feat: aligned elements properly

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'])}")

legacy/streamlit/streamlit_app.py

@@ -6,47 +6,105 @@
 import streamlit as st
 
 st.set_page_config(page_title="Quant Research Starter", layout="wide")
-st.title("Quant Research Starter Dashboard")
-
+st.title("📊 Quant Research Starter Dashboard")
 output_dir = Path.cwd() / "output"
 default_results = output_dir / "backtest_results.json"
 default_factors = output_dir / "factors.csv"
 
-st.sidebar.header("Inputs")
+# --- Sidebar Inputs ---
+st.sidebar.header("⚙️ Inputs")
 results_file = st.sidebar.text_input("Backtest results JSON", str(default_results))
 factors_file = st.sidebar.text_input("Factors CSV", str(default_factors))
 
-col1, col2 = st.columns(2)
+# --- Main Layout ---
+st.markdown("### Overview")
+st.caption("Visualize portfolio performance and factor signals side-by-side.")
+
+# Create two balanced columns
+col1, col2 = st.columns([1, 1], gap="large")
 
+# --- Left: Equity Curve ---
 with col1:
-    st.subheader("Equity Curve")
+    st.markdown("#### 📈 Equity Curve")
     if Path(results_file).exists():
         with open(results_file) as f:
             data = json.load(f)
+
         df = pd.DataFrame(
             {
                 "date": pd.to_datetime(data["dates"]),
                 "portfolio_value": data["portfolio_value"],
             }
         ).set_index("date")
-        fig = px.line(df, y="portfolio_value", title="Portfolio Value")
+
+        fig = px.line(
+            df,
+            y="portfolio_value",
+            title="Portfolio Value Over Time",
+            labels={"portfolio_value": "Portfolio Value"},
+        )
+        fig.update_layout(
+            margin={"l": 30, "r": 30, "t": 40, "b": 30},
+            height=400,
+            title_x=0.5,
+        )
+
         st.plotly_chart(fig, use_container_width=True)
-        st.json(data.get("metrics", {}))
+
+        # Centered metrics section
+        st.markdown("#### 🔍 Summary Metrics")
+        metrics = data.get("metrics", {})
+        if metrics:
+            mcol1, mcol2, mcol3 = st.columns(3)
+            items = list(metrics.items())
+            for i, (key, value) in enumerate(items[:3]):
+                with [mcol1, mcol2, mcol3][i]:
+                    st.metric(
+                        label=key.replace("_", " ").title(), value=round(value, 4)
+                    )
+            if len(items) > 3:
+                st.json(dict(items[3:]))
+        else:
+            st.info("No metrics found in results.")
     else:
-        st.info("Run the CLI backtest to generate results.")
+        st.info("⚠️ Run the CLI backtest to generate results.")
 
+# --- Right: Factor Signals ---
 with col2:
-    st.subheader("Factor Signals")
+    st.markdown("#### 📉 Factor Signals")
     if Path(factors_file).exists():
         fdf = pd.read_csv(factors_file, index_col=0, parse_dates=True)
-        st.dataframe(fdf.tail())
-        if "composite" in fdf.columns:
-            fig2 = px.line(fdf[["composite"]], title="Composite Signal")
-            st.plotly_chart(fig2, use_container_width=True)
+
+        # Tabs for cleaner organization
+        tab1, tab2 = st.tabs(["📑 Latest Data", "📊 Composite Signal"])
+
+        with tab1:
+            st.dataframe(
+                fdf.tail().style.set_table_styles(
+                    [{"selector": "th", "props": [("text-align", "center")]}]
+                )
+            )
+
+        with tab2:
+            if "composite" in fdf.columns:
+                fig2 = px.line(
+                    fdf[["composite"]],
+                    title="Composite Factor Signal",
+                    labels={"composite": "Composite"},
+                )
+                fig2.update_layout(
+                    margin={"l": 30, "r": 30, "t": 40, "b": 30},
+                    height=400,
+                    title_x=0.5,
+                )
+                st.plotly_chart(fig2, use_container_width=True)
+            else:
+                st.info("No composite signal found.")
     else:
-        st.info("Compute factors to view signals.")
+        st.info("⚠️ Compute factors to view signals.")
 
+# --- Footer ---
 st.markdown("---")
 st.caption(
-    "Tip: Use qrs CLI to generate data, factors, and backtest results. Then refresh this page."
+    "💡 Tip: Use `qrs` CLI to generate data, factors, and backtest results, then refresh this page."
 )

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"
    ]