From 42218741d451644d581a909b3a2cc659676bd34b Mon Sep 17 00:00:00 2001 From: Gahow Wang Date: Sun, 5 Apr 2026 00:41:19 +0800 Subject: [PATCH] Initial commit: quant backtesting framework with daily trading simulator Backtesting engine supporting 11 strategies across US (S&P 500) and CN (CSI 300) markets with open-to-close execution, proportional + fixed per-trade fees. Daily trader (trader.py) with auto/morning/evening/simulate/status commands and cron-friendly `auto` mode for unattended daily runs on a server. Co-Authored-By: Claude Opus 4.6 --- .gitignore | 31 ++ .python-version | 1 + CLAUDE.md | 79 +++ data/.gitkeep | 0 data_manager.py | 247 ++++++++ main.py | 247 ++++++++ metrics.py | 72 +++ pyproject.toml | 14 + strategies/__init__.py | 0 strategies/adaptive_momentum.py | 60 ++ strategies/base.py | 17 + strategies/buy_and_hold.py | 15 + strategies/dual_momentum.py | 55 ++ strategies/inverse_vol.py | 32 ++ strategies/mean_reversion.py | 56 ++ strategies/momentum.py | 37 ++ strategies/momentum_quality.py | 69 +++ strategies/multi_factor.py | 77 +++ strategies/recovery_momentum.py | 66 +++ strategies/trend_following.py | 51 ++ trader.py | 816 +++++++++++++++++++++++++++ universe.py | 135 +++++ uv.lock | 959 ++++++++++++++++++++++++++++++++ 23 files changed, 3136 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 CLAUDE.md create mode 100644 data/.gitkeep create mode 100644 data_manager.py create mode 100644 main.py create mode 100644 metrics.py create mode 100644 pyproject.toml create mode 100644 strategies/__init__.py create mode 100644 strategies/adaptive_momentum.py create mode 100644 strategies/base.py create mode 100644 strategies/buy_and_hold.py create mode 100644 strategies/dual_momentum.py create mode 100644 strategies/inverse_vol.py create mode 100644 strategies/mean_reversion.py create mode 100644 strategies/momentum.py create mode 100644 strategies/momentum_quality.py create mode 100644 strategies/multi_factor.py create mode 100644 strategies/recovery_momentum.py create mode 100644 strategies/trend_following.py create mode 100644 trader.py create mode 100644 universe.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3f42693 --- /dev/null +++ b/.gitignore @@ -0,0 +1,31 @@ +# Python +__pycache__/ +*.py[cod] +*.egg-info/ +build/ +dist/ +wheels/ + +# Virtual environment +.venv/ + +# Data files — downloaded at runtime, large CSVs +data/*.csv +data/*.png + +# Universe caches — re-fetched daily +data/universe_*.json + +# Trader state — per-machine, regenerated by auto/simulate +data/trader_*.json + +# IDE / editor +.idea/ +.vscode/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..5af2b30 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,79 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +Quantitative backtesting framework for comparing equity trading strategies across multiple markets. Supports US (S&P 500) and China A-shares (CSI 300). Uses Yahoo Finance data with persistent local CSV storage. Includes a daily trading simulator with automated cron support. + +## Commands + +```bash +# Backtesting +uv run python main.py # Run US backtest (default) +uv run python main.py --market cn # Run China A-share backtest +uv run python main.py --capital 50000 # Custom starting capital +uv run python main.py --top-n 20 # Override stock selection count +uv run python main.py --years 5 # Backtest last N years only +uv run python main.py --no-plot # Skip matplotlib charts +uv run python main.py --fixed-fee 2.0 # Add $2 per-trade fixed fee +uv run python main.py --execution open-close # Signal on open, execute at close + +# Daily trading simulator +uv run python trader.py auto --market us --strategy recovery_mom_top10 # Single daily run (for cron) +uv run python trader.py morning --market us --strategy recovery_mom_top10 # Morning: generate orders +uv run python trader.py evening --market us --strategy recovery_mom_top10 # Evening: record execution +uv run python trader.py status --market us --strategy recovery_mom_top10 # Portfolio status +uv run python trader.py simulate --market us --strategy recovery_mom_top10 --start 2026-01-01 --end 2026-04-01 # Historical replay + +# Setup +uv sync # Install/sync dependencies +``` + +No test suite or linter is configured. + +## Architecture + +**Universe provider** (`universe.py`): Dynamically fetches index constituents (S&P 500 from Wikipedia, CSI 300 from Wikipedia) and converts to Yahoo Finance ticker format. Constituent lists are cached daily in `data/universe_*.json`. New markets are added by registering in `UNIVERSES` dict. + +**Data manager** (`data_manager.py`): Persistent price storage in `data/{market}.csv` (close) and `data/{market}_open.csv` (open). First run downloads 10 years of history; subsequent runs append only new trading days (fast bulk fetch for gaps ≤7 days). New tickers from index rebalances are backfilled automatically. Tickers with >50% missing data are dropped. Pass `with_open=True` to also maintain open price files. + +**Backtest engine** (`main.py`): Orchestrates data loading, strategy execution, and visualization. The `backtest()` function is vectorized — it takes a strategy and price DataFrame, applies transaction costs (proportional + optional fixed per-trade fee) via turnover, and returns an equity curve. Supports two execution modes: `close` (classic) and `open-close` (signal on open prices, execute at close). + +**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`. + +**Strategy protocol** (`strategies/base.py`): All strategies inherit from `Strategy` ABC and implement `generate_signals(data) → DataFrame` where the returned DataFrame contains portfolio weights (rows = dates, columns = assets, values sum to ~1.0 per row). Each strategy is responsible for applying its own 1-day lag via `.shift(1)` to avoid lookahead bias — the backtest engine does not shift. + +**Strategies**: +- `buy_and_hold.py` — Equal-weight static allocation +- `momentum.py` — Cross-sectional momentum (12-1 month factor), selects top_n winners +- `inverse_vol.py` — Inverse-volatility (risk parity) weighting +- `multi_factor.py` — Combines momentum + value factors with benchmark MA timing filter +- `mean_reversion.py` — Short-term mean reversion +- `trend_following.py` — MA crossover + momentum trend filter +- `dual_momentum.py` — Absolute + relative momentum +- `momentum_quality.py` — Momentum + return consistency + low drawdown +- `adaptive_momentum.py` — Momentum weighted by inverse volatility +- `recovery_momentum.py` — Recovery (price/63d low) + 12-1mo momentum composite. Best US performer. + +**Metrics** (`metrics.py`): Standalone functions for portfolio analytics (Sharpe, Sortino, Calmar, max drawdown, etc.). `summary()` prints a formatted report and returns a dict. + +## Key Conventions + +- Strategies output **weights**, not buy/sell signals. Weights are normalized so each row sums to 1 (or 0 during warm-up). +- Warm-up periods: each strategy zeros out its first N rows where indicators are undefined. +- `data/` contains persistent price CSVs, universe JSON caches, and trader state files. Not checked into git (only `data/.gitkeep` is tracked). +- Python 3.12+, managed with `uv`. +- Do not show matplotlib figures when running backtests; use `--no-plot`. + +## Server Deployment (Cron) + +Run daily after market close. The `auto` command is idempotent — safe to re-run: + +```bash +# US market: 5pm ET weekdays +0 17 * * 1-5 cd /path/to/quant && uv run python trader.py auto --market us --strategy recovery_mom_top10 >> data/cron.log 2>&1 + +# CN market: 4pm CST weekdays +0 16 * * 1-5 cd /path/to/quant && uv run python trader.py auto --market cn --strategy inverse_vol >> data/cron.log 2>&1 +``` diff --git a/data/.gitkeep b/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/data_manager.py b/data_manager.py new file mode 100644 index 0000000..f1f4d4e --- /dev/null +++ b/data_manager.py @@ -0,0 +1,247 @@ +""" +Persistent local data store for price history. + +Layout: + data/us.csv — S&P 500 + SPY adjusted close prices + data/us_open.csv — S&P 500 + SPY adjusted open prices + data/cn.csv — CSI 300 + 000300.SS adjusted close prices + data/cn_open.csv — CSI 300 + 000300.SS adjusted open prices + +On first run: downloads full 10-year history. +On subsequent runs: reads existing file, downloads only new dates, appends. +New tickers (index rebalance) are backfilled automatically. +""" + +import os +from datetime import datetime, timedelta + +import pandas as pd +import yfinance as yf + +DATA_DIR = "data" + + +def _data_path(market: str, price_type: str = "close") -> str: + suffix = "" if price_type == "close" else f"_{price_type}" + return os.path.join(DATA_DIR, f"{market}{suffix}.csv") + + +def load(market: str, price_type: str = "close") -> pd.DataFrame | None: + """Load existing data file, or None if it doesn't exist.""" + path = _data_path(market, price_type) + if not os.path.exists(path): + return None + df = pd.read_csv(path, index_col=0, parse_dates=True) + return df + + +def _download(tickers: list[str], start: str, end: str | None = None, + fields: list[str] | None = None) -> dict[str, pd.DataFrame]: + """ + Download price data from Yahoo Finance. + + Parameters + ---------- + fields : list of str, optional + Price fields to extract, e.g. ["Close", "Open"]. + Defaults to ["Close"]. + + Returns + ------- + dict mapping field name to DataFrame (columns = tickers). + """ + if fields is None: + fields = ["Close"] + print(f"--- Downloading {len(tickers)} tickers from {start}{f' to {end}' if end else ''} ---") + kwargs = dict(auto_adjust=True) + if end: + kwargs["end"] = end + raw = yf.download(tickers, start=start, **kwargs) + if raw.empty: + return {f: pd.DataFrame() for f in fields} + + result = {} + for field in fields: + if field in raw.columns.get_level_values(0) if isinstance(raw.columns, pd.MultiIndex) else field in raw.columns: + if len(tickers) > 1: + result[field] = raw[field] + else: + result[field] = raw[field].to_frame(name=tickers[0]) + else: + result[field] = pd.DataFrame() + return result + + +def _download_period(tickers: list[str], period: str, + fields: list[str] | None = None) -> dict[str, pd.DataFrame]: + """Download using period-based API (for small gaps).""" + if fields is None: + fields = ["Close"] + raw = yf.download(tickers, period=period, auto_adjust=True) + if raw.empty: + return {f: pd.DataFrame() for f in fields} + result = {} + for field in fields: + if field in raw.columns.get_level_values(0) if isinstance(raw.columns, pd.MultiIndex) else field in raw.columns: + if len(tickers) > 1: + result[field] = raw[field] + else: + result[field] = raw[field].to_frame(name=tickers[0]) + else: + result[field] = pd.DataFrame() + return result + + +def _clean(data: pd.DataFrame) -> pd.DataFrame: + """Drop tickers with >50% missing data, forward-fill, drop all-NaN rows.""" + good = data.columns[data.notna().mean() > 0.5] + dropped = set(data.columns) - set(good) + if dropped: + print(f"--- Dropped {len(dropped)} tickers with >50% missing data ---") + data = data[good] + data = data.ffill().dropna(how="all") + return data + + +def update(market: str, tickers: list[str], + with_open: bool = False) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: + """ + Load existing data, download new dates and new tickers, save back. + + Parameters + ---------- + with_open : bool + If True, also download/maintain Open prices and return (close, open) tuple. + + Returns + ------- + pd.DataFrame or (close_df, open_df) + Price data ready for backtesting. + """ + os.makedirs(DATA_DIR, exist_ok=True) + fields = ["Close", "Open"] if with_open else ["Close"] + + existing_close = load(market, "close") + existing_open = load(market, "open") if with_open else None + + # If we need open prices but don't have them cached, force a full re-download + need_full_open = with_open and existing_open is None and existing_close is not None + + if existing_close is None: + # First run: download full 10-year history + start = (datetime.now() - timedelta(days=365 * 10)).strftime("%Y-%m-%d") + downloaded = _download(tickers, start=start, fields=fields) + close_data = downloaded["Close"] + open_data = downloaded.get("Open", pd.DataFrame()) if with_open else None + elif need_full_open: + # Have close data but need open data — download open prices for full range + close_data = existing_close + start = close_data.index[0].strftime("%Y-%m-%d") + known_tickers = [t for t in close_data.columns if t in tickers] + print(f"--- Downloading Open prices for {len(known_tickers)} tickers (first time) ---") + downloaded = _download(known_tickers, start=start, fields=["Open"]) + open_data = downloaded.get("Open", pd.DataFrame()) + last_date = close_data.index[-1] + + # Still need to append new close dates and backfill new tickers + next_day = (last_date + timedelta(days=1)).strftime("%Y-%m-%d") + today = datetime.now().strftime("%Y-%m-%d") + if next_day < today: + if known_tickers: + gap_days = (datetime.now() - last_date.to_pydatetime().replace(tzinfo=None)).days + if gap_days <= 7: + print(f"--- Fetching last {gap_days + 5}d to append ---") + new = _download_period(known_tickers, f"{gap_days + 5}d", fields=fields) + new_close = new["Close"] + new_close = new_close[new_close.index > last_date] + else: + new = _download(known_tickers, start=next_day, fields=fields) + new_close = new["Close"] + if not new_close.empty: + close_data = pd.concat([close_data, new_close]).sort_index() + close_data = close_data[~close_data.index.duplicated(keep="last")] + print(f"--- Appended {len(new_close)} new days ---") + else: + print("--- No new trading days to append ---") + + new_tickers = [t for t in tickers if t not in close_data.columns] + if new_tickers: + start = close_data.index[0].strftime("%Y-%m-%d") + backfill = _download(new_tickers, start=start, fields=fields) + backfill_close = backfill["Close"] + backfill_open = backfill.get("Open", pd.DataFrame()) + if not backfill_close.empty: + backfill_close = backfill_close.reindex(close_data.index) + close_data = pd.concat([close_data, backfill_close], axis=1) + if not backfill_open.empty and open_data is not None: + backfill_open = backfill_open.reindex(open_data.index) + open_data = pd.concat([open_data, backfill_open], axis=1) + print(f"--- Backfilled {len(new_tickers)} new tickers ---") + else: + close_data = existing_close + open_data = existing_open if with_open else None + last_date = close_data.index[-1] + + # 1) Append new dates for existing tickers + next_day = (last_date + timedelta(days=1)).strftime("%Y-%m-%d") + today = datetime.now().strftime("%Y-%m-%d") + if next_day < today: + known_tickers = [t for t in close_data.columns if t in tickers] + if known_tickers: + gap_days = (datetime.now() - last_date.to_pydatetime().replace(tzinfo=None)).days + if gap_days <= 7: + print(f"--- Fetching last {gap_days + 5}d to append ---") + new = _download_period(known_tickers, f"{gap_days + 5}d", fields=fields) + new_close = new["Close"] + new_close = new_close[new_close.index > last_date] + new_open = new.get("Open", pd.DataFrame()) + if not new_open.empty: + new_open = new_open[new_open.index > last_date] + else: + new = _download(known_tickers, start=next_day, fields=fields) + new_close = new["Close"] + new_open = new.get("Open", pd.DataFrame()) + + if not new_close.empty: + close_data = pd.concat([close_data, new_close]).sort_index() + close_data = close_data[~close_data.index.duplicated(keep="last")] + print(f"--- Appended {len(new_close)} new days ---") + if with_open and not new_open.empty and open_data is not None: + open_data = pd.concat([open_data, new_open]).sort_index() + open_data = open_data[~open_data.index.duplicated(keep="last")] + else: + print("--- No new trading days to append ---") + + # 2) Backfill any new tickers not in existing data + new_tickers = [t for t in tickers if t not in close_data.columns] + if new_tickers: + start = close_data.index[0].strftime("%Y-%m-%d") + backfill = _download(new_tickers, start=start, fields=fields) + backfill_close = backfill["Close"] + backfill_open = backfill.get("Open", pd.DataFrame()) + if not backfill_close.empty: + backfill_close = backfill_close.reindex(close_data.index) + close_data = pd.concat([close_data, backfill_close], axis=1) + print(f"--- Backfilled {len(new_tickers)} new tickers ---") + if with_open and not backfill_open.empty and open_data is not None: + backfill_open = backfill_open.reindex(open_data.index) + open_data = pd.concat([open_data, backfill_open], axis=1) + + # Clean and save close + close_data = _clean(close_data) + close_path = _data_path(market, "close") + close_data.to_csv(close_path) + print(f"--- Saved {close_data.shape[0]} days x {close_data.shape[1]} tickers to {close_path} ---") + + if with_open and open_data is not None and not open_data.empty: + # Align open data to same tickers/dates as close + open_data = open_data.reindex(index=close_data.index, columns=close_data.columns) + open_data = open_data.ffill().dropna(how="all") + # Where open is missing, fall back to close + open_data = open_data.fillna(close_data) + open_path = _data_path(market, "open") + open_data.to_csv(open_path) + print(f"--- Saved {open_data.shape[0]} days x {open_data.shape[1]} tickers to {open_path} ---") + return close_data, open_data + + return close_data diff --git a/main.py b/main.py new file mode 100644 index 0000000..e870cb2 --- /dev/null +++ b/main.py @@ -0,0 +1,247 @@ +import argparse + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +import data_manager +import metrics +from strategies.adaptive_momentum import AdaptiveMomentumStrategy +from strategies.buy_and_hold import BuyAndHoldStrategy +from strategies.dual_momentum import DualMomentumStrategy +from strategies.inverse_vol import InverseVolatilityStrategy +from strategies.mean_reversion import MeanReversionStrategy +from strategies.momentum import MomentumStrategy +from strategies.momentum_quality import MomentumQualityStrategy +from strategies.multi_factor import MultiFactorStrategy +from strategies.recovery_momentum import RecoveryMomentumStrategy +from strategies.trend_following import TrendFollowingStrategy +from universe import UNIVERSES + + +# --------------------------------------------------------------------------- +# Backtest engine +# --------------------------------------------------------------------------- + +def backtest( + strategy, + data: pd.DataFrame, + initial_capital: float = 100_000, + transaction_cost: float = 0.001, + fixed_fee: float = 0.0, + open_data: pd.DataFrame | None = None, +) -> pd.Series: + """ + Vectorized backtest. + + Parameters + ---------- + strategy : Strategy + Any class implementing generate_signals(data) → DataFrame of weights. + data : pd.DataFrame + Adjusted close prices, one column per asset. + initial_capital : float + Starting portfolio value. + transaction_cost : float + One-way cost per unit of turnover (e.g. 0.001 = 10 bps). + fixed_fee : float + Fixed dollar cost per individual trade (each buy or sell). + open_data : pd.DataFrame, optional + Open prices. When provided, enables open-to-close execution mode: + - Morning: observe open prices → run strategy → decide weights + - Evening: execute all trades at close prices + Strategies have an internal shift(1) designed for close prices. + Since open prices are observable same-day (before close), we undo + that shift so signals use today's open and execute at today's close. + + Returns + ------- + pd.Series + Daily equity curve. + """ + if open_data is not None: + # Open-to-close mode: + # Strategy's shift(1) on open prices gives: weights[t] = f(open_{t-1}) + # But open_t is known at morning of day t, so undo shift to get f(open_t) + # Then execute at close_t, earning close_t → close_{t+1} + weights = strategy.generate_signals(open_data) + weights = weights.shift(-1).fillna(0.0) + weights = weights.reindex(data.index).fillna(0.0) + positions = weights + + # Returns earned: close[t] → close[t+1], weighted by positions decided at open[t] + close_returns = data.pct_change().fillna(0.0) + portfolio_returns = (close_returns * positions.shift(1).fillna(0.0)).sum(axis=1) + else: + # Classic close-to-close mode + weights = strategy.generate_signals(data) + weights = weights.reindex(data.index).fillna(0.0) + positions = weights + + daily_returns = data.pct_change().fillna(0.0) + portfolio_returns = (daily_returns * positions).sum(axis=1) + + # Turnover cost: sum of absolute weight changes each day + turnover = positions.diff().abs().sum(axis=1).fillna(0.0) + portfolio_returns -= turnover * transaction_cost + + # Fixed per-trade fee: count positions with non-zero weight change + if fixed_fee > 0: + weight_changes = positions.diff().fillna(0.0) + n_trades = (weight_changes.abs() > 1e-8).sum(axis=1) + # Build running equity to convert dollar fees to return impact + equity_running = (1 + portfolio_returns).cumprod() * initial_capital + fee_impact = (n_trades * fixed_fee) / equity_running.shift(1).fillna(initial_capital) + portfolio_returns -= fee_impact + + equity = (1 + portfolio_returns).cumprod() * initial_capital + return equity + + +# --------------------------------------------------------------------------- +# Visualization +# --------------------------------------------------------------------------- + +def plot_results(results: pd.DataFrame) -> None: + """Two-panel chart: equity curves (top) + drawdowns (bottom).""" + # Compute drawdowns + drawdowns = results.div(results.cummax()) - 1 + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 9), sharex=True, + gridspec_kw={"height_ratios": [3, 1]}) + + for col in results.columns: + ax1.plot(results.index, results[col], label=col, linewidth=1.5) + ax1.set_title("Strategy Comparison — Equity Curves", fontsize=14) + ax1.set_ylabel("Portfolio Value ($)") + ax1.legend(loc="upper left") + ax1.grid(True, alpha=0.3) + + for col in drawdowns.columns: + ax2.plot(drawdowns.index, drawdowns[col] * 100, label=col, linewidth=1.0) + ax2.set_title("Drawdowns") + ax2.set_ylabel("Drawdown (%)") + ax2.set_xlabel("Date") + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main() -> None: + parser = argparse.ArgumentParser(description="Run strategy backtest") + parser.add_argument( + "--market", choices=UNIVERSES.keys(), default="us", + help="Market universe to backtest (default: us)", + ) + parser.add_argument( + "--capital", type=float, default=None, + help="Initial capital (default: 10000)", + ) + parser.add_argument( + "--top-n", type=int, default=None, + help="Number of stocks for selective strategies (default: ~10%% of universe)", + ) + parser.add_argument( + "--years", type=int, default=None, + help="Limit backtest to last N years of data", + ) + parser.add_argument( + "--no-plot", action="store_true", + help="Skip plotting charts", + ) + parser.add_argument( + "--fixed-fee", type=float, default=0.0, + help="Fixed dollar cost per trade, e.g. 2.0 means $2 per buy or sell", + ) + parser.add_argument( + "--execution", choices=["close", "open-close"], default="close", + help="Execution mode: 'close' (default, signal & execute on close) or " + "'open-close' (signal on morning open, execute at close)", + ) + args = parser.parse_args() + initial_capital = args.capital if args.capital is not None else 10_000 + use_open = args.execution == "open-close" + + universe = UNIVERSES[args.market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + benchmark_label = universe["benchmark_label"] + all_tickers = sorted(set(tickers + [benchmark])) + + result = data_manager.update(args.market, all_tickers, with_open=use_open) + if use_open: + data, open_data = result + else: + data = result + open_data = None + + if args.years: + cutoff = data.index[-1] - pd.DateOffset(years=args.years) + data = data[data.index >= cutoff] + if open_data is not None: + open_data = open_data[open_data.index >= cutoff] + print(f"--- Sliced to last {args.years} years: {data.index[0].date()} to {data.index[-1].date()} ---") + + # Filter tickers to only those in the data + tickers = [t for t in tickers if t in data.columns] + print(f"--- Universe: {len(tickers)} stocks + {benchmark} benchmark ---") + + top_n = args.top_n if args.top_n else max(5, len(tickers) // 10) + print(f"--- Selective strategies will pick top {top_n} stocks ---") + if args.fixed_fee > 0: + print(f"--- Fixed fee: ${args.fixed_fee:.2f} per trade ---") + if use_open: + print(f"--- Execution: open-close (signal on open, execute at close) ---") + + # Build strategy tuples: (strategy, close_data, open_data_or_None) + open_tickers = open_data[tickers] if open_data is not None else None + open_all = open_data if open_data is not None else None + + strategies = { + # --- Original strategies --- + "Buy & Hold (EW)": (BuyAndHoldStrategy(), data[tickers], open_tickers), + "Momentum": (MomentumStrategy(lookback=252, skip=21, top_n=top_n), data[tickers], open_tickers), + "Inverse Volatility": (InverseVolatilityStrategy(vol_window=20), data[tickers], open_tickers), + "Multi-Factor": (MultiFactorStrategy(tickers=tickers, benchmark=benchmark, top_n=top_n), data, open_all), + # --- New strategies --- + "Mean Reversion": (MeanReversionStrategy(top_n=top_n), data[tickers], open_tickers), + "Trend Following": (TrendFollowingStrategy(ma_window=150, momentum_period=126, top_n=top_n), data[tickers], open_tickers), + "Dual Momentum": (DualMomentumStrategy(top_n=top_n), data[tickers], open_tickers), + "Momentum+Quality": (MomentumQualityStrategy(momentum_period=252, skip=21, top_n=top_n), data[tickers], open_tickers), + "Mom+InvVol": (AdaptiveMomentumStrategy(top_n=top_n), data[tickers], open_tickers), + "Recovery+Mom Top20": (RecoveryMomentumStrategy(top_n=min(20, top_n)), data[tickers], open_tickers), + "Recovery+Mom Top10": (RecoveryMomentumStrategy(top_n=10), data[tickers], open_tickers), + } + + results: dict[str, pd.Series] = {} + for name, (strategy, strat_data, strat_open) in strategies.items(): + print(f"\nRunning: {name}") + results[name] = backtest(strategy, strat_data, initial_capital=initial_capital, + fixed_fee=args.fixed_fee, open_data=strat_open) + + # Add benchmark (normalized to same initial capital) + bench = data[benchmark].dropna() + results[benchmark_label] = (bench / bench.iloc[0]) * initial_capital + + results_df = pd.DataFrame(results) + + # --- Performance metrics --- + for name, equity in results_df.items(): + eq = equity.dropna() + if len(eq) < 2: + print(f"\n {name}: insufficient data, skipping") + continue + metrics.summary(eq, name=name) + + # --- Visualization --- + if not args.no_plot: + plot_results(results_df.dropna()) + + +if __name__ == "__main__": + main() diff --git a/metrics.py b/metrics.py new file mode 100644 index 0000000..0b10495 --- /dev/null +++ b/metrics.py @@ -0,0 +1,72 @@ +import numpy as np +import pandas as pd + + +def total_return(equity: pd.Series) -> float: + return equity.iloc[-1] / equity.iloc[0] - 1 + + +def annualized_return(equity: pd.Series, periods: int = 252) -> float: + n = len(equity) + return (1 + total_return(equity)) ** (periods / n) - 1 + + +def annualized_volatility(returns: pd.Series, periods: int = 252) -> float: + return returns.std() * np.sqrt(periods) + + +def sharpe_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float: + excess = returns - risk_free_rate / periods + std = excess.std() + if std == 0: + return 0.0 + return (excess.mean() / std) * np.sqrt(periods) + + +def sortino_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float: + excess = returns - risk_free_rate / periods + downside_std = excess[excess < 0].std() + if downside_std == 0: + return 0.0 + return (excess.mean() / downside_std) * np.sqrt(periods) + + +def max_drawdown(equity: pd.Series) -> float: + rolling_peak = equity.cummax() + drawdown = (equity - rolling_peak) / rolling_peak + return drawdown.min() + + +def calmar_ratio(equity: pd.Series, periods: int = 252) -> float: + ann_ret = annualized_return(equity, periods) + mdd = abs(max_drawdown(equity)) + if mdd == 0: + return np.inf + return ann_ret / mdd + + +def win_rate(returns: pd.Series) -> float: + active = returns[returns != 0] + if len(active) == 0: + return 0.0 + return (active > 0).sum() / len(active) + + +def summary(equity: pd.Series, name: str = "Strategy") -> dict: + returns = equity.pct_change().dropna() + metrics = { + "Total Return": f"{total_return(equity):.2%}", + "Annualized Return": f"{annualized_return(equity):.2%}", + "Annualized Volatility": f"{annualized_volatility(returns):.2%}", + "Sharpe Ratio": f"{sharpe_ratio(returns):.2f}", + "Sortino Ratio": f"{sortino_ratio(returns):.2f}", + "Max Drawdown": f"{max_drawdown(equity):.2%}", + "Calmar Ratio": f"{calmar_ratio(equity):.2f}", + "Win Rate": f"{win_rate(returns):.2%}", + } + print(f"\n{'='*45}") + print(f" {name}") + print(f"{'='*45}") + for k, v in metrics.items(): + print(f" {k:<26} {v:>10}") + return metrics diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..5818f4b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "quant" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "lxml>=6.0.2", + "matplotlib>=3.10.8", + "numpy>=2.4.0", + "pandas>=2.3.3", + "scipy>=1.17.1", + "yfinance>=1.0", +] diff --git a/strategies/__init__.py b/strategies/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/strategies/adaptive_momentum.py b/strategies/adaptive_momentum.py new file mode 100644 index 0000000..f79b4fa --- /dev/null +++ b/strategies/adaptive_momentum.py @@ -0,0 +1,60 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class AdaptiveMomentumStrategy(Strategy): + """ + Momentum with dynamic position sizing via inverse-volatility weighting. + + Combines two proven ideas: + 1. Momentum selection: pick top_n stocks by 12-1 month return + 2. Inverse-vol weighting: instead of equal-weight, allocate more + to lower-vol winners (smoother ride, better risk-adjusted returns) + 3. Regime scaling: scale total exposure by inverse of market vol + (reduce exposure in high-vol periods, increase in calm periods) + + This addresses momentum's main weakness: concentration in high-vol + names that crash hard in risk-off episodes. + """ + + def __init__(self, lookback: int = 252, skip: int = 21, + vol_window: int = 60, top_n: int = 20): + self.lookback = lookback + self.skip = skip + self.vol_window = vol_window + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # Momentum ranking + momentum = data.shift(self.skip).pct_change(self.lookback - self.skip) + + n_valid = momentum.notna().sum(axis=1) + enough = n_valid >= self.top_n + + score_rank = momentum.rank(axis=1, ascending=False, na_option="bottom") + top_mask = (score_rank <= self.top_n) & enough.values.reshape(-1, 1) + + # Inverse-vol weighting among selected stocks + returns = data.pct_change() + vol = returns.rolling(self.vol_window).std().replace(0, np.nan) + inv_vol = (1.0 / vol).where(top_mask, 0.0) + + row_sums = inv_vol.sum(axis=1).replace(0, np.nan) + signals = inv_vol.div(row_sums, axis=0).fillna(0.0) + + # Regime scaling: compare current market vol to its 1-year median + market_vol = vol.mean(axis=1) + vol_median = market_vol.rolling(252).median() + vol_scale = (vol_median / market_vol).clip(0.3, 1.5) + signals = signals.mul(vol_scale, axis=0) + + # Re-normalize so max total weight = 1.0 + row_totals = signals.sum(axis=1) + overflow = row_totals > 1.0 + signals[overflow] = signals[overflow].div(row_totals[overflow], axis=0) + + warmup = max(self.lookback, self.vol_window + 252) + signals.iloc[:warmup] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/base.py b/strategies/base.py new file mode 100644 index 0000000..63e2a1c --- /dev/null +++ b/strategies/base.py @@ -0,0 +1,17 @@ +from abc import ABC, abstractmethod + +class Strategy(ABC): + """ + Abstract base class for all strategies. + """ + + @abstractmethod + def generate_signals(self, data): + """ + Generates trading signals for each stock. + + :param data: A pandas DataFrame of historical price data. + :return: A pandas Series with the same index as the input data, + containing the trading signals (e.g., 'buy', 'hold', 'sell'). + """ + pass diff --git a/strategies/buy_and_hold.py b/strategies/buy_and_hold.py new file mode 100644 index 0000000..d058214 --- /dev/null +++ b/strategies/buy_and_hold.py @@ -0,0 +1,15 @@ +import pandas as pd +from strategies.base import Strategy + +class BuyAndHoldStrategy(Strategy): + """ + A simple buy and hold strategy. + """ + + def generate_signals(self, data): + """ + Generates equal weights for all assets. + """ + tickers = data.columns + weights = pd.DataFrame(1 / len(tickers), index=data.index, columns=tickers) + return weights diff --git a/strategies/dual_momentum.py b/strategies/dual_momentum.py new file mode 100644 index 0000000..af8a77f --- /dev/null +++ b/strategies/dual_momentum.py @@ -0,0 +1,55 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class DualMomentumStrategy(Strategy): + """ + Dual momentum with multi-timeframe confirmation. + + Unlike plain momentum (single 12-1m lookback), this requires agreement + across THREE timeframes — short (1-3m), medium (3-6m), and long (6-12m). + A stock must show positive returns on ALL three to qualify. + + This filters out stocks riding a single spike and favors sustained trends. + Position count is variable: when few stocks pass all three filters, + the strategy naturally goes to partial cash. + """ + + def __init__(self, top_n: int = 20): + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # Three timeframe returns (each skipping most recent 5 days) + skip = 5 + short_mom = data.shift(skip).pct_change(63 - skip) # ~3 month + med_mom = data.shift(skip).pct_change(126 - skip) # ~6 month + long_mom = data.shift(skip).pct_change(252 - skip) # ~12 month + + # All three must be positive (absolute momentum across timeframes) + all_positive = (short_mom > 0) & (med_mom > 0) & (long_mom > 0) + + # Composite score: average percentile rank across timeframes + short_rank = short_mom.rank(axis=1, pct=True, na_option="bottom") + med_rank = med_mom.rank(axis=1, pct=True, na_option="bottom") + long_rank = long_mom.rank(axis=1, pct=True, na_option="bottom") + composite = (short_rank + med_rank + long_rank) / 3 + + # Only consider stocks passing absolute filter + composite_filtered = composite.where(all_positive, np.nan) + + n_valid = composite_filtered.notna().sum(axis=1) + enough = n_valid >= 1 + + rank = composite_filtered.rank(axis=1, ascending=False, na_option="bottom") + effective_n = n_valid.clip(upper=self.top_n) + top_mask = (rank <= effective_n.values.reshape(-1, 1)) & enough.values.reshape(-1, 1) + top_mask = top_mask & all_positive + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + signals.iloc[:252] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/inverse_vol.py b/strategies/inverse_vol.py new file mode 100644 index 0000000..8789ced --- /dev/null +++ b/strategies/inverse_vol.py @@ -0,0 +1,32 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class InverseVolatilityStrategy(Strategy): + """ + Risk parity via inverse-volatility weighting. + + Allocates capital inversely proportional to each asset's realized volatility + over a rolling window. Assets with lower recent volatility receive larger + allocations, equalizing the risk contribution of each position. + + This is a simple but robust baseline for risk-based portfolio construction. + """ + + def __init__(self, vol_window: int = 20): + self.vol_window = vol_window + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + returns = data.pct_change() + vol = returns.rolling(self.vol_window).std() + + # Replace zero vol with NaN to avoid division by zero + vol = vol.replace(0, np.nan) + inv_vol = 1.0 / vol + + row_sums = inv_vol.sum(axis=1).replace(0, np.nan) + signals = inv_vol.div(row_sums, axis=0).fillna(0.0) + signals.iloc[:self.vol_window] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/mean_reversion.py b/strategies/mean_reversion.py new file mode 100644 index 0000000..031c4cc --- /dev/null +++ b/strategies/mean_reversion.py @@ -0,0 +1,56 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class MeanReversionStrategy(Strategy): + """ + Monthly "buy the dip" with momentum confirmation. + + Among stocks with positive 12-month momentum, overweight those + that dipped most in the past month. Rebalances monthly to keep + turnover low. Combines long-term trend (avoid losers) with + short-term mean reversion (buy winners on sale). + """ + + def __init__(self, mom_lookback: int = 252, dip_window: int = 21, + rebal_freq: int = 21, top_n: int = 20): + self.mom_lookback = mom_lookback + self.dip_window = dip_window + self.rebal_freq = rebal_freq + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + long_mom = data.pct_change(self.mom_lookback) + has_momentum = long_mom > 0 + + short_ret = data.pct_change(self.dip_window) + + # Among positive-momentum stocks, rank by biggest dip + scores = short_ret.where(has_momentum, np.nan) + + # Rank: most negative = rank 1 (biggest dip) + rank = scores.rank(axis=1, ascending=True, na_option="bottom") + n_valid = scores.notna().sum(axis=1) + effective_n = n_valid.clip(upper=self.top_n) + + top_mask = rank <= effective_n.values.reshape(-1, 1) + top_mask = top_mask & has_momentum # ensure we only pick momentum stocks + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + warmup = self.mom_lookback + self.dip_window + + # Only keep rebalance-day signals, forward-fill between them + rebal_mask = pd.Series(False, index=data.index) + rebal_indices = range(warmup, len(data), self.rebal_freq) + rebal_mask.iloc[list(rebal_indices)] = True + + # Zero out non-rebalance days then forward-fill + signals[~rebal_mask] = np.nan + signals = signals.ffill().fillna(0.0) + + signals.iloc[:warmup] = 0.0 + return signals.shift(1).fillna(0.0) diff --git a/strategies/momentum.py b/strategies/momentum.py new file mode 100644 index 0000000..659a532 --- /dev/null +++ b/strategies/momentum.py @@ -0,0 +1,37 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class MomentumStrategy(Strategy): + """ + Classic cross-sectional momentum strategy (Jegadeesh & Titman, 1993). + + Ranks assets by their past (lookback - skip) day return, skipping the most + recent `skip` days to avoid short-term mean reversion. Allocates equally + among the top_n winners each period. + + Default parameters approximate the 12-1 month academic factor. + """ + + def __init__(self, lookback: int = 252, skip: int = 21, top_n: int = 5): + self.lookback = lookback # ~12 months + self.skip = skip # ~1 month — skip to avoid reversal + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # Momentum: return from T-(lookback) to T-skip + momentum = data.shift(self.skip).pct_change(self.lookback - self.skip) + + n_valid = momentum.notna().sum(axis=1) + enough_data = n_valid >= self.top_n + + score_rank = momentum.rank(axis=1, ascending=False, na_option="bottom") + top_mask = (score_rank <= self.top_n) & enough_data.values.reshape(-1, 1) + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + signals.iloc[:self.lookback] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/momentum_quality.py b/strategies/momentum_quality.py new file mode 100644 index 0000000..f56581e --- /dev/null +++ b/strategies/momentum_quality.py @@ -0,0 +1,69 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class MomentumQualityStrategy(Strategy): + """ + Momentum + quality factor strategy using price-derived signals only. + + Quality proxy (from price data): + - Return consistency: fraction of positive monthly returns over past year + (stocks that grind up steadily are higher "quality" than volatile jumpers) + - Low max drawdown: smaller peak-to-trough drop = more stable + + Combined with momentum, this favors stocks with strong AND stable uptrends, + filtering out lottery-ticket stocks that spike then crash. + """ + + def __init__(self, momentum_period: int = 252, skip: int = 21, + quality_window: int = 252, top_n: int = 20): + self.momentum_period = momentum_period + self.skip = skip + self.quality_window = quality_window + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # --- Momentum factor --- + momentum = data.shift(self.skip).pct_change(self.momentum_period - self.skip) + + # --- Quality factor 1: return consistency --- + # Rolling 21-day (monthly) returns, then count fraction positive over past year + monthly_ret = data.pct_change(21) + consistency = monthly_ret.rolling(self.quality_window).apply( + lambda x: (x > 0).sum() / len(x), raw=True + ) + + # --- Quality factor 2: inverse max drawdown --- + # Smaller drawdown = higher quality + def rolling_max_dd(prices: pd.DataFrame, window: int) -> pd.DataFrame: + rolling_max = prices.rolling(window).max() + drawdown = prices / rolling_max - 1 + # Rolling worst drawdown (most negative) + worst_dd = drawdown.rolling(window).min() + # Invert: less negative = better, so negate + return -worst_dd # higher = smaller drawdown = better + inv_dd = rolling_max_dd(data, self.quality_window) + + # --- Cross-sectional ranking --- + mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") + con_rank = consistency.rank(axis=1, pct=True, na_option="bottom") + dd_rank = inv_dd.rank(axis=1, pct=True, na_option="bottom") + + # Composite: momentum 50%, consistency 25%, drawdown 25% + scores = 0.50 * mom_rank + 0.25 * con_rank + 0.25 * dd_rank + + # --- Select top_n --- + n_valid = scores.notna().sum(axis=1) + enough = n_valid >= self.top_n + score_rank = scores.rank(axis=1, ascending=False, na_option="bottom") + top_mask = (score_rank <= self.top_n) & enough.values.reshape(-1, 1) + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + warmup = max(self.momentum_period, self.quality_window) + 21 + signals.iloc[:warmup] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/multi_factor.py b/strategies/multi_factor.py new file mode 100644 index 0000000..eb26cdf --- /dev/null +++ b/strategies/multi_factor.py @@ -0,0 +1,77 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class MultiFactorStrategy(Strategy): + """ + Multi-factor strategy combining momentum and value signals with a market-timing filter. + + Factors: + - Momentum: past return from (momentum_period + skip) to skip days ago (avoids short-term reversal) + - Value: rolling min / current price (inverted price-to-low ratio — cheaper = higher score) + - Market timing: only invest when SPY is above its long-term moving average + + Signal generation is fully vectorized — no Python loops over time. + """ + + def __init__( + self, + tickers, + benchmark: str = "SPY", + window: int = 200, + momentum_period: int = 230, + momentum_skip: int = 20, + value_period: int = 250, + top_n: int = 5, + ): + self.tickers = list(tickers) + self.benchmark = benchmark + self.window = window + self.momentum_period = momentum_period + self.momentum_skip = momentum_skip + self.value_period = value_period + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + stock = data[self.tickers] + + # --- Market timing filter --- + spy_ma = data[self.benchmark].rolling(self.window).mean() + market_up = (data[self.benchmark] > spy_ma).values # shape (T,) + + # --- Momentum factor --- + # Return from T-(momentum_period+skip) to T-skip, avoiding the last month + momentum = stock.shift(self.momentum_skip).pct_change(self.momentum_period) + + # --- Value factor --- + # min_price_over_period / current_price (higher = more "undervalued" vs recent range) + value = stock.rolling(self.value_period).min() / stock + + # --- Cross-sectional ranking (each row ranked across assets) --- + mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") + val_rank = value.rank(axis=1, pct=True, na_option="bottom") + scores = mom_rank + val_rank # combined score, higher = better + + # --- Select top_n assets per row --- + # Only allocate rows that have enough valid scores + n_valid = scores.notna().sum(axis=1) + enough_data = n_valid >= self.top_n + + score_rank = scores.rank(axis=1, ascending=False, na_option="bottom") + top_mask = (score_rank <= self.top_n) & enough_data.values.reshape(-1, 1) + + # Equal-weight allocation among selected assets + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + # --- Apply market timing: zero out when SPY is below its MA --- + signals[~market_up] = 0.0 + + # --- Zero out warm-up period --- + warmup = max(self.window, self.momentum_period + self.momentum_skip, self.value_period) + signals.iloc[:warmup] = 0.0 + + # Shift by 1: signal computed at close of day t trades at open of day t+1 + return signals.shift(1).fillna(0.0) diff --git a/strategies/recovery_momentum.py b/strategies/recovery_momentum.py new file mode 100644 index 0000000..896f3e0 --- /dev/null +++ b/strategies/recovery_momentum.py @@ -0,0 +1,66 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class RecoveryMomentumStrategy(Strategy): + """ + Recovery + momentum composite factor strategy. + + Combines the two strongest predictive factors (by IC analysis): + 1. Recovery: price relative to 63-day low (how far stock has bounced + from its recent trough). Higher = stronger recovery trajectory. + 2. Long-term momentum: 12-1 month return. Avoids most recent month + to sidestep short-term reversal noise. + + Rank-combines both factors (50/50), selects top_n, equal-weights. + Rebalances monthly to control turnover. + + The recovery factor captures stocks in strong V-shaped rebounds — + a pattern that tends to persist. Combined with momentum, it filters + for stocks with both long-term strength AND recent acceleration. + """ + + def __init__(self, recovery_window: int = 63, mom_lookback: int = 252, + mom_skip: int = 21, rebal_freq: int = 21, top_n: int = 10): + self.recovery_window = recovery_window + self.mom_lookback = mom_lookback + self.mom_skip = mom_skip + self.rebal_freq = rebal_freq + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # Factor 1: Recovery — price / rolling min + recovery = data / data.rolling(self.recovery_window).min() - 1 + + # Factor 2: 12-1 month momentum + momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip) + + # Cross-sectional percentile ranks + rec_rank = recovery.rank(axis=1, pct=True, na_option="bottom") + mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") + + # Composite score (50/50) + composite = 0.5 * rec_rank + 0.5 * mom_rank + + # Select top_n + rank = composite.rank(axis=1, ascending=False, na_option="bottom") + n_valid = composite.notna().sum(axis=1) + enough = n_valid >= self.top_n + top_mask = (rank <= self.top_n) & enough.values.reshape(-1, 1) + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + # Monthly rebalance: keep only rebal-day signals, forward-fill + warmup = max(self.mom_lookback, self.recovery_window) + rebal_mask = pd.Series(False, index=data.index) + rebal_indices = list(range(warmup, len(data), self.rebal_freq)) + rebal_mask.iloc[rebal_indices] = True + + signals[~rebal_mask] = np.nan + signals = signals.ffill().fillna(0.0) + signals.iloc[:warmup] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/strategies/trend_following.py b/strategies/trend_following.py new file mode 100644 index 0000000..6a11e35 --- /dev/null +++ b/strategies/trend_following.py @@ -0,0 +1,51 @@ +import numpy as np +import pandas as pd +from strategies.base import Strategy + + +class TrendFollowingStrategy(Strategy): + """ + Per-stock trend following with momentum ranking. + + Two filters applied: + 1. Trend filter: only hold stocks trading above their own moving average + (individual uptrend, not market-level timing) + 2. Momentum rank: among trending stocks, pick the top_n by 6-month return + + This avoids the Multi-Factor problem of all-or-nothing market timing + while still providing downside protection at the individual stock level. + """ + + def __init__(self, ma_window: int = 150, momentum_period: int = 126, top_n: int = 20): + self.ma_window = ma_window + self.momentum_period = momentum_period + self.top_n = top_n + + def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame: + # Per-stock trend filter: price > MA + ma = data.rolling(self.ma_window).mean() + in_uptrend = data > ma + + # Momentum score among trending stocks + momentum = data.pct_change(self.momentum_period) + # Mask out stocks not in uptrend + momentum_filtered = momentum.where(in_uptrend, np.nan) + + # Rank and select top_n + n_valid = momentum_filtered.notna().sum(axis=1) + enough = n_valid >= 1 + + rank = momentum_filtered.rank(axis=1, ascending=False, na_option="bottom") + effective_n = n_valid.clip(upper=self.top_n) + top_mask = (rank <= effective_n.values.reshape(-1, 1)) & enough.values.reshape(-1, 1) + # Ensure we only pick stocks that are actually in uptrend + top_mask = top_mask & in_uptrend + + raw = top_mask.astype(float) + row_sums = raw.sum(axis=1).replace(0, np.nan) + signals = raw.div(row_sums, axis=0).fillna(0.0) + + warmup = max(self.ma_window, self.momentum_period) + signals.iloc[:warmup] = 0.0 + + return signals.shift(1).fillna(0.0) diff --git a/trader.py b/trader.py new file mode 100644 index 0000000..df71ee0 --- /dev/null +++ b/trader.py @@ -0,0 +1,816 @@ +""" +Daily trading simulation system. + +Subcommands: + morning — Download today's open prices, run strategy, output trade orders + evening — Download close prices, record execution, update portfolio + status — Print current portfolio, P&L, and recent trades + simulate — Replay a date range day-by-day (for forward testing) + +Usage: + uv run python trader.py morning --market us --strategy recovery_mom_top10 + uv run python trader.py evening --market us --strategy recovery_mom_top10 + uv run python trader.py status --market us --strategy recovery_mom_top10 + uv run python trader.py simulate --market us --strategy recovery_mom_top10 \\ + --start 2026-01-01 --end 2026-04-04 + +State is persisted in data/trader_{market}_{strategy}.json between runs. +""" + +import argparse +import json +import os +from datetime import datetime, timedelta + +import numpy as np +import pandas as pd +import yfinance as yf + +import data_manager +from strategies.buy_and_hold import BuyAndHoldStrategy +from strategies.dual_momentum import DualMomentumStrategy +from strategies.inverse_vol import InverseVolatilityStrategy +from strategies.momentum import MomentumStrategy +from strategies.momentum_quality import MomentumQualityStrategy +from strategies.recovery_momentum import RecoveryMomentumStrategy +from strategies.trend_following import TrendFollowingStrategy +from universe import UNIVERSES + +# --------------------------------------------------------------------------- +# Strategy registry +# --------------------------------------------------------------------------- + +STRATEGY_REGISTRY = { + "recovery_mom_top10": lambda **kw: RecoveryMomentumStrategy(top_n=10), + "recovery_mom_top20": lambda **kw: RecoveryMomentumStrategy(top_n=20), + "recovery_mom_top50": lambda **kw: RecoveryMomentumStrategy(top_n=50), + "momentum": lambda **kw: MomentumStrategy(lookback=252, skip=21, top_n=kw.get("top_n", 20)), + "momentum_quality": lambda **kw: MomentumQualityStrategy(top_n=kw.get("top_n", 20)), + "dual_momentum": lambda **kw: DualMomentumStrategy(top_n=kw.get("top_n", 20)), + "inverse_vol": lambda **kw: InverseVolatilityStrategy(vol_window=20), + "trend_following": lambda **kw: TrendFollowingStrategy(top_n=kw.get("top_n", 20)), + "buy_and_hold": lambda **kw: BuyAndHoldStrategy(), +} + + +# --------------------------------------------------------------------------- +# Persistent state +# --------------------------------------------------------------------------- + +def _state_path(market: str, strategy_name: str) -> str: + return os.path.join("data", f"trader_{market}_{strategy_name}.json") + + +def load_state(market: str, strategy_name: str) -> dict: + path = _state_path(market, strategy_name) + if os.path.exists(path): + with open(path) as f: + return json.load(f) + return {} + + +def save_state(state: dict, market: str, strategy_name: str) -> None: + os.makedirs("data", exist_ok=True) + path = _state_path(market, strategy_name) + with open(path, "w") as f: + json.dump(state, f, indent=2, default=str) + + +def init_state(market: str, strategy_name: str, capital: float) -> dict: + return { + "strategy": strategy_name, + "market": market, + "initial_capital": capital, + "cash": capital, + "holdings": {}, # ticker -> shares (float) + "pending_trades": None, # set by morning, consumed by evening + "trade_log": [], # list of trade dicts + "daily_equity": {}, # date_str -> portfolio value + "last_morning": None, + "last_evening": None, + } + + +# --------------------------------------------------------------------------- +# Core logic +# --------------------------------------------------------------------------- + +def portfolio_value(holdings: dict, prices: dict, cash: float) -> float: + stock_value = sum(holdings.get(t, 0) * prices.get(t, 0) for t in holdings) + return cash + stock_value + + +def generate_target_weights(strategy, open_data: pd.DataFrame, target_date) -> dict: + """ + Run strategy on full open price history, extract target_date's weights. + + Strategies have an internal shift(1) designed for close prices: weights[t] + uses data up to t-1. With open prices (observable same-day), we want the + signal that uses data up to today. To get this, we append a dummy future + row so the strategy computes one extra day of signals, then we read that + last row — which incorporates today's open prices. + """ + # Append a dummy row one day after the last date so the strategy's + # shift(1) produces a valid signal row that uses target_date's open + dummy_date = open_data.index[-1] + pd.Timedelta(days=1) + dummy_row = pd.DataFrame( + [open_data.iloc[-1].values], + index=[dummy_date], + columns=open_data.columns, + ) + extended = pd.concat([open_data, dummy_row]) + + weights = strategy.generate_signals(extended) + + # The dummy_date row in weights now uses data up to target_date (the real last day) + # Read the dummy row — that's today's actual signal + if dummy_date in weights.index: + row = weights.loc[dummy_date] + elif target_date in weights.index: + # Fallback: use target_date row (which uses data up to target_date - 1) + row = weights.loc[target_date] + else: + idx = weights.index.get_indexer([target_date], method="ffill") + if idx[0] >= 0: + row = weights.iloc[idx[0]] + else: + return {} + + # Return only non-zero weights + return {t: float(w) for t, w in row.items() if w > 1e-6} + + +def compute_trades(holdings: dict, cash: float, target_weights: dict, + prices: dict, min_trade_value: float = 50.0) -> list[dict]: + """ + Compute trades needed to move from current holdings to target weights. + + Returns list of {ticker, shares_delta, direction, est_value}. + """ + total = portfolio_value(holdings, prices, cash) + trades = [] + + all_tickers = set(list(target_weights.keys()) + list(holdings.keys())) + for ticker in sorted(all_tickers): + target_weight = target_weights.get(ticker, 0.0) + price = prices.get(ticker, 0.0) + if price <= 0: + continue + + target_value = total * target_weight + target_shares = target_value / price + current_shares = holdings.get(ticker, 0.0) + delta = target_shares - current_shares + + trade_value = abs(delta * price) + if trade_value < min_trade_value: + continue + + trades.append({ + "ticker": ticker, + "shares_delta": round(delta, 4), + "direction": "BUY" if delta > 0 else "SELL", + "est_value": round(trade_value, 2), + "price": round(price, 2), + "target_shares": round(target_shares, 4), + "current_shares": round(current_shares, 4), + }) + + return trades + + +def execute_trades(state: dict, trades: list[dict], prices: dict, + tx_cost: float = 0.001, fixed_fee: float = 0.0, + trade_date: str = "") -> None: + """Execute trades: update holdings and cash in state, append to trade_log.""" + holdings = state["holdings"] + cash = state["cash"] + + for trade in trades: + ticker = trade["ticker"] + delta = trade["shares_delta"] + price = prices.get(ticker, trade["price"]) + cost = abs(delta * price) + commission = cost * tx_cost + fixed_fee + + if delta > 0: + # BUY + cash -= (cost + commission) + holdings[ticker] = holdings.get(ticker, 0.0) + delta + else: + # SELL + cash += (cost - commission) + holdings[ticker] = holdings.get(ticker, 0.0) + delta # delta is negative + + # Remove zero holdings + if ticker in holdings and abs(holdings[ticker]) < 0.001: + del holdings[ticker] + + state["trade_log"].append({ + "date": trade_date, + "action": trade["direction"], + "ticker": ticker, + "shares": round(abs(delta), 4), + "price": round(price, 2), + "value": round(cost, 2), + "commission": round(commission, 2), + }) + + state["cash"] = round(cash, 2) + state["holdings"] = {k: round(v, 4) for k, v in holdings.items() if abs(v) >= 0.001} + + +def get_prices_for_date(tickers: list[str], date_idx, price_df: pd.DataFrame) -> dict: + """Extract prices for specific tickers on a given date from a DataFrame.""" + if date_idx in price_df.index: + row = price_df.loc[date_idx] + else: + idx = price_df.index.get_indexer([date_idx], method="ffill") + if idx[0] >= 0: + row = price_df.iloc[idx[0]] + else: + return {} + return {t: float(row[t]) for t in tickers if t in row.index and pd.notna(row[t])} + + +# --------------------------------------------------------------------------- +# Commands +# --------------------------------------------------------------------------- + +def cmd_morning(args): + """Morning: download open prices, generate today's trade orders.""" + market = args.market + strategy_name = args.strategy + universe = UNIVERSES[market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + all_tickers = sorted(set(tickers + [benchmark])) + + # Load or init state + state = load_state(market, strategy_name) + if not state: + state = init_state(market, strategy_name, args.capital) + print(f"--- Initialized new portfolio: ${args.capital:,.0f} cash ---") + + # Download data (close + open) + close_data, open_data = data_manager.update(market, all_tickers, with_open=True) + tickers = [t for t in tickers if t in close_data.columns] + + today = open_data.index[-1] + today_str = str(today.date()) + + if state["last_morning"] == today_str: + print(f"Morning already run for {today_str}. Showing pending trades.\n") + + print(f"\n{'='*60}") + print(f" MORNING SIGNAL — {today_str}") + print(f" Strategy: {strategy_name} | Market: {market.upper()}") + print(f"{'='*60}") + + # Run strategy on open prices + strategy = STRATEGY_REGISTRY[strategy_name](top_n=max(5, len(tickers) // 10)) + target_weights = generate_target_weights(strategy, open_data[tickers], today) + + # Get today's open prices for trade computation + open_prices = get_prices_for_date( + list(set(list(target_weights.keys()) + list(state["holdings"].keys()))), + today, open_data + ) + + total = portfolio_value(state["holdings"], open_prices, state["cash"]) + trades = compute_trades(state["holdings"], state["cash"], target_weights, + open_prices, min_trade_value=max(50, total * 0.001)) + + # Store pending + state["pending_trades"] = { + "date": today_str, + "target_weights": target_weights, + "trades": trades, + } + state["last_morning"] = today_str + save_state(state, market, strategy_name) + + # Print summary + print(f"\n Portfolio value (at open): ${total:,.2f}") + print(f" Cash: ${state['cash']:,.2f}") + print(f" Target positions: {len(target_weights)}") + print(f" Trades needed: {len(trades)}") + + if target_weights: + print(f"\n {'Target Weights':}") + print(f" {'Ticker':<8} {'Weight':>8} {'Open Price':>12}") + print(f" {'-'*30}") + for t, w in sorted(target_weights.items(), key=lambda x: -x[1]): + p = open_prices.get(t, 0) + print(f" {t:<8} {w:>7.1%} {p:>11.2f}") + + if trades: + print(f"\n {'Trade Orders (execute at close)':}") + print(f" {'Action':<6} {'Ticker':<8} {'Shares':>10} {'~Value':>12} {'Open':>10}") + print(f" {'-'*48}") + total_buy = 0 + total_sell = 0 + for t in trades: + print(f" {t['direction']:<6} {t['ticker']:<8} {t['shares_delta']:>+10.2f} " + f"${t['est_value']:>10,.2f} {t['price']:>10.2f}") + if t["direction"] == "BUY": + total_buy += t["est_value"] + else: + total_sell += t["est_value"] + print(f"\n Total buys: ${total_buy:>12,.2f}") + print(f" Total sells: ${total_sell:>12,.2f}") + print(f" Net flow: ${total_sell - total_buy:>+12,.2f}") + else: + print("\n No trades needed today.") + + print() + + +def cmd_evening(args): + """Evening: record execution at close prices, update portfolio.""" + market = args.market + strategy_name = args.strategy + universe = UNIVERSES[market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + all_tickers = sorted(set(tickers + [benchmark])) + + state = load_state(market, strategy_name) + if not state: + print("No state found. Run 'morning' first.") + return + + if not state.get("pending_trades"): + print("No pending trades. Run 'morning' first.") + return + + pending = state["pending_trades"] + trade_date = pending["date"] + + if state["last_evening"] == trade_date: + print(f"Evening already recorded for {trade_date}.") + return + + # Get close prices + close_data = data_manager.update(market, all_tickers) + tickers = [t for t in tickers if t in close_data.columns] + + target_date = pd.Timestamp(trade_date) + all_held = list(set( + list(pending.get("target_weights", {}).keys()) + + list(state["holdings"].keys()) + )) + close_prices = get_prices_for_date(all_held, target_date, close_data) + + # Recompute trades at close prices for accurate execution + trades = pending.get("trades", []) + + print(f"\n{'='*60}") + print(f" EVENING EXECUTION — {trade_date}") + print(f" Strategy: {strategy_name} | Market: {market.upper()}") + print(f"{'='*60}") + + pre_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + print(f"\n Pre-trade value: ${pre_value:,.2f}") + + # Recompute trades with close prices for execution + target_weights = pending.get("target_weights", {}) + exec_trades = compute_trades( + state["holdings"], state["cash"], target_weights, + close_prices, min_trade_value=max(50, pre_value * 0.001) + ) + + execute_trades(state, exec_trades, close_prices, + tx_cost=args.tx_cost, fixed_fee=args.fixed_fee, + trade_date=trade_date) + + post_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + state["daily_equity"][trade_date] = round(post_value, 2) + state["pending_trades"] = None + state["last_evening"] = trade_date + save_state(state, market, strategy_name) + + n_executed = len(exec_trades) + total_commission = sum(t.get("commission", 0) + for t in state["trade_log"][-n_executed:]) if n_executed else 0 + + print(f" Trades executed: {n_executed}") + print(f" Total commission: ${total_commission:,.2f}") + print(f" Post-trade value: ${post_value:,.2f}") + print(f" Cash remaining: ${state['cash']:,.2f}") + print(f" Holdings: {len(state['holdings'])} positions") + + if exec_trades: + print(f"\n {'Executed':}") + print(f" {'Action':<6} {'Ticker':<8} {'Shares':>10} {'Close':>10} {'Value':>12}") + print(f" {'-'*48}") + for t in exec_trades: + p = close_prices.get(t["ticker"], t["price"]) + v = abs(t["shares_delta"]) * p + print(f" {t['direction']:<6} {t['ticker']:<8} {t['shares_delta']:>+10.2f} " + f"{p:>10.2f} ${v:>10,.2f}") + + pnl = post_value - state["initial_capital"] + pnl_pct = pnl / state["initial_capital"] * 100 + print(f"\n P&L since inception: ${pnl:>+,.2f} ({pnl_pct:>+.2f}%)") + print() + + +def cmd_status(args): + """Print current portfolio status.""" + market = args.market + strategy_name = args.strategy + + state = load_state(market, strategy_name) + if not state: + print("No state found. Run 'morning' to initialize.") + return + + # Get latest prices + universe = UNIVERSES[market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + all_tickers = sorted(set(tickers + [benchmark])) + close_data = data_manager.update(market, all_tickers) + + last_date = close_data.index[-1] + all_held = list(state["holdings"].keys()) + prices = get_prices_for_date(all_held + [benchmark], last_date, close_data) + + total = portfolio_value(state["holdings"], prices, state["cash"]) + pnl = total - state["initial_capital"] + + print(f"\n{'='*60}") + print(f" PORTFOLIO STATUS") + print(f" Strategy: {strategy_name} | Market: {market.upper()}") + print(f" Last data: {last_date.date()}") + print(f"{'='*60}") + + print(f"\n Initial capital: ${state['initial_capital']:>12,.2f}") + print(f" Current value: ${total:>12,.2f}") + print(f" Cash: ${state['cash']:>12,.2f}") + print(f" P&L: ${pnl:>+12,.2f} ({pnl/state['initial_capital']*100:>+.2f}%)") + print(f" Last morning: {state.get('last_morning', 'N/A')}") + print(f" Last evening: {state.get('last_evening', 'N/A')}") + + if state["holdings"]: + print(f"\n {'Holdings':}") + print(f" {'Ticker':<8} {'Shares':>10} {'Price':>10} {'Value':>12} {'Weight':>8}") + print(f" {'-'*52}") + stock_value = total - state["cash"] + for ticker, shares in sorted(state["holdings"].items(), + key=lambda x: -x[1] * prices.get(x[0], 0)): + p = prices.get(ticker, 0) + v = shares * p + w = v / total if total > 0 else 0 + print(f" {ticker:<8} {shares:>10.2f} {p:>10.2f} ${v:>10,.2f} {w:>7.1%}") + print(f" {'Cash':<8} {'':>10} {'':>10} ${state['cash']:>10,.2f} " + f"{state['cash']/total*100 if total > 0 else 0:>6.1f}%") + else: + print("\n No holdings (100% cash)") + + # Equity curve + if state.get("daily_equity"): + eq = state["daily_equity"] + dates = sorted(eq.keys()) + print(f"\n {'Equity History (last 10 days)':}") + print(f" {'Date':<12} {'Value':>12} {'Daily':>8}") + print(f" {'-'*34}") + for i, d in enumerate(dates[-10:]): + v = eq[d] + prev = eq[dates[dates.index(d) - 1]] if dates.index(d) > 0 else state["initial_capital"] + daily_ret = (v / prev - 1) * 100 + print(f" {d:<12} ${v:>10,.2f} {daily_ret:>+7.2f}%") + + # Recent trades + log = state.get("trade_log", []) + if log: + recent = log[-15:] + print(f"\n {'Recent Trades (last 15)':}") + print(f" {'Date':<12} {'Action':<6} {'Ticker':<8} {'Shares':>8} {'Price':>10} {'Value':>10}") + print(f" {'-'*58}") + for t in recent: + print(f" {t['date']:<12} {t['action']:<6} {t['ticker']:<8} " + f"{t['shares']:>8.2f} {t['price']:>10.2f} ${t['value']:>8,.2f}") + + print() + + +def cmd_simulate(args): + """Simulate day-by-day over a date range.""" + market = args.market + strategy_name = args.strategy + universe = UNIVERSES[market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + all_tickers = sorted(set(tickers + [benchmark])) + + # Load both open and close data + close_data, open_data = data_manager.update(market, all_tickers, with_open=True) + tickers = [t for t in tickers if t in close_data.columns] + + # Date range + start = pd.Timestamp(args.start) + end = pd.Timestamp(args.end) + trading_days = close_data.index[(close_data.index >= start) & (close_data.index <= end)] + + if len(trading_days) == 0: + print(f"No trading days found between {args.start} and {args.end}") + return + + # Fresh state for simulation + state = init_state(market, f"sim_{strategy_name}", args.capital) + + # Pre-compute strategy weights for the full period (much faster than per-day) + # Append dummy row so shift(1) inside strategy produces valid last-day signal + strategy = STRATEGY_REGISTRY[strategy_name](top_n=max(5, len(tickers) // 10)) + open_tickers = open_data[tickers] + dummy_date = open_tickers.index[-1] + pd.Timedelta(days=1) + dummy_row = pd.DataFrame( + [open_tickers.iloc[-1].values], index=[dummy_date], columns=open_tickers.columns + ) + extended = pd.concat([open_tickers, dummy_row]) + raw_weights = strategy.generate_signals(extended) + # Strategy's shift(1): raw_weights[t] uses data up to t-1 + # For date t, the signal using open[t] is in raw_weights[t+1] + # So shift(-1) to align: full_weights[t] = signal using open[t] + full_weights = raw_weights.shift(-1).fillna(0.0) + + print(f"\n{'='*70}") + print(f" SIMULATION: {strategy_name} | {market.upper()}") + print(f" Period: {trading_days[0].date()} to {trading_days[-1].date()} " + f"({len(trading_days)} trading days)") + print(f" Capital: ${args.capital:,.0f}") + print(f"{'='*70}\n") + + total_trades = 0 + total_commission = 0.0 + + for i, day in enumerate(trading_days): + day_str = str(day.date()) + + # Morning: get target weights from pre-computed matrix + if day in full_weights.index: + row = full_weights.loc[day] + target_weights = {t: float(w) for t, w in row.items() if w > 1e-6} + else: + target_weights = {} + + # Get open and close prices + all_held = list(set(list(target_weights.keys()) + list(state["holdings"].keys()))) + open_prices = get_prices_for_date(all_held, day, open_data) + close_prices = get_prices_for_date(all_held + [benchmark], day, close_data) + + if not close_prices: + continue + + # Compute portfolio value before trades (at close, reflecting overnight changes) + pre_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + + # Compute and execute trades at close prices + trades = compute_trades( + state["holdings"], state["cash"], target_weights, + close_prices, min_trade_value=max(50, pre_value * 0.001) + ) + + execute_trades(state, trades, close_prices, + tx_cost=args.tx_cost, fixed_fee=args.fixed_fee, + trade_date=day_str) + + post_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + state["daily_equity"][day_str] = round(post_value, 2) + + day_trades = len(trades) + day_commission = sum( + t.get("commission", 0) for t in state["trade_log"][-day_trades:] + ) if day_trades else 0 + total_trades += day_trades + total_commission += day_commission + + # Progress: print weekly or on trade days + if day_trades > 0 or i == 0 or i == len(trading_days) - 1: + prev_date = dates[-1] if (dates := sorted(state["daily_equity"].keys())[:-1]) else None + prev_val = state["daily_equity"].get(prev_date, args.capital) if prev_date else args.capital + daily_ret = (post_value / prev_val - 1) * 100 if prev_val > 0 else 0 + print(f" {day_str} ${post_value:>12,.2f} {daily_ret:>+7.2f}% " + f"trades: {day_trades:>2} positions: {len(state['holdings']):>2}") + + # Final summary + equity = state["daily_equity"] + dates = sorted(equity.keys()) + final = equity[dates[-1]] + initial = args.capital + total_ret = (final / initial - 1) * 100 + n_days = len(dates) + ann_ret = ((final / initial) ** (252 / n_days) - 1) * 100 if n_days > 0 else 0 + + # Max drawdown + values = [equity[d] for d in dates] + peak = values[0] + max_dd = 0 + for v in values: + peak = max(peak, v) + dd = (v - peak) / peak + max_dd = min(max_dd, dd) + + # Daily returns for Sharpe + daily_rets = [] + for i in range(1, len(values)): + daily_rets.append(values[i] / values[i - 1] - 1) + daily_rets = np.array(daily_rets) if daily_rets else np.array([0]) + sharpe = (daily_rets.mean() / daily_rets.std() * np.sqrt(252)) if daily_rets.std() > 0 else 0 + + print(f"\n{'='*70}") + print(f" SIMULATION RESULTS") + print(f"{'='*70}") + print(f" Initial capital: ${initial:>12,.2f}") + print(f" Final value: ${final:>12,.2f}") + print(f" Total return: {total_ret:>+11.2f}%") + print(f" Annualized return: {ann_ret:>+11.2f}%") + print(f" Sharpe ratio: {sharpe:>11.2f}") + print(f" Max drawdown: {max_dd * 100:>11.2f}%") + print(f" Total trades: {total_trades:>11}") + print(f" Total commission: ${total_commission:>11,.2f}") + print(f" Trading days: {n_days:>11}") + + # Save state for review + state["strategy"] = strategy_name + state["market"] = market + save_state(state, market, f"sim_{strategy_name}") + print(f"\n State saved to: {_state_path(market, f'sim_{strategy_name}')}") + + # Benchmark comparison + bench_start = close_data[benchmark].loc[trading_days[0]] + bench_end = close_data[benchmark].loc[trading_days[-1]] + bench_ret = (bench_end / bench_start - 1) * 100 + print(f"\n Benchmark ({benchmark}): {bench_ret:>+.2f}%") + print(f" Alpha: {total_ret - bench_ret:>+.2f}%") + print() + + +def cmd_auto(args): + """ + Automated daily run — single invocation handles both morning + evening. + + Designed for cron/systemd: run once after market close each trading day. + Downloads open + close prices for today, generates signals, executes, records. + + Usage with cron (US market, run at 5pm ET weekdays): + 0 17 * * 1-5 cd /path/to/quant && uv run python trader.py auto --market us + For CN market (run at 4pm CST): + 0 16 * * 1-5 cd /path/to/quant && uv run python trader.py auto --market cn + """ + import time + + market = args.market + strategy_name = args.strategy + universe = UNIVERSES[market] + tickers = universe["fetch"]() + benchmark = universe["benchmark"] + all_tickers = sorted(set(tickers + [benchmark])) + + # Load or init state + state = load_state(market, strategy_name) + if not state: + state = init_state(market, strategy_name, args.capital) + print(f"[auto] Initialized new portfolio: ${args.capital:,.0f} cash") + + # Download data (close + open) + close_data, open_data = data_manager.update(market, all_tickers, with_open=True) + tickers = [t for t in tickers if t in close_data.columns] + + today = close_data.index[-1] + today_str = str(today.date()) + + # Skip if already processed today + if state.get("last_evening") == today_str: + print(f"[auto] {today_str} already processed. Nothing to do.") + return + + print(f"\n[auto] {'='*55}") + print(f"[auto] {today_str} | {strategy_name} | {market.upper()}") + print(f"[auto] {'='*55}") + + # --- MORNING PHASE: Generate target weights from open prices --- + strategy = STRATEGY_REGISTRY[strategy_name](top_n=max(5, len(tickers) // 10)) + target_weights = generate_target_weights(strategy, open_data[tickers], today) + + all_held = list(set(list(target_weights.keys()) + list(state["holdings"].keys()))) + close_prices = get_prices_for_date(all_held + [benchmark], today, close_data) + + if not close_prices: + print(f"[auto] No close prices for {today_str}. Market likely closed.") + return + + pre_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + print(f"[auto] Pre-trade value: ${pre_value:,.2f}") + print(f"[auto] Target positions: {len(target_weights)}") + + # --- EVENING PHASE: Execute trades at close prices --- + trades = compute_trades( + state["holdings"], state["cash"], target_weights, + close_prices, min_trade_value=max(50, pre_value * 0.001) + ) + + execute_trades(state, trades, close_prices, + tx_cost=args.tx_cost, fixed_fee=args.fixed_fee, + trade_date=today_str) + + post_value = portfolio_value(state["holdings"], close_prices, state["cash"]) + state["daily_equity"][today_str] = round(post_value, 2) + state["last_morning"] = today_str + state["last_evening"] = today_str + state["pending_trades"] = None + save_state(state, market, strategy_name) + + # Print summary + pnl = post_value - state["initial_capital"] + pnl_pct = pnl / state["initial_capital"] * 100 + n_trades = len(trades) + commission = sum(t.get("commission", 0) for t in state["trade_log"][-n_trades:]) if n_trades else 0 + + print(f"[auto] Trades: {n_trades} | Commission: ${commission:,.2f}") + print(f"[auto] Post-trade value: ${post_value:,.2f} | Cash: ${state['cash']:,.2f}") + print(f"[auto] P&L: ${pnl:>+,.2f} ({pnl_pct:>+.2f}%)") + print(f"[auto] Holdings: {len(state['holdings'])} positions") + + if trades: + for t in trades: + p = close_prices.get(t["ticker"], t["price"]) + print(f"[auto] {t['direction']:<4} {t['ticker']:<8} {t['shares_delta']:>+10.2f} @ {p:.2f}") + + # Benchmark + bench_eq = state["daily_equity"] + dates = sorted(bench_eq.keys()) + if len(dates) >= 2: + prev_val = bench_eq[dates[-2]] + daily_ret = (post_value / prev_val - 1) * 100 + print(f"[auto] Daily return: {daily_ret:>+.2f}%") + + print(f"[auto] State saved: {_state_path(market, strategy_name)}") + print() + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser(description="Daily trading simulation") + sub = parser.add_subparsers(dest="command", required=True) + + # Common args + def add_common(p): + p.add_argument("--market", choices=UNIVERSES.keys(), default="us") + p.add_argument("--strategy", choices=STRATEGY_REGISTRY.keys(), + default="recovery_mom_top10") + p.add_argument("--capital", type=float, default=100_000) + p.add_argument("--tx-cost", type=float, default=0.001, + help="Proportional transaction cost (default: 0.001 = 10bps)") + p.add_argument("--fixed-fee", type=float, default=0.0, + help="Fixed dollar fee per trade") + + # Morning + p_morning = sub.add_parser("morning", help="Generate trade orders from open prices") + add_common(p_morning) + + # Evening + p_evening = sub.add_parser("evening", help="Record execution at close prices") + add_common(p_evening) + + # Auto (single daily run — for cron/systemd) + p_auto = sub.add_parser("auto", + help="Automated daily run: signal + execute in one step (for cron)") + add_common(p_auto) + + # Status (strategy is a free-form string to allow sim_ prefixed names) + p_status = sub.add_parser("status", help="Show current portfolio") + p_status.add_argument("--market", choices=UNIVERSES.keys(), default="us") + p_status.add_argument("--strategy", default="recovery_mom_top10", + help="Strategy name (or sim_ for simulation state)") + p_status.add_argument("--capital", type=float, default=100_000) + p_status.add_argument("--tx-cost", type=float, default=0.001) + p_status.add_argument("--fixed-fee", type=float, default=0.0) + + # Simulate + p_sim = sub.add_parser("simulate", help="Simulate over a date range") + add_common(p_sim) + p_sim.add_argument("--start", required=True, help="Start date (YYYY-MM-DD)") + p_sim.add_argument("--end", required=True, help="End date (YYYY-MM-DD)") + + args = parser.parse_args() + + if args.command == "morning": + cmd_morning(args) + elif args.command == "evening": + cmd_evening(args) + elif args.command == "auto": + cmd_auto(args) + elif args.command == "status": + cmd_status(args) + elif args.command == "simulate": + cmd_simulate(args) + + +if __name__ == "__main__": + main() diff --git a/universe.py b/universe.py new file mode 100644 index 0000000..0d6e5be --- /dev/null +++ b/universe.py @@ -0,0 +1,135 @@ +""" +Stock universe providers — fetch index constituents for backtesting. + +Each function returns a dict with 'tickers' (list[str]) and 'benchmark' (str) +compatible with Yahoo Finance ticker format. Results are cached locally so +Wikipedia / web scrapes only happen once per day. +""" + +import io +import json +import os +import urllib.request +from datetime import date + +import pandas as pd + +_HEADERS = {"User-Agent": "Mozilla/5.0 (quant-backtest)"} + + +def _fetch_html_tables(url: str) -> list[pd.DataFrame]: + """Fetch HTML tables from a URL with a proper User-Agent.""" + req = urllib.request.Request(url, headers=_HEADERS) + with urllib.request.urlopen(req) as resp: + html = resp.read().decode("utf-8") + return pd.read_html(io.StringIO(html)) + +CACHE_DIR = "data" + + +def _read_cache(name: str) -> list[str] | None: + path = os.path.join(CACHE_DIR, f"universe_{name}.json") + if not os.path.exists(path): + return None + try: + with open(path) as f: + data = json.load(f) + if data.get("date") == str(date.today()): + return data["tickers"] + except Exception: + pass + return None + + +def _write_cache(name: str, tickers: list[str]) -> None: + os.makedirs(CACHE_DIR, exist_ok=True) + path = os.path.join(CACHE_DIR, f"universe_{name}.json") + with open(path, "w") as f: + json.dump({"date": str(date.today()), "tickers": tickers}, f) + + +# --------------------------------------------------------------------------- +# S&P 500 +# --------------------------------------------------------------------------- + +def get_sp500() -> list[str]: + """Fetch current S&P 500 constituents from Wikipedia.""" + cached = _read_cache("sp500") + if cached: + print(f"--- Loaded S&P 500 universe from cache ({len(cached)} tickers) ---") + return cached + + print("--- Fetching S&P 500 constituents from Wikipedia ---") + url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies" + tables = _fetch_html_tables(url) + df = tables[0] + tickers = sorted(df["Symbol"].str.replace(".", "-", regex=False).tolist()) + _write_cache("sp500", tickers) + print(f"--- Got {len(tickers)} S&P 500 tickers ---") + return tickers + + +# --------------------------------------------------------------------------- +# CSI 300 +# --------------------------------------------------------------------------- + +# Yahoo Finance format: Shanghai → XXXXXX.SS, Shenzhen → XXXXXX.SZ +# Wikipedia CSI 300 page has a table with stock codes and exchanges. + +def get_csi300() -> list[str]: + """Fetch current CSI 300 constituents from Wikipedia.""" + cached = _read_cache("csi300") + if cached: + print(f"--- Loaded CSI 300 universe from cache ({len(cached)} tickers) ---") + return cached + + print("--- Fetching CSI 300 constituents from Wikipedia ---") + url = "https://en.wikipedia.org/wiki/CSI_300_Index" + tables = _fetch_html_tables(url) + + # Find the constituent table — it has ~300 rows and a "Ticker" column + # with format like "SSE: 600519" or "SZSE: 300750" + df = None + for t in tables: + if len(t) >= 200 and "Ticker" in t.columns: + df = t + break + + if df is None: + raise RuntimeError("Could not find CSI 300 constituent table on Wikipedia") + + tickers = [] + for _, row in df.iterrows(): + raw = str(row["Ticker"]) # e.g. "SSE: 600519" or "SZSE: 300750" + parts = raw.split(":") + if len(parts) != 2: + continue + exchange_prefix = parts[0].strip().upper() + code = parts[1].strip() + if not code.isdigit(): + continue + suffix = ".SS" if exchange_prefix == "SSE" else ".SZ" + tickers.append(code + suffix) + + tickers = sorted(set(tickers)) + _write_cache("csi300", tickers) + print(f"--- Got {len(tickers)} CSI 300 tickers ---") + return tickers + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +UNIVERSES = { + "us": { + "fetch": get_sp500, + "benchmark": "SPY", + "benchmark_label": "SPY (Benchmark)", + }, + "cn": { + "fetch": get_csi300, + "benchmark": "000300.SS", + "benchmark_label": "CSI 300 (Benchmark)", + }, +} diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..e86a37d 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