Dominik Roth
b615920843
- Costs updated to evidence-based values (SEC small-cap liquidity study 2013, Nasdaq spread data 2021, AQR Trading Costs paper 2018): large ~0.2% RT, mid ~0.5%, small ~1.5%, micro ~5% - Micro-cap note: Alpaca does not allow new OTC/Pink Sheet positions; most micro-cap signals are untradeable; at realistic 5% RT, micro-cap destroys capital (-36% to -81% excess return) - db.py: get_cached_market_caps returns already_fetched set including null rows, preventing repeated yfinance re-queries for known-missing tickers - plot_hp_heatmap: colorbar in dedicated axes (right margin), no overlap - plot_equity_curves: two-pass approach clips all curves to min end date - README: updated cost table, shortened insidercopytrading.com section Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
321 lines
12 KiB
Python
321 lines
12 KiB
Python
"""
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Generate performance plots for the insider-copytrade strategy.
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python main.py plot # saves to plots/
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python backtest/plot.py # same
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"""
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import logging
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import os
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import sys
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from datetime import datetime
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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import config
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from backtest.simulate import Strategy, _load_all_prices, _fetch_market_caps, simulate
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from db.db import get_signals_for_backtest
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logger = logging.getLogger(__name__)
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PLOTS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "plots")
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def _get_matplotlib():
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try:
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import matplotlib
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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return matplotlib, plt, mdates, np
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except ImportError:
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raise ImportError("pip install matplotlib numpy")
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def plot_hp_heatmap(prices: dict, out_dir: str = PLOTS_DIR, signals=None, market_caps=None) -> str:
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"""
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6-panel heatmap: one panel per cap tier (+ theoretical + all-cap).
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Axes: holding_days (rows) x buy_delay (cols).
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Color: annualised excess return vs SPY.
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"""
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matplotlib, plt, mdates, np = _get_matplotlib()
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from matplotlib.colors import TwoSlopeNorm
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hold_days = [3, 5, 7, 10, 14, 21, 30]
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buy_delays = [0, 1, 2, 3]
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# Cap tier definitions: (label, cap_tier, spread, slippage)
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# Costs based on SEC small-cap liquidity study (2013), Nasdaq spread data (2021),
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# and Frazzini/Israel/Moskowitz "Trading Costs" (AQR, 2018).
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# Alpaca charges zero commission. OTC/Pink Sheet stocks cannot be opened on Alpaca
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# (close-only), so micro-cap signals overlap heavily with untradeable names.
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tiers = [
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("Theoretical (0% RT, all)", None, 0.000, 0.000),
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("All cap (~1% RT)", None, 0.003, 0.004),
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("Large cap (~0.2% RT)", "large", 0.0005, 0.001),
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("Mid cap (~0.5% RT)", "mid", 0.0015, 0.002),
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("Small cap (~1.5% RT)", "small", 0.005, 0.005),
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("Micro cap (~5% RT, if listed)", "micro", 0.015, 0.020),
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]
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total = len(tiers) * len(hold_days) * len(buy_delays)
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done = 0
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tier_matrices = []
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for label, cap_tier, spread, slippage in tiers:
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Z = []
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for hd in hold_days:
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row = []
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for delay in buy_delays:
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s = Strategy(
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holding_days=hd, buy_delay=delay,
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spread=spread, slippage=slippage, commission=0,
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cap_tier=cap_tier,
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)
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r = simulate(s, prices=prices, _signals=signals, _market_caps=market_caps)
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excess = r.get("performance", {}).get("excess_return_pct", 0.0)
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row.append(excess)
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done += 1
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print(f" [{done}/{total}] {label} hold={hd}d delay={delay}d excess={excess:+.1f}%", flush=True)
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Z.append(row)
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tier_matrices.append((label, np.array(Z)))
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# Global color scale so all panels are comparable
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all_vals = np.concatenate([Z.flatten() for _, Z in tier_matrices])
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vmax = float(max(abs(all_vals.max()), abs(all_vals.min()), 10))
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norm = TwoSlopeNorm(vmin=-vmax, vcenter=0, vmax=vmax)
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fig, axes = plt.subplots(2, 3, figsize=(16, 9))
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axes_flat = axes.flatten()
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for ax, (label, Z) in zip(axes_flat, tier_matrices):
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im = ax.imshow(Z, cmap="RdYlGn", norm=norm, aspect="auto")
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ax.set_xticks(range(len(buy_delays)))
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ax.set_xticklabels([f"{d}d" for d in buy_delays], fontsize=9)
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ax.set_yticks(range(len(hold_days)))
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ax.set_yticklabels([f"{h}d" for h in hold_days], fontsize=9)
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ax.set_xlabel("Entry delay", fontsize=9)
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ax.set_ylabel("Holding period", fontsize=9)
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ax.set_title(label, fontsize=10, fontweight="bold")
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for i in range(len(hold_days)):
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for j in range(len(buy_delays)):
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val = Z[i, j]
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brightness = norm(val)
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color = "white" if brightness < 0.3 or brightness > 0.75 else "black"
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ax.text(j, i, f"{val:+.1f}", ha="center", va="center",
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fontsize=8, color=color)
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fig.suptitle(
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"HP sweep: holding period x entry delay, by cap tier (Alpaca, zero commission)",
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fontsize=13,
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)
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plt.tight_layout(rect=[0, 0, 0.88, 1])
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# Shared colorbar in reserved right margin — avoids overlapping panels
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cbar_ax = fig.add_axes([0.905, 0.15, 0.018, 0.65])
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fig.colorbar(
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plt.cm.ScalarMappable(norm=norm, cmap="RdYlGn"),
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cax=cbar_ax, label="Annualised excess return vs SPY (%)",
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)
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os.makedirs(out_dir, exist_ok=True)
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out = os.path.join(out_dir, "hp_sweep.png")
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plt.savefig(out, dpi=150, bbox_inches="tight")
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plt.close()
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logger.info(f"Saved {out}")
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return out
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def plot_equity_curves(prices: dict, out_dir: str = PLOTS_DIR, signals=None, market_caps=None) -> str:
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"""
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Plot portfolio equity curves for several cost scenarios vs SPY buy-and-hold.
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"""
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matplotlib, plt, mdates, np = _get_matplotlib()
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# Realistic Alpaca costs by cap tier (zero commission, spread + slippage only).
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# Sources: SEC small-cap liquidity study (2013); Nasdaq spread data (2021);
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# Frazzini/Israel/Moskowitz "Trading Costs" AQR (2018).
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# Micro-cap: Alpaca does not allow new positions in OTC/Pink Sheet stocks — most
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# micro-cap names fall in this category and are simply not tradeable.
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scenarios = [
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{"label": "Large cap (~0.2% RT)", "cap_tier": "large", "spread": 0.0005, "slippage": 0.001},
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{"label": "Mid cap (~0.5% RT)", "cap_tier": "mid", "spread": 0.0015, "slippage": 0.002},
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{"label": "Small cap (~1.5% RT)", "cap_tier": "small", "spread": 0.005, "slippage": 0.005},
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{"label": "Micro cap (~5% RT, if listed)", "cap_tier": "micro", "spread": 0.015, "slippage": 0.020},
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]
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fig, ax = plt.subplots(figsize=(13, 7))
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colors = ["#2ecc71", "#3498db", "#e67e22", "#e74c3c"]
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sim_start = None
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last_curve_date = None # earliest end across all scenarios — all curves clipped here
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# First pass: simulate and find common end date
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raw_curves = []
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for sc, color in zip(scenarios, colors):
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s = Strategy(
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holding_days=7, buy_delay=1,
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spread=sc["spread"], slippage=sc["slippage"], commission=0,
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cap_tier=sc["cap_tier"],
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)
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print(f" equity curve: {sc['label']}...", flush=True)
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r = simulate(s, prices=prices, _signals=signals, _market_caps=market_caps)
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curve = r.get("equity_curve", [])
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raw_curves.append((sc, color, curve, r))
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if curve:
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sim_start = sim_start or r["period"]["start"]
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end = curve[-1][0]
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last_curve_date = min(last_curve_date, end) if last_curve_date else end
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# Second pass: plot all curves clipped to the minimum end date
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for sc, color, curve, r in raw_curves:
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if not curve:
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continue
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curve = [(d, v) for d, v in curve if d <= last_curve_date]
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if not curve:
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continue
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dates = [datetime.strptime(d, "%Y-%m-%d") for d, _ in curve]
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values = [v for _, v in curve]
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base = values[0]
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ax.plot(dates, [v / base * 100 for v in values],
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label=sc["label"], color=color, linewidth=1.8)
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# SPY buy-and-hold overlay — clamp to last data point of strategy curves
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spy_entry = prices.get("SPY")
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if spy_entry and spy_entry[0] and sim_start and last_curve_date:
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spy_dates_all, spy_closes_all = spy_entry
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spy_pairs = [(d, c) for d, c in zip(spy_dates_all, spy_closes_all)
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if sim_start <= d <= last_curve_date]
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if spy_pairs:
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base = spy_pairs[0][1]
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ax.plot(
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[datetime.strptime(d, "%Y-%m-%d") for d, _ in spy_pairs],
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[c / base * 100 for _, c in spy_pairs],
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label="SPY buy & hold", color="black", linewidth=2.2, linestyle="--",
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)
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ax.axhline(100, color="gray", linewidth=0.8, linestyle=":")
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ax.set_xlabel("Date", fontsize=11)
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ax.set_ylabel("Portfolio value (indexed to 100)", fontsize=11)
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ax.set_title(
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"Insider Copytrade: equity curves by cap tier, Alpaca costs (7d hold, 1d delay, 10% position size)",
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fontsize=12,
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)
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ax.legend(fontsize=10)
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ax.grid(True, alpha=0.25)
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
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ax.xaxis.set_major_locator(mdates.MonthLocator(interval=6))
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plt.xticks(rotation=30)
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plt.tight_layout()
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os.makedirs(out_dir, exist_ok=True)
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out = os.path.join(out_dir, "equity_curves.png")
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plt.savefig(out, dpi=150, bbox_inches="tight")
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plt.close()
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logger.info(f"Saved {out}")
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return out
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def plot_position_size(prices: dict, out_dir: str = PLOTS_DIR, signals=None, market_caps=None) -> str:
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"""
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Line chart: annualised return vs position size for each cap tier.
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Shows whether 10% is conservative or optimal.
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"""
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matplotlib, plt, mdates, np = _get_matplotlib()
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pos_sizes = [0.03, 0.05, 0.07, 0.10, 0.15, 0.20, 0.25]
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tiers = [
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("Large (~0.2% RT)", "large", 0.0005, 0.001),
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("Mid (~0.5% RT)", "mid", 0.0015, 0.002),
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("Small (~1.5% RT)", "small", 0.005, 0.005),
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("Micro (~5% RT, if lsted)","micro", 0.015, 0.020),
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]
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colors = ["#2ecc71", "#3498db", "#e67e22", "#e74c3c"]
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fig, ax = plt.subplots(figsize=(10, 6))
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spy_ann = None
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total = len(tiers) * len(pos_sizes)
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done = 0
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for (label, cap_tier, spread, slippage), color in zip(tiers, colors):
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ann_returns = []
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for ps in pos_sizes:
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s = Strategy(
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holding_days=7, buy_delay=1,
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spread=spread, slippage=slippage, commission=0,
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cap_tier=cap_tier, position_size=ps,
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)
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r = simulate(s, prices=prices, _signals=signals, _market_caps=market_caps)
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perf = r.get("performance", {})
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ann_returns.append(perf.get("annualized_return_pct", 0.0))
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if spy_ann is None:
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spy_ann = perf.get("spy_annualized_pct", 16.0)
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done += 1
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print(f" [{done}/{total}] {label} pos={ps:.0%} ann={ann_returns[-1]:.1f}%", flush=True)
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ax.plot([p * 100 for p in pos_sizes], ann_returns,
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label=label, color=color, linewidth=2, marker="o", markersize=5)
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if spy_ann is not None:
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ax.axhline(spy_ann, color="black", linewidth=1.8, linestyle="--",
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label=f"SPY buy & hold ({spy_ann:.1f}%)")
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ax.axvline(10, color="gray", linewidth=1, linestyle=":", alpha=0.7)
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ax.text(10.3, ax.get_ylim()[0] + 1, "default\n(10%)", fontsize=8, color="gray")
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ax.set_xlabel("Position size (% of available cash per signal)", fontsize=11)
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ax.set_ylabel("Annualised return (%)", fontsize=11)
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ax.set_title(
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"Position size sensitivity by cap tier (7d hold, 1d delay, Alpaca costs)",
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fontsize=12,
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)
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ax.legend(fontsize=10)
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ax.grid(True, alpha=0.25)
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plt.tight_layout()
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os.makedirs(out_dir, exist_ok=True)
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out = os.path.join(out_dir, "position_size.png")
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plt.savefig(out, dpi=150, bbox_inches="tight")
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plt.close()
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logger.info(f"Saved {out}")
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return out
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def main():
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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)
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from db.db import init_db
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init_db()
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logger.info("Loading price cache...")
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prices = _load_all_prices()
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logger.info("Pre-fetching signals and market caps...")
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signals = get_signals_for_backtest(0.0, 1)
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tickers = list({s["ticker"] for s in signals})
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market_caps = _fetch_market_caps(tickers)
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logger.info(f" {len(signals)} signals, {len(market_caps)} market caps cached")
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logger.info("Generating HP heatmap (168 simulations)...")
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p1 = plot_hp_heatmap(prices, signals=signals, market_caps=market_caps)
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logger.info("Generating equity curves (4 simulations)...")
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p2 = plot_equity_curves(prices, signals=signals, market_caps=market_caps)
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logger.info("Generating position size sensitivity (28 simulations)...")
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p3 = plot_position_size(prices, signals=signals, market_caps=market_caps)
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print(f"\nPlots saved:\n {p1}\n {p2}\n {p3}\n")
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if __name__ == "__main__":
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main()
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