feat: aligned elements properly

Author: Satvik-Singh192

examples/csv_validation_example.py

@@ -6,6 +6,7 @@
 """
 
 import pandas as pd
+
 from quant_research_starter.data import validate_input_csv, validate_price_csv
 
 
@@ -23,7 +24,7 @@ def example_validate_price_file():
 
     # Check results
     if result["valid"]:
-        print(f"✓ File is valid!")
+        print("✓ File is valid!")
         print(f"  Rows: {result['row_count']}")
         print(f"  Columns: {result['column_count']}")
     else:
@@ -55,7 +56,7 @@ def example_validate_with_required_columns():
     if is_valid:
         print(f"✓ All required symbols present: {', '.join(required_symbols)}")
     else:
-        print(f"✗ Validation failed:")
+        print("✗ Validation failed:")
         for err in errors:
             print(f"  {err}")
 
@@ -76,7 +77,7 @@ def load_and_validate_prices(file_path: str):
         if not result["valid"]:
             # Handle errors
             error_messages = [err["message"] for err in result["errors"]]
-            raise ValueError(f"Invalid price file:\n" + "\n".join(error_messages))
+            raise ValueError("Invalid price file:\n" + "\n".join(error_messages))
 
         # If valid, proceed with loading
         prices = pd.read_csv(file_path, index_col=0, parse_dates=True)
@@ -102,7 +103,7 @@ def example_detailed_error_info():
 
     result = validate_input_csv("invalid_file.csv", csv_type="price")
 
-    print(f"Validation Summary:")
+    print("Validation Summary:")
     print(f"  Valid: {result['valid']}")
     print(f"  Errors: {len(result['errors'])}")
     print(f"  Warnings: {len(result['warnings'])}")

notebooks/01-getting-started.ipynb

@@ -45,6 +45,7 @@
     "\n",
     "# Show which Python the kernel is using (debugging)\n",
     "import sys as _sys\n",
+    "\n",
     "print(\"Kernel Python:\", _sys.executable)\n"
    ]
   },
@@ -56,14 +57,17 @@
    "outputs": [],
    "source": [
     "# Setup: imports\n",
-    "import os\n",
-    "import json\n",
-    "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
     "\n",
-    "from quant_research_starter.data import SampleDataLoader, SyntheticDataGenerator\n",
-    "from quant_research_starter.factors import MomentumFactor, ValueFactor, SizeFactor, VolatilityFactor\n",
     "from quant_research_starter.backtest import VectorizedBacktest\n",
+    "from quant_research_starter.data import SampleDataLoader\n",
+    "from quant_research_starter.factors import (\n",
+    "    MomentumFactor,\n",
+    "    SizeFactor,\n",
+    "    ValueFactor,\n",
+    "    VolatilityFactor,\n",
+    ")\n",
     "from quant_research_starter.metrics import RiskMetrics\n",
     "\n"
    ]
@@ -384,7 +388,7 @@
     "# Backtest\n",
     "common = prices.index.intersection(signals.index)\n",
     "px = prices.loc[common]\n",
-    "sg = pd.DataFrame({c: signals['composite'] for c in px.columns}, index=signals.index).loc[common]\n",
+    "sg = pd.DataFrame(dict.fromkeys(px.columns, signals['composite']), index=signals.index).loc[common]\n",
     "\n",
     "bt = VectorizedBacktest(px, sg, initial_capital=100000)\n",
     "results = bt.run(weight_scheme=\"rank\")\n",