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a2cbcac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | from __future__ import annotations
from datetime import datetime
from pathlib import Path
from typing import List, Tuple, Dict, Any
import matplotlib.pyplot as plt
from .engine import DEFAULT_CASH
def calculate_trade_execution(
signals_df, dates: List, prices: List[float], starting_cash: float
) -> Tuple[List, List[float], List, List[float]]:
"""Calculate executed buy/sell points to overlay on portfolio chart."""
buy_dates, buy_values, sell_dates, sell_values = [], [], [], []
current_shares = 0
current_cash = starting_cash
for i, date in enumerate(dates):
matching_signals = signals_df[signals_df["date"].dt.date == date]
if matching_signals.empty:
continue
signal_row = matching_signals.iloc[0]
current_price = prices[i]
if signal_row["trading_signal"] == "BUY":
position_percent = signal_row["position_size"] / 100.0
target_cash = current_cash * position_percent
shares_bought = int(target_cash / current_price) if current_cash > current_price else 0
if shares_bought > 0:
current_shares += shares_bought
current_cash -= shares_bought * current_price
buy_dates.append(date)
buy_values.append(current_shares * current_price + current_cash)
elif signal_row["trading_signal"] == "SELL":
position_percent = signal_row["position_size"] / 100.0
shares_sold = int(current_shares * position_percent) if current_shares > 0 else 0
if shares_sold > 0:
current_shares -= shares_sold
current_cash += shares_sold * current_price
sell_dates.append(date)
sell_values.append(current_shares * current_price + current_cash)
return buy_dates, buy_values, sell_dates, sell_values
def _extract_dates_prices(cerebro) -> Tuple[List, List[float]]:
data_feed = cerebro.datas[0]
data_len = len(data_feed.close.array)
dates, prices = [], []
for i in range(data_len):
dt_val = data_feed.datetime.array[i]
date_obj = datetime.fromordinal(int(dt_val)).date()
dates.append(date_obj)
prices.append(data_feed.close.array[i])
return dates, prices
def plot_single_stock(symbol: str, primo_cerebro, buyhold_cerebro, output_dir: str, filename: str | None = None) -> Path:
"""Create single-stock portfolio comparison chart and save it."""
dates, prices = _extract_dates_prices(primo_cerebro)
primo_strategy = primo_cerebro.runstrats[0][0]
primo_portfolio = getattr(primo_strategy, "portfolio_values", [])
buyhold_portfolio = [DEFAULT_CASH]
if prices:
buyhold_shares = int(DEFAULT_CASH / prices[0])
buyhold_cash_left = DEFAULT_CASH - (buyhold_shares * prices[0])
for price in prices[1:]:
buyhold_portfolio.append(buyhold_shares * price + buyhold_cash_left)
if len(primo_portfolio) != len(dates):
if len(primo_portfolio) < len(dates):
last_value = primo_portfolio[-1] if primo_portfolio else DEFAULT_CASH
primo_portfolio.extend([last_value] * (len(dates) - len(primo_portfolio)))
else:
primo_portfolio = primo_portfolio[: len(dates)]
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), gridspec_kw={"height_ratios": [2, 1]})
ax1.plot(dates, primo_portfolio, color="blue", linewidth=2, label="PrimoAgent Portfolio")
ax1.plot(dates, buyhold_portfolio, color="red", linewidth=2, label="Buy & Hold Portfolio")
ax1.set_ylabel("Portfolio Value ($)")
signals_df = getattr(primo_strategy, "signals_df", None)
if signals_df is not None:
buy_dates, buy_values, sell_dates, sell_values = calculate_trade_execution(
signals_df, dates, prices, DEFAULT_CASH
)
if buy_dates:
ax1.scatter(buy_dates, buy_values, color="green", marker="^", s=100, alpha=0.8, label="BUY Executed", zorder=5)
if sell_dates:
ax1.scatter(sell_dates, sell_values, color="red", marker="v", s=100, alpha=0.8, label="SELL Executed", zorder=5)
ax1.legend(loc="upper left")
ax1.set_title(f"{symbol}: PrimoAgent vs Buy & Hold Performance")
ax1.grid(True, alpha=0.3)
if signals_df is not None:
buy_volumes, sell_volumes = [], []
current_shares, current_cash = 0, DEFAULT_CASH
for i, date in enumerate(dates):
matching_signals = signals_df[signals_df["date"].dt.date == date]
if not matching_signals.empty:
signal_row = matching_signals.iloc[0]
price = prices[i]
if signal_row["trading_signal"] == "BUY":
position_percent = signal_row["position_size"] / 100.0
target_cash = current_cash * position_percent
shares_bought = int(target_cash / price) if current_cash > price else 0
if shares_bought > 0:
current_shares += shares_bought
current_cash -= shares_bought * price
buy_volumes.append(shares_bought)
sell_volumes.append(0)
else:
buy_volumes.append(0)
sell_volumes.append(0)
elif signal_row["trading_signal"] == "SELL":
position_percent = signal_row["position_size"] / 100.0
shares_sold = int(current_shares * position_percent) if current_shares > 0 else 0
if shares_sold > 0:
current_shares -= shares_sold
current_cash += shares_sold * price
buy_volumes.append(0)
sell_volumes.append(-shares_sold)
else:
buy_volumes.append(0)
sell_volumes.append(0)
else:
buy_volumes.append(0)
sell_volumes.append(0)
else:
buy_volumes.append(0)
sell_volumes.append(0)
ax2.bar(dates, buy_volumes, color="green", alpha=0.7, label="BUY Shares")
ax2.bar(dates, sell_volumes, color="red", alpha=0.7, label="SELL Shares")
ax2.set_ylabel("Number of Shares")
ax2.set_xlabel("Date")
ax2.set_title("Trading Volume")
ax2.legend()
ax2.grid(True, alpha=0.3)
ax2.axhline(y=0, color="black", linewidth=0.5)
plt.tight_layout()
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
save_path = output_path / (filename or f"single_backtest_{symbol}.png")
plt.savefig(save_path, dpi=300, bbox_inches="tight")
plt.close()
return save_path
def plot_returns_bar_chart(all_results: Dict[str, Dict[str, Any]], save_path: Path) -> None:
"""Create simple bar chart showing returns for all stocks and strategies."""
fig, ax = plt.subplots(figsize=(12, 8))
symbols = sorted(all_results.keys())
primo_returns = [all_results[s]["primo"]["Cumulative Return [%]"] for s in symbols]
buyhold_returns = [all_results[s]["buyhold"]["Cumulative Return [%]"] for s in symbols]
x = range(len(symbols))
width = 0.35
bars1 = ax.bar([i - width / 2 for i in x], primo_returns, width, label="PrimoAgent", color="#1f77b4", alpha=0.8)
bars2 = ax.bar([i + width / 2 for i in x], buyhold_returns, width, label="Buy & Hold", color="#ff7f0e", alpha=0.8)
ax.set_xlabel("Stocks")
ax.set_ylabel("Cumulative Return (%)")
ax.set_title("Performance Comparison: PrimoAgent vs Buy & Hold")
ax.set_xticks(list(x))
ax.set_xticklabels(symbols)
ax.legend()
ax.grid(True, alpha=0.3, axis="y")
ax.axhline(y=0, color="black", linewidth=0.5)
for bar, value in zip(bars1, primo_returns):
height = bar.get_height()
ax.text(
bar.get_x() + bar.get_width() / 2.0,
height + (0.5 if height >= 0 else -1.5),
f"{value:.1f}%",
ha="center",
va="bottom" if height >= 0 else "top",
fontsize=10,
fontweight="bold",
)
for bar, value in zip(bars2, buyhold_returns):
height = bar.get_height()
ax.text(
bar.get_x() + bar.get_width() / 2.0,
height + (0.5 if height >= 0 else -1.5),
f"{value:.1f}%",
ha="center",
va="bottom" if height >= 0 else "top",
fontsize=10,
fontweight="bold",
)
plt.tight_layout()
save_path.parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=300, bbox_inches="tight")
plt.close()
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