feat: added loading bars centrally cuz file wise approach resulted in…

.gitignore

@@ -5,3 +5,5 @@ __pycache__/
 *.py[cod]
 *$py.class
 src/quant_research_starter.egg-info/PKG-INFO
+
+myenv/

README.md

@@ -125,7 +125,6 @@ The backtester supports different rebalancing frequencies to match your strategy
 
 ```python
 from quant_research_starter.backtest import VectorizedBacktest
-
 # Daily rebalancing (default)
 bt_daily = VectorizedBacktest(prices, signals, rebalance_freq="D")
 

src/quant_research_starter/cli.py

@@ -6,6 +6,7 @@
 import click
 import matplotlib.pyplot as plt
 import pandas as pd
+from tqdm import tqdm
 
 from .backtest import VectorizedBacktest
 from .data import SampleDataLoader, SyntheticDataGenerator
@@ -30,9 +31,14 @@ def generate_data(output, symbols, days):
     click.echo("Generating synthetic price data...")
 
     generator = SyntheticDataGenerator()
-    prices = generator.generate_price_data(
-        n_symbols=symbols, days=days, start_date="2020-01-01"
-    )
+    all_prices = []
+    for _ in tqdm(range(symbols), desc="Generating price series"):
+        prices = generator.generate_price_data(
+            n_symbols=1, days=days, start_date="2020-01-01"
+        )
+        all_prices.append(prices)
+
+    prices = pd.concat(all_prices, axis=1)
 
     # Ensure output directory exists
     output_path = Path(output)
@@ -94,8 +100,11 @@ def compute_factors(data_file, factors, output):
         vol = VolatilityFactor(lookback=21)
         factor_data["volatility"] = vol.compute(prices)
 
-    # Combine factors (simple average for demo)
-    combined_signals = pd.DataFrame({k: v.mean(axis=1) for k, v in factor_data.items()})
+    combined_signals_dict = {}
+    for k, v in tqdm(factor_data.items(), desc="Averaging factors"):
+        combined_signals_dict[k] = v.mean(axis=1)
+
+    combined_signals = pd.DataFrame(combined_signals_dict)
     combined_signals["composite"] = combined_signals.mean(axis=1)
 
     # Save results
@@ -148,7 +157,7 @@ def backtest(data_file, signals_file, initial_capital, output, plot, plotly):
     # Load signals
     if Path(signals_file).exists():
         signals_data = pd.read_csv(signals_file, index_col=0, parse_dates=True)
-        # Use composite signal if available, otherwise first column
+        # If a 'composite' signal column exists, use it; otherwise, fall back to the first available signal column.
         if "composite" in signals_data.columns:
             signals = signals_data["composite"]
         else:
@@ -158,27 +167,27 @@ def backtest(data_file, signals_file, initial_capital, output, plot, plotly):
         momentum = MomentumFactor(lookback=63)
         signals = momentum.compute(prices).mean(axis=1)
 
-    # Ensure signals align with prices
+    # Align dates
     common_dates = prices.index.intersection(signals.index)
     prices = prices.loc[common_dates]
     signals = signals.loc[common_dates]
 
-    # Expand signals to all symbols (simplified - same signal for all)
+    # Expand signals across symbols
     signal_matrix = pd.DataFrame(
         dict.fromkeys(prices.columns, signals), index=signals.index
     )
 
-    # Run backtest
+    # Use the original vectorized run() method for performance
+
     backtest = VectorizedBacktest(
         prices=prices,
         signals=signal_matrix,
         initial_capital=initial_capital,
         transaction_cost=0.001,
     )
-
     results = backtest.run(weight_scheme="rank")
 
-    # Calculate metrics
+    # Metrics
     metrics_calc = RiskMetrics(results["returns"])
     metrics = metrics_calc.calculate_all()
 
@@ -195,18 +204,16 @@ def backtest(data_file, signals_file, initial_capital, output, plot, plotly):
     with open(output_path, "w") as f:
         json.dump(results_dict, f, indent=2)
 
-    # Generate plot
+    # Plotting
     if plot:
         plt.figure(figsize=(12, 8))
 
-        # Plot portfolio value
         plt.subplot(2, 1, 1)
         plt.plot(results["portfolio_value"].index, results["portfolio_value"].values)
         plt.title("Portfolio Value")
         plt.ylabel("USD")
         plt.grid(True)
 
-        # Plot returns
         plt.subplot(2, 1, 2)
         plt.bar(results["returns"].index, results["returns"].values, alpha=0.7)
         plt.title("Daily Returns")
@@ -220,7 +227,6 @@ def backtest(data_file, signals_file, initial_capital, output, plot, plotly):
 
         click.echo(f"Plot saved -> {plot_path}")
 
-    # Generate Plotly HTML chart if requested
     if plotly:
         html_path = output_path.parent / "backtest_plot.html"