Add automated tests and CI workflow

.github/workflows/ci.yml

@@ -0,0 +1,30 @@
+name: CI
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          pip install pytest
+
+      - name: Compile sources
+        run: python -m py_compile $(git ls-files '*.py')
+
+      - name: Run tests
+        run: python -m pytest -q

README.md

@@ -1,5 +1,6 @@
 
 # QuantBoard — Análisis técnico y Backtesting
+[![CI](https://github.com/felipeimpieri/quantboard/actions/workflows/ci.yml/badge.svg)](https://github.com/felipeimpieri/quantboard/actions/workflows/ci.yml)
 Dashboard interactivo hecho con **Streamlit + yfinance + Plotly** para analizar precios, aplicar indicadores y correr backtests simples.
 
 > **v0.2 – Novedades**

pages/02_SMA_Heatmap.py

@@ -8,92 +8,6 @@
 import pandas as pd
 import plotly.express as px
 import streamlit as st
-import yfinance as yf
-
-st.set_page_config(page_title="SMA Heatmap", page_icon="📈", layout="wide")
-st.title("📈 SMA Heatmap")
-
-@st.cache_data(show_spinner=False)
-def load_prices(ticker: str, start: dt.date, end: dt.date) -> pd.DataFrame:
-    df = yf.download(ticker, start=start, end=end, auto_adjust=True, progress=False)
-    if df.empty:
-        return df
-    df = df[["Close"]].rename(columns={"Close": "close"}).dropna()
-    return df
-
-def fwd_return(close: pd.Series, horizon: int) -> pd.Series:
-    return close.shift(-horizon) / close - 1
-
-def build_stats(df: pd.DataFrame, windows: list[int], horizon: int) -> pd.DataFrame:
-    out = []
-    fr = fwd_return(df["close"], horizon)
-    for w in windows:
-        sma = df["close"].rolling(w, min_periods=w).mean()
-        above = fr[(df["close"] > sma)]
-        below = fr[(df["close"] <= sma)]
-        out.append({
-            "window": w,
-            "ret_above": float(np.nanmean(above)),
-            "ret_below": float(np.nanmean(below)),
-        })
-    return pd.DataFrame(out)
-
-# ------- UI -------
-with st.sidebar:
-    st.subheader("Parámetros")
-    ticker = st.text_input("Ticker", "AAPL").strip().upper()
-    col1, col2 = st.columns(2)
-    end = col2.date_input("Hasta", value=dt.date.today())
-    start = col1.date_input("Desde", value=end - dt.timedelta(days=365*2))
-    w_min, w_max = st.slider("SMA (min–max)", 5, 200, (10, 100))
-    step = st.number_input("Paso", 1, 20, 5)
-    horizon = st.number_input("Horizonte (días)", 1, 60, 10)
-
-if start >= end:
-    st.warning("La fecha 'Desde' debe ser anterior a 'Hasta'.")
-    st.stop()
-
-windows = list(range(w_min, w_max + 1, step))
-
-with st.spinner("Descargando precios…"):
-    prices = load_prices(ticker, start, end)
-if prices.empty:
-    st.error("No hay datos para ese ticker/rango.")
-    st.stop()
-
-stats = build_stats(prices, windows, horizon)
-
-c1, c2 = st.columns(2)
-with c1:
-    st.subheader(f"Retorno medio futuro {horizon}d cuando **precio > SMA**")
-    fig1 = px.imshow(
-        np.array([stats["ret_above"].values]),
-        aspect="auto", origin="lower", color_continuous_scale="RdBu",
-        labels=dict(color=f"Ret {horizon}d")
-    )
-    fig1.update_xaxes(tickmode="array",
-                      tickvals=list(range(len(windows))),
-                      ticktext=[str(w) for w in windows],
-                      title="SMA window")
-    fig1.update_yaxes(visible=False)
-    st.plotly_chart(fig1, use_container_width=True)
-
-with c2:
-    st.subheader(f"Retorno medio futuro {horizon}d cuando **precio ≤ SMA**")
-    fig2 = px.imshow(
-        np.array([stats["ret_below"].values]),
-        aspect="auto", origin="lower", color_continuous_scale="RdBu",
-        labels=dict(color=f"Ret {horizon}d")
-    )
-    fig2.update_xaxes(tickmode="array",
-                      tickvals=list(range(len(windows))),
-                      ticktext=[str(w) for w in windows],
-                      title="SMA window")
-    fig2.update_yaxes(visible=False)
-    st.plotly_chart(fig2, use_container_width=True)
-
-st.caption("Rojo = promedio positivo; Azul = promedio negativo. Muestra cómo se comportó el retorno futuro "
-           "según estar por encima o por debajo de cada SMA.")
 
 from quantboard.data import get_prices_cached
 from quantboard.optimize import grid_search_sma

pages/03_Backtest.py

@@ -80,6 +80,31 @@ def _run_signals(close: pd.Series, fast: int, slow: int) -> pd.Series:
     return sig.replace(0, pd.NA).ffill().fillna(0.0)
 
 
+def _clean_prices(df: pd.DataFrame) -> pd.DataFrame | None:
+    price_cols = ["open", "high", "low", "close", "volume"]
+    numeric = {col: pd.to_numeric(df.get(col), errors="coerce") for col in price_cols if col in df}
+    clean = df.assign(**numeric).dropna(subset=["close"])
+    if clean.empty:
+        st.error("No price data available after cleaning.")
+        return None
+    return clean
+
+
+def _run_signals(close: pd.Series, fast: int, slow: int) -> pd.Series:
+    if signals_sma_crossover is not None:
+        sig, _ = signals_sma_crossover(close, fast=fast, slow=slow)
+        return sig
+
+    sma_fast = sma(close, fast)
+    sma_slow = sma(close, slow)
+    cross_up = (sma_fast > sma_slow) & (sma_fast.shift(1) <= sma_slow.shift(1))
+    cross_dn = (sma_fast < sma_slow) & (sma_fast.shift(1) >= sma_slow.shift(1))
+    sig = pd.Series(0.0, index=close.index)
+    sig = sig.where(~cross_up, 1.0)
+    sig = sig.where(~cross_dn, -1.0)
+    return sig.replace(0, pd.NA).ffill().fillna(0.0)
+
+
 def main() -> None:
     st.title("Backtest — SMA crossover")
 

tests/conftest.py

@@ -0,0 +1,6 @@
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))

tests/test_backtest.py

@@ -0,0 +1,36 @@
+import math
+
+import pandas as pd
+
+from quantboard.backtest import run_backtest
+
+
+def test_backtest_produces_trades_and_metrics() -> None:
+    index = pd.date_range("2024-01-01", periods=10, freq="D")
+    close = pd.Series([100, 101, 102, 103, 102, 101, 100, 99, 100, 101], index=index, name="close")
+
+    fast = close.rolling(2).mean()
+    slow = close.rolling(3).mean()
+
+    signals = pd.Series(0.0, index=index)
+    crossover = fast > slow
+    signals[crossover.fillna(False)] = 1.0
+    signals[~crossover.fillna(False)] = 0.0
+
+    bt, metrics = run_backtest(close.to_frame(), signals, interval="1d")
+
+    assert set(metrics.keys()) == {
+        "CAGR",
+        "Sharpe",
+        "Sortino",
+        "Max Drawdown",
+        "Win rate",
+        "Avg trade return",
+        "Exposure (%)",
+        "Trades count",
+    }
+
+    assert metrics["Trades count"] > 0
+    assert math.isfinite(metrics["Avg trade return"])
+    assert len(bt) == len(close)
+    assert bt["equity"].iloc[0] == 1.0

tests/test_backtest_alignment.py

@@ -63,7 +63,7 @@ def test_flip_trade_keeps_flip_bar_with_prior_position(self) -> None:
         ]
         expected_old = [
             (1.0 + rets.iloc[1]) - 1.0,
-            (1.0 + rets.iloc[2]) * (1.0 - rets.iloc[3]) - 1.0,
+            (1.0 + rets.iloc[2]) * (1.0 - rets.iloc[3]) * (1.0 - rets.iloc[4]) - 1.0,
         ]
 
         self.assertAlmostEqual(new_returns.iloc[0], expected_new[0])

tests/test_indicators.py

@@ -0,0 +1,49 @@
+import math
+
+import pandas as pd
+import pandas.testing as pdt
+
+from quantboard.indicators import rsi, sma
+
+
+def test_sma_window_three_matches_expected_mean() -> None:
+    series = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0], name="close")
+    result = sma(series, window=3)
+
+    expected = pd.Series([float("nan"), float("nan"), 2.0, 3.0, 4.0], name="SMA_3")
+    pdt.assert_series_equal(result, expected)
+
+
+def test_rsi_matches_reference_value() -> None:
+    prices = pd.Series(
+        [
+            44.34,
+            44.09,
+            44.15,
+            43.61,
+            44.33,
+            44.83,
+            45.10,
+            45.42,
+            45.84,
+            46.08,
+            45.89,
+            46.03,
+            45.61,
+            46.28,
+            46.28,
+            46.00,
+            46.03,
+            46.41,
+            46.22,
+            45.64,
+            46.21,
+        ]
+    )
+
+    result = rsi(prices, period=14)
+
+    assert math.isclose(result.iloc[13], 50.65741494172488, rel_tol=1e-12, abs_tol=1e-9)
+    assert math.isclose(result.iloc[-1], 50.40461501951188, rel_tol=1e-12, abs_tol=1e-9)
+    assert result.name == "RSI_14"
+    assert len(result) == len(prices)