Merge pull request #16 from cvxgrp/add-backtest

Add backtest

.gitignore

@@ -1,5 +1,9 @@
 .idea
 .vscode
+.python-version
+
+# all .DS_Store files
+**/.DS_Store
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -1,8 +1,9 @@
 # Markowitz Reference Implementation
 
 This repository accompanies the paper \cite{X}.
-It contains a reference implementation of the
-Markowitz portfolio optimization problem and the data used in the paper.
+It contains a reference implementation of the Markowitz portfolio optimization
+problem and the data used in the paper. Please note that the tickers of the
+stocks have been obfuscated to comply with the data provider's terms of use.
 
 ## Installation
 

experiments/backtest.py

@@ -0,0 +1,198 @@
+from dataclasses import dataclass
+from functools import lru_cache
+from pathlib import Path
+import pickle
+import sys
+from typing import Callable
+import numpy as np
+import pandas as pd
+
+# hack to allow importing from parent directory without having a package
+sys.path.append(str(Path(__file__).parent.parent))
+
+
+def data_folder():
+    return Path(__file__).parent.parent / "data"
+
+
+@lru_cache(maxsize=1)
+def load_data() -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    prices = pd.read_csv(data_folder() / "prices.csv", index_col=0, parse_dates=True)
+    spread = pd.read_csv(
+        data_folder() / "spreads.csv", index_col=0, parse_dates=True
+    ).fillna(0.10)
+    volume = pd.read_csv(data_folder() / "volumes.csv", index_col=0, parse_dates=True)
+    return prices, spread, volume
+
+
+@dataclass
+class OptimizationInput:
+    """
+    At time t, we have data from t-lookback to t-1.
+    """
+
+    prices: pd.DataFrame
+    spread: pd.DataFrame
+    volume: pd.DataFrame
+    quantities: np.ndarray
+    cash: float
+    risk_target: float
+
+
+def run_backtest(
+    strategy: Callable, risk_target: float, verbose: bool = False
+) -> tuple[pd.Series, pd.DataFrame]:
+    """
+    Run a simplified backtest for a given strategy.
+    At time t we use data from t-500 to t-1 to forecast the data and
+    compute the optimal portfolio weights and cash holdings.
+    We then trade to these weights at time t.
+    """
+
+    prices, spread, volume = load_data()
+    n_assets = prices.shape[1]
+
+    lookback = 500
+
+    quantities = np.zeros(n_assets)
+    cash = 1e6
+
+    post_trade_cash = []
+    post_trade_quantities = []
+
+    for day in range(lookback, len(prices)):
+        if verbose:
+            print(f"Day {day} of {len(prices)-1}, {prices.index[day]}")
+
+        prices_t = prices.iloc[day - lookback : day]  # Up to t-1
+        spread_t = spread.iloc[day - lookback : day]
+        volume_t = volume.iloc[day - lookback : day]
+
+        inputs_t = OptimizationInput(
+            prices_t, spread_t, volume_t, quantities, cash, risk_target
+        )
+        w, _ = strategy(inputs_t)
+
+        latest_prices = prices.iloc[day]  # At t
+        latest_spread = spread.iloc[day]
+
+        cash += interest_and_fees(cash, quantities)
+        trade_quantities = create_orders(w, quantities, cash, latest_prices)
+        quantities += trade_quantities
+        cash += execute_orders(latest_prices, trade_quantities, latest_spread)
+
+        post_trade_cash.append(cash)
+        post_trade_quantities.append(quantities)
+
+    post_trade_cash = pd.Series(post_trade_cash, index=prices.index[lookback:])
+    post_trade_quantities = pd.DataFrame(
+        post_trade_quantities, index=prices.index[lookback:], columns=prices.columns
+    )
+    return BacktestResult(post_trade_cash, post_trade_quantities, risk_target)
+
+
+def create_orders(w, quantities, cash, latest_prices) -> np.array:
+    portfolio_value = cash + quantities @ latest_prices
+    w_prev = (quantities * latest_prices) / portfolio_value
+
+    z = w - w_prev
+    trades = z * portfolio_value
+    trade_quantities = trades / latest_prices
+    return trade_quantities.values
+
+
+def execute_orders(latest_prices, trade_quantities, latest_spread) -> float:
+    sell_order_quantities = np.clip(trade_quantities, None, 0)
+    buy_order_quantities = np.clip(trade_quantities, 0, None)
+
+    sell_order_prices = latest_prices * (1 - latest_spread / 2 / 100)
+    buy_order_prices = latest_prices * (1 + latest_spread / 2 / 100)
+
+    sell_receipt = -sell_order_quantities @ sell_order_prices
+    buy_payment = buy_order_quantities @ buy_order_prices
+
+    return sell_receipt - buy_payment
+
+
+def interest_and_fees(cash, quantities) -> float:
+    # TODO: add risk free rate, borrow rate, shorting fees
+    return 0
+
+
+@dataclass
+class BacktestResult:
+    cash: pd.Series
+    quantities: pd.DataFrame
+    risk_target: float
+
+    @property
+    def valuations(self) -> pd.DataFrame:
+        prices = load_data()[0].loc[self.history]
+        return self.quantities * prices
+
+    @property
+    def portfolio_value(self) -> pd.Series:
+        return self.cash + self.valuations.sum(axis=1)
+
+    @property
+    def portfolio_returns(self):
+        return self.portfolio_value.pct_change().dropna()
+
+    @property
+    def periods_per_year(self):
+        return len(self.history) / ((self.history[-1] - self.history[0]).days / 365.25)
+
+    @property
+    def history(self):
+        return self.cash.index
+
+    @property
+    def cash_weight(self):
+        return self.cash / self.portfolio_value
+
+    @property
+    def asset_weights(self):
+        return self.valuations.div(self.portfolio_value, axis=0)
+
+    @property
+    def turnover(self) -> float:
+        return (
+            self.asset_weights.diff().abs().sum(axis=1).mean() * self.periods_per_year
+        )
+
+    @property
+    def mean_return(self) -> float:
+        return self.portfolio_returns.mean() * self.periods_per_year
+
+    @property
+    def volatility(self) -> float:
+        return self.portfolio_returns.std() * np.sqrt(self.periods_per_year)
+
+    @property
+    def max_drawdown(self) -> float:
+        return self.portfolio_value.div(self.portfolio_value.cummax()).sub(1).min()
+
+    @property
+    def max_leverage(self) -> float:
+        return self.asset_weights.abs().sum(axis=1).max()
+
+    @property
+    def sharpe(self) -> float:
+        return self.mean_return / self.volatility  # TODO: risk free rate
+
+    def save(self, path: Path):
+        with open(path, "wb") as f:
+            pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
+
+    @staticmethod
+    def load(path: Path) -> "BacktestResult":
+        with open(path, "rb") as f:
+            return pickle.load(f)
+
+
+if __name__ == "__main__":
+    # Example usage with equal weights
+    n_assets = load_data()[0].shape[1]
+    w_targets = np.ones(n_assets) / (n_assets + 1)
+    c_target = 1 / (n_assets + 1)
+    run_backtest(lambda _inputs: (w_targets, c_target), risk_target=0.0, verbose=True)