"{max_dd:.2%}\",\n", " \"卡玛比率 Calmar Ratio\": f\"{calmar:.3f}\",\n", " \"胜率 Win Rate\": f\"{win_rate:.2%}\",\n", " \"盈亏比 Profit Factor\": f\"{profit_factor:.3f}\",\n", " \"交易次数 # Trades\": str(self.n_trades),\n", " \"总成本 Total Cost\": f\"{self.total_cost:.4%}\",\n", " }\n", "\n", " def print_metrics(self):\n", " \"\"\"Pretty-print the performance report. 格式化打印绩效报告。\"\"\"\n", " print(f\"\\n{'=' * 55}\")\n", " print(f\" 策略绩效报告 / Performance Report: {self.name}\")\n", " print(f\"{'=' * 55}\")\n", " for k, v in self.metrics().items():\n", " print(f\" {k:<35} {v}\")\n", " print(f\"{'=' * 55}\")" ] }, { "cell_type": "markdown", "id": "ea411c63", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 5: Run Backtests 执行回测\n", "\n", "# =============================================================================\n", "\n", "# ── Strategy A: MA Crossover 双均线策略 ──────────────────────────────────────" ] }, { "cell_type": "code", "execution_count": null, "id": "e13a487c", "metadata": {}, "outputs": [], "source": [ "bt_ma = VectorizedBacktester(\n", " prices=price,\n", " signal=ma_signal, # +1 = long, 0 = flat\n", " cost_per_trade=0.001, # 0.1% per trade (reasonable for liquid stocks)\n", " name=\"双均线策略 (MA Crossover 20/60)\",\n", ")\n", "\n", "# ── Strategy B: RSI Mean Reversion RSI均值回归策略 ──────────────────────────\n", "bt_rsi = VectorizedBacktester(\n", " prices=price,\n", " signal=rsi_signal_shifted, # +1 = long, -1 = short, 0 = flat\n", " cost_per_trade=0.001,\n", " name=\"RSI均值回归策略 (RSI Mean Reversion 14)\",\n", ")\n", "\n", "# ── Benchmark: Buy & Hold 基准：买入并持有 ───────────────────────────────────\n", "# Buy & Hold (买入持有) is always our benchmark: simply hold the asset forever.\n", "# It requires zero skill and zero effort — any strategy must beat this to\n", "# justify the extra complexity and transaction costs.\n", "# 买入持有是永远的基准策略：无需技能、零成本。任何策略都必须超越它才有意义。\n", "bt_bh = VectorizedBacktester(\n", " prices=price,\n", " signal=pd.Series(1, index=price.index, dtype=float), # always long / 始终做多\n", " cost_per_trade=0.0, # no trading costs / 无交易成本\n", " name=\"Buy & Hold 基准 (买入持有)\",\n", ")\n", "\n", "bt_ma.print_metrics()\n", "bt_rsi.print_metrics()\n", "bt_bh.print_metrics()" ] }, { "cell_type": "markdown", "id": "9d601da1", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 6: Visualization 可视化\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "e6b6bfc8", "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(16, 22))\n", "gs = gridspec.GridSpec(6, 2, figure=fig, hspace=0.45, wspace=0.3)\n", "\n", "# ── Plot 1: Price + MA signals 价格 + 均线信号 ────────────────────────────────\n", "ax1 = fig.add_subplot(gs[0, :]) # span full width\n", "ax1.plot(price, color=\"#1f77b4\", linewidth=1, label=\"价格 Price\")\n", "ax1.plot(ma_short, color=\"orange\", linewidth=1.2, label=f\"SMA{SHORT_WIN} (快线)\")\n", "ax1.plot(ma_long, color=\"red\", linewidth=1.2, label=f\"SMA{LONG_WIN} (慢线)\")\n", "\n", "# Shade long periods (持多仓的区间着色)\n", "ax1.fill_between(\n", " price.index, price.min(), price.max(),\n", " where=(ma_signal == 1).values,\n", " alpha=0.12, color=\"green\", label=\"多头持仓区间 Long Period\"\n", ")\n", "ax1.set_title(\"策略A — 双均线信号 (MA Crossover Signals)\", fontsize=13, fontweight=\"bold\")\n", "ax1.legend(loc=\"upper left\", fontsize=8)\n", "ax1.set_ylabel(\"价格 Price\")\n", "ax1.grid(alpha=0.3)\n", "\n", "# ── Plot 2: RSI RSI指标 ────────────────────────────────────────────────────\n", "ax2 = fig.add_subplot(gs[1, :])\n", "ax2.plot(rsi14, color=\"purple\", linewidth=1)\n", "ax2.axhline(RSI_OVERBOUGHT, color=\"red\", linestyle=\"--\", linewidth=1, label=f\"超买线 {RSI_OVERBOUGHT}\")\n", "ax2.axhline(RSI_OVERSOLD, color=\"green\", linestyle=\"--\", linewidth=1, label=f\"超卖线 {RSI_OVERSOLD}\")\n", "ax2.axhline(50, color=\"gray\", linestyle=\":\", linewidth=0.8)\n", "ax2.fill_between(rsi14.index, RSI_OVERSOLD, rsi14,\n", " where=(rsi14 < RSI_OVERSOLD), alpha=0.25, color=\"green\",\n", " label=\"超卖区域 Oversold\")\n", "ax2.fill_between(rsi14.index, rsi14, RSI_OVERBOUGHT,\n", " where=(rsi14 > RSI_OVERBOUGHT), alpha=0.25, color=\"red\",\n", " label=\"超买区域 Overbought\")\n", "ax2.set_ylim(0, 100)\n", "ax2.set_title(f\"策略B指标 — RSI({14}) 均值回归信号 (RSI Mean Reversion)\", fontsize=13, fontweight=\"bold\")\n", "ax2.set_ylabel(\"RSI\")\n", "ax2.legend(loc=\"upper left\", fontsize=8, ncol=2)\n", "ax2.grid(alpha=0.3)\n", "\n", "# ── Plot 3: Bollinger Bands 布林带 ────────────────────────────────────────────\n", "ax3 = fig.add_subplot(gs[2, :])\n", "ax3.plot(price, color=\"#1f77b4\", linewidth=1, label=\"价格 Price\")\n", "ax3.plot(bb[\"mid\"], color=\"orange\", linewidth=1.2, label=\"中轨 Middle (SMA20)\")\n", "ax3.plot(bb[\"upper\"],color=\"red\", linewidth=1, linestyle=\"--\", label=\"上轨 Upper (+2σ)\")\n", "ax3.plot(bb[\"lower\"],color=\"green\", linewidth=1, linestyle=\"--\", label=\"下轨 Lower (-2σ)\")\n", "ax3.fill_between(price.index, bb[\"upper\"], bb[\"lower\"], alpha=0.07, color=\"blue\")\n", "ax3.set_title(\"布林带 (Bollinger Bands 20, 2σ)\", fontsize=13, fontweight=\"bold\")\n", "ax3.set_ylabel(\"价格 Price\")\n", "ax3.legend(loc=\"upper left\", fontsize=8, ncol=2)\n", "ax3.grid(alpha=0.3)\n", "\n", "# ── Plot 4: MACD MACD指标 ────────────────────────────────────────────────────\n", "ax4 = fig.add_subplot(gs[3, :])\n", "ax4.plot(macd_df[\"macd\"], color=\"blue\", linewidth=1, label=\"MACD线 (DIF)\")\n", "ax4.plot(macd_df[\"signal\"], color=\"orange\", linewidth=1, label=\"信号线 (DEA)\")\n", "colors = [\"green\" if v >= 0 else \"red\" for v in macd_df[\"histogram\"]]\n", "ax4.bar(macd_df.index, macd_df[\"histogram\"], color=colors, alpha=0.5, width=1, label=\"柱状图 Histogram\")\n", "ax4.axhline(0, color=\"black\", linewidth=0.8)\n", "ax4.set_title(\"MACD (12/26/9) — 趋势确认指标 (Trend Confirmation)\", fontsize=13, fontweight=\"bold\")\n", "ax4.set_ylabel(\"MACD\")\n", "ax4.legend(loc=\"upper left\", fontsize=8)\n", "ax4.grid(alpha=0.3)\n", "\n", "# ── Plot 5: Equity Curves 净值曲线 ────────────────────────────────────────────\n", "ax5 = fig.add_subplot(gs[4, :])\n", "ax5.plot(bt_ma.equity, color=\"blue\", linewidth=1.5, label=\"策略A: 双均线 MA Crossover\")\n", "ax5.plot(bt_rsi.equity, color=\"purple\", linewidth=1.5, label=\"策略B: RSI 均值回归 RSI Reversion\")\n", "ax5.plot(bt_bh.equity, color=\"gray\", linewidth=1.2, linestyle=\"--\", label=\"基准: 买入持有 Buy & Hold\")\n", "ax5.set_title(\"净值曲线对比 (Equity Curve Comparison)\", fontsize=13, fontweight=\"bold\")\n", "ax5.set_ylabel(\"账户价值 Portfolio Value (元)\")\n", "ax5.legend(loc=\"upper left\", fontsize=9)\n", "ax5.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"¥{x/1e4:.0f}万\"))\n", "ax5.grid(alpha=0.3)\n", "\n", "# ── Plot 6: Drawdown 回撤曲线 ────────────────────────────────────────────────\n", "ax6 = fig.add_subplot(gs[5, :])\n", "ax6.fill_between(bt_ma.drawdown.index, bt_ma.drawdown, 0, alpha=0.5, color=\"blue\", label=\"策略A\")\n", "ax6.fill_between(bt_rsi.drawdown.index, bt_rsi.drawdown, 0, alpha=0.5, color=\"purple\", label=\"策略B\")\n", "ax6.fill_between(bt_bh.drawdown.index, bt_bh.drawdown, 0, alpha=0.3, color=\"gray\", label=\"Buy & Hold\")\n", "ax6.set_title(\"回撤曲线 (Drawdown Curves)\", fontsize=13, fontweight=\"bold\")\n", "ax6.set_ylabel(\"回撤幅度 Drawdown\")\n", "ax6.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x:.0%}\"))\n", "ax6.legend(loc=\"lower left\", fontsize=9)\n", "ax6.grid(alpha=0.3)\n", "\n", "plt.suptitle(\n", " \"量化交易策略开发与回测演示\\nQuantitative Trading: Strategy Development & Backtesting\",\n", " fontsize=15, fontweight=\"bold\", y=1.005,\n", ")\n", "plt.savefig(\"strategy_backtest_demo.png\", dpi=120, bbox_inches=\"tight\")\n", "plt.show()\n", "print(\"\\n[图表] 已保存至 strategy_backtest_demo.png\")" ] }, { "cell_type": "markdown", "id": "ff8384a5", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 7: Walk-Forward Validation 滚动前向验证\n", "\n", "# -----------------------------------------------------------------------------\n", "# A critical warning for all new quant traders / 对所有量化新手的重要警告:\n", "#\n", "# In-sample overfitting (样本内过拟合) is the #1 trap in backtesting.\n", "# 样本内过拟合是回测中最大的陷阱。\n", "#\n", "# If you test 100 different parameter sets on the same data and pick the best,\n", "# that \"best\" result will almost certainly NOT hold out of sample.\n", "# 如果在同一份数据上测试100组参数并选最好的，这个\"最优\"结果在样本外几乎必然失效。\n", "# This is called data snooping bias / 数据窥探偏差 or p-hacking.\n", "#\n", "# Walk-Forward Validation (滚动前向验证) helps guard against this:\n", "# ┌──────────────────────────────────────────────────────────────────────┐\n", "# │ Window 1: [TRAIN period 1] → optimize params → TEST on period 1+ │\n", "# │ Window 2: [TRAIN period 2] → optimize params → TEST on period 2+ │\n", "# │ …repeat, always training on past, testing on future │\n", "# │ 始终用过去数据训练，用未来数据测试 │\n", "# └──────────────────────────────────────────────────────────────────────┘\n", "# Only report the concatenated OUT-OF-SAMPLE test results.\n", "# 只汇报样本外（OOS）的测试结果。\n", "#\n", "# Below: a simplified version — we just split into train / test (80/20).\n", "# 下面是简化版：直接按 80/20 切分训练集和测试集。\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "5e99677e", "metadata": {}, "outputs": [], "source": [ "TRAIN_RATIO = 0.8\n", "split_idx = int(len(price) * TRAIN_RATIO)\n", "split_date = price.index[split_idx]\n", "\n", "price_train = price.iloc[:split_idx]\n", "price_test = price.iloc[split_idx:]\n", "\n", "print(f\"\\n{'=' * 55}\")\n", "print(f\" 滚动前向验证 / Walk-Forward Split\")\n", "print(f\"{'=' * 55}\")\n", "print(f\" 训练期 Train: {price_train.index[0].date()} → {price_train.index[-1].date()} ({len(price_train)} 天)\")\n", "print(f\" 测试期 Test : {price_test.index[0].date()} → {price_test.index[-1].date()} ({len(price_test)} 天)\")\n", "\n", "# ── Optimize MA windows on TRAIN set 在训练集上优化均线参数 ────────────────────\n", "#\n", "# Grid search (网格搜索): try all combinations in the parameter space.\n", "# This is the simplest optimization method — good for small parameter spaces.\n", "# 网格搜索：遍历参数空间内的所有组合。适合参数空间小的情形。\n", "\n", "print(\"\\n[优化] 在训练集上搜索最优均线参数...\")\n", "print(\" (搜索空间: short=[5,10,15,20,30], long=[30,40,50,60,80,100])\")\n", "\n", "best_sharpe = -np.inf\n", "best_short = SHORT_WIN\n", "best_long = LONG_WIN\n", "results_grid = []\n", "\n", "for sw in [5, 10, 15, 20, 30]:\n", " for lw in [30, 40, 50, 60, 80, 100]:\n", " if sw >= lw:\n", " continue # short must be shorter than long / 短期必须小于长期\n", " ma_s = sma(price_train, sw)\n", " ma_l = sma(price_train, lw)\n", " sig = (ma_s > ma_l).astype(int).shift(1).fillna(0)\n", " bt = VectorizedBacktester(price_train, sig, cost_per_trade=0.001, name=\"grid\")\n", " m = bt.metrics()\n", " sharpe_val = float(m[\"夏普比率 Sharpe Ratio\"])\n", " results_grid.append({\"short\": sw, \"long\": lw, \"sharpe\": sharpe_val})\n", " if sharpe_val > best_sharpe:\n", " best_sharpe = sharpe_val\n", " best_short = sw\n", " best_long = lw\n", "\n", "print(f\"\\n 最优参数 (训练集 in-sample): short={best_short}, long={best_long}\")\n", "print(f\" 训练集夏普比率 In-sample Sharpe: {best_sharpe:.3f}\")\n", "\n", "# ── Apply best params on TEST set 将最优参数应用于测试集 ──────────────────────\n", "ma_s_test = sma(price_test, best_short)\n", "ma_l_test = sma(price_test, best_long)\n", "sig_test = (ma_s_test > ma_l_test).astype(int).shift(1).fillna(0)\n", "\n", "bt_test = VectorizedBacktester(price_test, sig_test, cost_per_trade=0.001,\n", " name=f\"MA({best_short}/{best_long}) — 测试集 OOS\")\n", "bt_test.print_metrics()\n", "\n", "print(\"\\n\" + \"=\" * 55)\n", "print(\" ⚠️ 注意 / WARNING:\")\n", "print(\" 训练集(in-sample)夏普 通常高于 测试集(out-of-sample)夏普\")\n", "print(\" In-sample Sharpe is typically HIGHER than out-of-sample.\")\n", "print(\" 夏普衰减 (Sharpe decay) 是策略过拟合的典型信号。\")\n", "print(\" Sharpe decay is a classic sign of overfitting.\")\n", "print(\"=\" * 55)" ] }, { "cell_type": "markdown", "id": "95c33a90", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 8: Return Distribution Analysis 收益率分布分析\n", "\n", "# -----------------------------------------------------------------------------\n", "# Before trusting your Sharpe ratio, check if the return distribution\n", "# violates the normality assumption.\n", "# 在相信夏普比率之前，检验收益率分布是否违背正态假设。\n", "#\n", "# Real returns typically show:\n", "# 真实收益率通常呈现:\n", "# Fat tails (厚尾 / leptokurtosis): extreme events more frequent than normal\n", "# Negative skew (负偏态): crashes are larger than rallies\n", "#\n", "# A high Sharpe ratio on a fat-tailed distribution can be misleading.\n", "# 厚尾分布下的高夏普比率可能具有误导性。\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "e74d07b8", "metadata": {}, "outputs": [], "source": [ "fig2, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# ── Return distribution histogram 收益率直方图 ─────────────────────────────\n", "ax = axes[0]\n", "r_ma = bt_ma.strat_ret.dropna()\n", "r_bh = bt_bh.strat_ret.dropna()\n", "\n", "ax.hist(r_bh, bins=80, alpha=0.5, color=\"gray\", density=True, label=\"Buy & Hold\")\n", "ax.hist(r_ma, bins=80, alpha=0.5, color=\"blue\", density=True, label=\"策略A: MA Crossover\")\n", "\n", "# Overlay a normal distribution for comparison / 叠加正态分布对比\n", "x_range = np.linspace(r_bh.min(), r_bh.max(), 300)\n", "ax.plot(x_range, stats.norm.pdf(x_range, r_bh.mean(), r_bh.std()),\n", " color=\"red\", linewidth=1.5, linestyle=\"--\", label=\"正态分布 Normal Dist.\")\n", "ax.set_title(\"收益率分布 (Return Distribution)\", fontsize=12)\n", "ax.set_xlabel(\"日收益率 Daily Return\")\n", "ax.set_ylabel(\"频率密度 Density\")\n", "ax.legend(fontsize=8)\n", "ax.grid(alpha=0.3)\n", "\n", "# Print distribution stats\n", "print(f\"\\n[分布] 策略A — 日收益率统计:\")\n", "print(f\" 偏度 Skewness : {r_ma.skew():.3f} (负值=左尾更厚 fat left tail)\")\n", "print(f\" 峰度 Kurtosis : {r_ma.kurtosis():.3f} (>0 表示厚尾 fat tails vs normal)\")\n", "\n", "# ── Monthly returns heatmap 月度收益热力图 ─────────────────────────────────\n", "ax = axes[1]\n", "monthly = bt_ma.strat_ret.resample(\"M\").apply(lambda x: (1 + x).prod() - 1)\n", "monthly_df = pd.DataFrame({\n", " \"year\": monthly.index.year,\n", " \"month\": monthly.index.month,\n", " \"ret\": monthly.values,\n", "})\n", "pivot = monthly_df.pivot(index=\"year\", columns=\"month\", values=\"ret\")\n", "pivot.columns = [\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"]\n", "\n", "import matplotlib.colors as mcolors\n", "cmap = mcolors.LinearSegmentedColormap.from_list(\"rg\", [\"#d73027\",\"#ffffff\",\"#1a9850\"])\n", "im = ax.imshow(pivot.values, cmap=cmap, aspect=\"auto\",\n", " vmin=-0.15, vmax=0.15)\n", "ax.set_xticks(range(12))\n", "ax.set_xticklabels(pivot.columns, fontsize=8)\n", "ax.set_yticks(range(len(pivot.index)))\n", "ax.set_yticklabels(pivot.index, fontsize=9)\n", "for i in range(len(pivot.index)):\n", " for j in range(12):\n", " v = pivot.values[i, j]\n", " if not np.isnan(v):\n", " ax.text(j, i, f\"{v:.1%}\", ha=\"center\", va=\"center\", fontsize=6,\n", " color=\"black\" if abs(v) < 0.08 else \"white\")\n", "ax.set_title(\"策略A月度收益热力图\\n(Monthly Return Heatmap)\", fontsize=11)\n", "plt.colorbar(im, ax=ax, format=plt.FuncFormatter(lambda x, _: f\"{x:.0%}\"))\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"return_distribution.png\", dpi=120, bbox_inches=\"tight\")\n", "plt.show()\n", "print(\"[图表] 已保存至 return_distribution.png\")" ] }, { "cell_type": "markdown", "id": "4dfb5f58", "metadata": {}, "source": [ "# =============================================================================\n", "# SECTION 9: Summary & Next Steps 总结与后续\n", "\n", "# =============================================================================" ] }, { "cell_type": "code", "execution_count": null, "id": "7bac6b3d", "metadata": {}, "outputs": [], "source": [ "print(f\"\"\"\n", "{'=' * 70}\n", " 总结 Summary\n", "{'=' * 70}\n", " 本 Demo 演示了量化策略开发与回测的完整流程：\n", "\n", " ① 技术指标计算 Technical Indicators\n", " SMA / EMA / RSI / MACD / Bollinger Bands\n", "\n", " ② 信号生成 Signal Generation\n", " 策略A: 双均线金叉死叉 (MA Crossover) — 趋势跟随\n", " 策略B: RSI 超买超卖 (RSI Reversion) — 均值回归\n", "\n", " ③ 向量化回测引擎 Vectorized Backtester\n", " 考虑了交易成本(佣金+滑点)和前视偏差(lookahead bias)\n", "\n", " ④ 绩效指标 Performance Metrics\n", " Sharpe / Sortino / Max Drawdown / Calmar / Win Rate / Profit Factor\n", "\n", " ⑤ 前向验证 Walk-Forward Validation\n", " 训练集优化参数 → 测试集验证 → 防止过拟合\n", "\n", " ⑥ 收益率分布 Return Distribution\n", " 偏度/峰度检验，月度热力图\n", "\n", " 下一步学习方向 Next Steps:\n", " ──────────────────────────────────────────────────────────────\n", " • 因子选股策略 (Alpha Factor Models) — Fama-French, Momentum\n", " • 组合优化 (Portfolio Optimization) — Mean-Variance, Risk Parity\n", " • 事件驱动回测 (Event-Driven Backtesting) — more realistic execution\n", " • 机器学习信号 (ML-based Signals) — XGBoost, LSTM for return prediction\n", " • 风险管理 (Risk Management) — Position sizing, Stop-loss, VaR\n", "{'=' * 70}\n", "\"\"\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (trading)", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }