small tweaks

rohitashwachaks · rohitashwachaks · commit 35be8d8ca561 · 2025-07-07T11:22:01.000-05:00
diff --git a/core/visualizer.py b/core/visualizer.py
@@ -201,7 +201,9 @@ def plotly_interactive_equity(equity_curve: pd.Series,
     fig.write_html(html_path)
     print(f"Interactive plot saved to {html_path}")
 
-    return fig
+    # Explicitly set figure to None to help with garbage collection
+    # This should help prevent semaphore leaks
+    fig = None
 
 
 def plotly_equity_vs_benchmark(
@@ -263,4 +265,6 @@ def plotly_equity_vs_benchmark(
     fig.write_html(html_path)
     print(f"Interactive plot saved to {html_path}")
 
-    return fig
+    # Explicitly set figure to None to help with garbage collection
+    # This should help prevent semaphore leaks
+    fig = None
diff --git a/data_ingestion/yahoo_fetcher.py b/data_ingestion/yahoo_fetcher.py
@@ -1,6 +1,7 @@
+import pandas as pd
 import yfinance as yf
 
 
-def fetch_yahoo_data(ticker: str, start_date: str, end_date: str):
-    data = yf.download(ticker, start=start_date, end=end_date, multi_level_index=False, threads=False)
+def fetch_yahoo_data(ticker: str, start_date: str, end_date: str, interval: str = "1d") -> pd.DataFrame:
+    data = yf.download(ticker, start=start_date, end=end_date, interval=interval, multi_level_index=False, threads=False)
     return data
diff --git a/guardrails/trailing_stop_loss.py b/guardrails/trailing_stop_loss.py
@@ -6,7 +6,8 @@
 
 @GuardrailFactory.register("trailing_stop_loss")
 class TrailingStopLossGuardrail(GuardrailBase):
-    def __init__(self, stop_pct: float = 0.05):
+    def __init__(self, stop_pct: float = 0.07):
+        super().__init__()
         self.stop_pct = stop_pct
         self.entry_prices = {}
         self.highest_since_entry = {}
diff --git a/run_backtest.py b/run_backtest.py
@@ -63,7 +63,7 @@ def main():
     bt.run(start_date=args.start, end_date=args.end)
 
     # --- Backtest Results ---
-    print(f"\n🚀 Backtest completed for {portfolio.name} with {len(tickers)} tickers from {args.start} to {args.end}")
+    print(f"\n🚀 Backtest completed for {portfolio.name} with {len(tickers)} tickers ({', '.join(tickers)}) from {args.start} to {args.end}")
     print(f"💰 Starting Cash: ${args.cash:,.2f}")
     print(f"📈 Final Net Worth: ${bt.get_final_net_worth():,.2f}")
 
diff --git a/strategies/stock/__init__.py b/strategies/stock/__init__.py
@@ -3,3 +3,4 @@
 from .buy_n_hold import *
 from .capm_portfolio import *
 from .no_invest import *
+from .stoploss import *
diff --git a/strategies/stock/stoploss.py b/strategies/stock/stoploss.py
@@ -0,0 +1,58 @@
+from abc import ABC
+from typing import Dict, Optional
+
+import pandas as pd
+
+from contracts.asset import Asset, CashAsset
+from core.market_data import MarketData
+from strategies.stock.base import StrategyBase, StrategyFactory
+
+
+@StrategyFactory.register("stoploss")
+class StoplossStrategy(StrategyBase, ABC):
+    def __init__(self, **kwargs):
+        self.lookback_period = 7
+        self.has_bought: Optional[float] = None
+        self.trail_pct: float = kwargs.get("trail_pct", 0.04)
+
+    def get_name(self) -> str:
+        return "stoploss"
+
+    def get_config(self) -> Dict:
+        return {
+            "type": self.get_name()
+        }
+
+    def generate_signals(self, price_data: pd.DataFrame | Dict[str, pd.DataFrame], current_date: pd.Timestamp,
+                         positions: Dict[str, Asset], cash: float, **kwargs) -> Optional[Dict[str, int]]:
+        """
+        Buy once and hold till perpetuity for the strategy.
+        :param price_data: A DataFrame or dict of DataFrames containing price data for each asset.
+        :param cash:
+        :param current_date:
+        :param positions:
+        :return:
+        """
+        ticker = list(positions.keys())[0]  # Single ticker strategy
+        signals = {}
+
+        if self.has_bought:
+            # If already bought, SELL if cur_price lower than trailing stop-loss
+            cur_price = price_data[ticker]['Close'].loc[current_date]
+            if cur_price < (self.has_bought * (1-self.trail_pct)):
+                # SELL
+                signals[ticker] = -positions[ticker].shares
+                self.has_bought = None
+            elif cur_price > self.has_bought:
+                self.has_bought = cur_price
+
+        else:
+            pct_change = price_data[ticker]['Close'].pct_change().dropna()
+            avg_pct_change = pct_change.mean()
+            cur_pct_change = pct_change[-int(self.lookback_period/2):].mean()
+            if cur_pct_change > 0 and cur_pct_change > avg_pct_change:
+                cur_price = price_data[ticker]['Close'].loc[current_date]
+                signals[ticker] = int(cash / cur_price)
+                self.has_bought = cur_price
+
+        return signals
