feat: add MACD, Bollinger, VWAP, ATR, EMA, SMA-200 indicator computations

2026-02-23 21:49:26 +00:00 · 2026-02-23 21:49:26 +00:00 · 6c909d12c3
commit 6c909d12c3
parent 6f512cf91f
2 changed files with 421 additions and 1 deletions
--- a/services/signal_generator/market_data.py
+++ b/services/signal_generator/market_data.py
@ -1,11 +1,13 @@
 """In-memory market data manager with rolling OHLCV windows.

 Maintains a per-ticker deque of recent bars and computes technical
-indicators (SMA, RSI) on demand when building ``MarketSnapshot`` objects.
+indicators (SMA, RSI, EMA, MACD, Bollinger Bands, VWAP, ATR) on demand
+when building ``MarketSnapshot`` objects.
 """

 from __future__ import annotations

+import math
 from collections import deque
 from typing import Any

@ -60,6 +62,17 @@ class MarketDataManager:

        latest = bars[-1]
        closes = [b.close for b in bars]
+        bar_list = list(bars)
+
+        macd_result = self._compute_macd(closes)
+        macd_val = macd_signal = macd_hist = None
+        if macd_result:
+            macd_val, macd_signal, macd_hist = macd_result
+
+        boll_result = self._compute_bollinger(closes)
+        boll_upper = boll_mid = boll_lower = None
+        if boll_result:
+            boll_upper, boll_mid, boll_lower = boll_result

        return MarketSnapshot(
            ticker=ticker,
@ -71,7 +84,18 @@ class MarketDataManager:
            volume=latest.volume,
            sma_20=self._compute_sma(closes, 20),
            sma_50=self._compute_sma(closes, 50),
+            sma_200=self._compute_sma(closes, 200),
            rsi=self._compute_rsi(closes, _RSI_PERIOD),
+            ema_9=self._compute_ema(closes, 9),
+            ema_21=self._compute_ema(closes, 21),
+            macd=macd_val,
+            macd_signal=macd_signal,
+            macd_histogram=macd_hist,
+            bollinger_upper=boll_upper,
+            bollinger_mid=boll_mid,
+            bollinger_lower=boll_lower,
+            vwap=self._compute_vwap(bar_list),
+            atr=self._compute_atr(bar_list),
            bars=[b.model_dump(mode="json") for b in bars],
        )

@ -120,3 +144,136 @@ class MarketDataManager:
        rs = avg_gain / avg_loss
        rsi = 100.0 - (100.0 / (1.0 + rs))
        return round(rsi, 4)
+
+    @staticmethod
+    def _compute_ema(closes: list[float], period: int) -> float | None:
+        """Compute the exponential moving average over *closes*.
+
+        Seeds with the SMA of the first *period* closes, then applies the
+        EMA multiplier ``2 / (period + 1)`` for each subsequent close.
+        Returns ``None`` if there are fewer than *period* data points.
+        """
+        if len(closes) < period:
+            return None
+
+        multiplier = 2.0 / (period + 1)
+        # Seed with SMA of first `period` values
+        ema = sum(closes[:period]) / period
+        for price in closes[period:]:
+            ema = (price - ema) * multiplier + ema
+        return round(ema, 6)
+
+    @staticmethod
+    def _compute_macd(
+        closes: list[float],
+    ) -> tuple[float, float, float] | None:
+        """Compute MACD(12, 26, 9).
+
+        Needs at least 35 closes (26 for slow EMA seed + 9 for signal
+        line).  Returns ``(macd_line, signal_line, histogram)`` or
+        ``None``.
+        """
+        if len(closes) < 35:
+            return None
+
+        # Helper to compute a running EMA series from closes
+        def _ema_series(data: list[float], period: int) -> list[float]:
+            multiplier = 2.0 / (period + 1)
+            ema = sum(data[:period]) / period
+            result = [ema]
+            for val in data[period:]:
+                ema = (val - ema) * multiplier + ema
+                result.append(ema)
+            return result
+
+        ema12_series = _ema_series(closes, 12)
+        ema26_series = _ema_series(closes, 26)
+
+        # Align: ema12_series starts at index 12, ema26_series at index 26
+        # After ema26 seed, we have len(closes)-26 subsequent values (+1 for seed)
+        # We need to align ema12 to the same time window as ema26
+        # ema26 has values for indices 26..len(closes)-1 (total: len-26+1 entries incl seed at 26)
+        # ema12 has values for indices 12..len(closes)-1 (total: len-12+1 entries incl seed at 12)
+        # Offset into ema12 for index 26 = 26 - 12 = 14
+        offset = 26 - 12
+        macd_values = [
+            ema12_series[offset + i] - ema26_series[i]
+            for i in range(len(ema26_series))
+        ]
+
+        if len(macd_values) < 9:
+            return None
+
+        # Signal line = EMA-9 of MACD values
+        signal_series = _ema_series(macd_values, 9)
+
+        macd_line = round(macd_values[-1], 6)
+        signal_line = round(signal_series[-1], 6)
+        histogram = round(macd_line - signal_line, 6)
+        return (macd_line, signal_line, histogram)
+
+    @staticmethod
+    def _compute_bollinger(
+        closes: list[float], period: int = 20, num_std: float = 2.0
+    ) -> tuple[float, float, float] | None:
+        """Compute Bollinger Bands (SMA ± *num_std* standard deviations).
+
+        Returns ``(upper, mid, lower)`` or ``None`` if insufficient data.
+        """
+        if len(closes) < period:
+            return None
+
+        window = closes[-period:]
+        mid = sum(window) / period
+        variance = sum((x - mid) ** 2 for x in window) / period
+        std = math.sqrt(variance)
+
+        upper = round(mid + num_std * std, 6)
+        mid_r = round(mid, 6)
+        lower = round(mid - num_std * std, 6)
+        return (upper, mid_r, lower)
+
+    @staticmethod
+    def _compute_vwap(bars: list[OHLCVBar]) -> float | None:
+        """Compute the cumulative Volume-Weighted Average Price.
+
+        Typical price = (high + low + close) / 3.
+        Returns ``None`` if no bars are provided.
+        """
+        if not bars:
+            return None
+
+        cum_tp_vol = 0.0
+        cum_vol = 0.0
+        for bar in bars:
+            typical = (bar.high + bar.low + bar.close) / 3.0
+            cum_tp_vol += typical * bar.volume
+            cum_vol += bar.volume
+
+        if cum_vol == 0:
+            return None
+
+        return round(cum_tp_vol / cum_vol, 6)
+
+    @staticmethod
+    def _compute_atr(bars: list[OHLCVBar], period: int = 14) -> float | None:
+        """Compute the Average True Range.
+
+        Needs at least ``period + 1`` bars (to compute ``period`` true
+        ranges).  True range = max(H-L, |H-prevC|, |L-prevC|).
+        Returns ``None`` if insufficient data.
+        """
+        if len(bars) < period + 1:
+            return None
+
+        true_ranges: list[float] = []
+        for i in range(1, len(bars)):
+            h = bars[i].high
+            l = bars[i].low  # noqa: E741
+            prev_c = bars[i - 1].close
+            tr = max(h - l, abs(h - prev_c), abs(l - prev_c))
+            true_ranges.append(tr)
+
+        # Simple average of the last `period` true ranges
+        recent = true_ranges[-period:]
+        return round(sum(recent) / period, 6)
--- a/tests/test_indicators.py
+++ b/tests/test_indicators.py
@ -0,0 +1,263 @@
+"""Tests for extended technical indicator computations in MarketDataManager."""
+
+from datetime import datetime, timezone
+
+import pytest
+
+from services.signal_generator.market_data import MarketDataManager
+from shared.schemas.trading import OHLCVBar
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _bar(
+    close: float,
+    volume: float = 1000.0,
+    high: float | None = None,
+    low: float | None = None,
+    open_: float | None = None,
+) -> OHLCVBar:
+    """Create a single OHLCVBar with sensible defaults."""
+    return OHLCVBar(
+        timestamp=datetime.now(tz=timezone.utc),
+        open=open_ if open_ is not None else close,
+        high=high if high is not None else close,
+        low=low if low is not None else close,
+        close=close,
+        volume=volume,
+    )
+
+
+def _add_bars(
+    mgr: MarketDataManager,
+    ticker: str,
+    closes: list[float],
+    volume: float = 1000.0,
+) -> None:
+    """Add multiple bars (one per close) to the manager."""
+    for c in closes:
+        mgr.add_bar(ticker, _bar(c, volume=volume))
+
+
+# ---------------------------------------------------------------------------
+# EMA
+# ---------------------------------------------------------------------------
+
+
+class TestEMA:
+    """Tests for exponential moving average computation."""
+
+    def test_ema_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 5)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.ema_9 is None
+
+    def test_ema_9_with_exact_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 9)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.ema_9 == pytest.approx(100.0, abs=0.01)
+
+    def test_ema_responds_to_recent_prices(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 20 + [110.0])
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.ema_9 is not None
+        assert 100.0 < snap.ema_9 < 110.0
+
+    def test_ema_21_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 15)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.ema_21 is None
+
+    def test_ema_21_computed_with_enough_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 25)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.ema_21 == pytest.approx(100.0, abs=0.01)
+
+
+# ---------------------------------------------------------------------------
+# SMA-200
+# ---------------------------------------------------------------------------
+
+
+class TestSMA200:
+    """Tests for 200-period simple moving average."""
+
+    def test_sma_200_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 100)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.sma_200 is None
+
+    def test_sma_200_computed_with_enough_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 200)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.sma_200 == pytest.approx(100.0, abs=0.01)
+
+
+# ---------------------------------------------------------------------------
+# MACD
+# ---------------------------------------------------------------------------
+
+
+class TestMACD:
+    """Tests for MACD(12,26,9) computation."""
+
+    def test_macd_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 20)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.macd is None
+
+    def test_macd_computed_with_enough_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 40)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.macd == pytest.approx(0.0, abs=0.01)
+
+    def test_macd_positive_in_uptrend(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        rising = [50.0 + i for i in range(50)]
+        _add_bars(mgr, "X", rising)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.macd is not None
+        assert snap.macd > 0
+
+
+# ---------------------------------------------------------------------------
+# Bollinger Bands
+# ---------------------------------------------------------------------------
+
+
+class TestBollingerBands:
+    """Tests for Bollinger Bands (SMA-20 +/- 2 std dev)."""
+
+    def test_bollinger_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 10)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.bollinger_upper is None
+
+    def test_bollinger_computed_with_enough_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        _add_bars(mgr, "X", [100.0] * 25)
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.bollinger_mid == pytest.approx(100.0, abs=0.01)
+        assert snap.bollinger_upper is not None
+        assert snap.bollinger_lower is not None
+        assert snap.bollinger_upper >= snap.bollinger_mid
+        assert snap.bollinger_lower <= snap.bollinger_mid
+
+    def test_bollinger_width_increases_with_volatility(self) -> None:
+        # Stable prices -> narrow bands
+        mgr_stable = MarketDataManager(max_bars=300)
+        _add_bars(mgr_stable, "X", [100.0] * 25)
+        snap_stable = mgr_stable.get_snapshot("X")
+
+        # Alternating prices -> wider bands
+        mgr_volatile = MarketDataManager(max_bars=300)
+        alternating = [100.0 + (10.0 if i % 2 == 0 else -10.0) for i in range(25)]
+        _add_bars(mgr_volatile, "X", alternating)
+        snap_volatile = mgr_volatile.get_snapshot("X")
+
+        assert snap_stable is not None and snap_volatile is not None
+        assert snap_stable.bollinger_upper is not None
+        assert snap_stable.bollinger_lower is not None
+        assert snap_volatile.bollinger_upper is not None
+        assert snap_volatile.bollinger_lower is not None
+
+        width_stable = snap_stable.bollinger_upper - snap_stable.bollinger_lower
+        width_volatile = snap_volatile.bollinger_upper - snap_volatile.bollinger_lower
+        assert width_volatile > width_stable
+
+
+# ---------------------------------------------------------------------------
+# VWAP
+# ---------------------------------------------------------------------------
+
+
+class TestVWAP:
+    """Tests for Volume-Weighted Average Price."""
+
+    def test_vwap_computed(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        for _ in range(5):
+            mgr.add_bar("X", _bar(close=100.0, high=100.0, low=100.0, volume=1000.0))
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.vwap == pytest.approx(100.0, abs=0.01)
+
+    def test_vwap_weighted_by_volume(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        # Bar 1: typical price = (110+90+100)/3 = 100, volume = 100
+        mgr.add_bar("X", _bar(close=100.0, high=110.0, low=90.0, volume=100.0))
+        # Bar 2: typical price = (220+180+200)/3 = 200, volume = 900
+        mgr.add_bar("X", _bar(close=200.0, high=220.0, low=180.0, volume=900.0))
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.vwap is not None
+        # Expected: (100*100 + 200*900) / (100+900) = 190
+        assert snap.vwap == pytest.approx(190.0, abs=0.01)
+
+
+# ---------------------------------------------------------------------------
+# ATR
+# ---------------------------------------------------------------------------
+
+
+class TestATR:
+    """Tests for Average True Range."""
+
+    def test_atr_returns_none_insufficient_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        for _ in range(5):
+            mgr.add_bar("X", _bar(close=100.0, high=105.0, low=95.0))
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.atr is None
+
+    def test_atr_computed_with_enough_data(self) -> None:
+        mgr = MarketDataManager(max_bars=300)
+        for _ in range(15):
+            mgr.add_bar("X", _bar(close=100.0, high=105.0, low=95.0))
+        snap = mgr.get_snapshot("X")
+        assert snap is not None
+        assert snap.atr is not None
+        assert snap.atr >= 0
+
+    def test_atr_increases_with_volatility(self) -> None:
+        # Tight range
+        mgr_tight = MarketDataManager(max_bars=300)
+        for _ in range(15):
+            mgr_tight.add_bar("X", _bar(close=100.0, high=101.0, low=99.0))
+        snap_tight = mgr_tight.get_snapshot("X")
+
+        # Wide range
+        mgr_wide = MarketDataManager(max_bars=300)
+        for _ in range(15):
+            mgr_wide.add_bar("X", _bar(close=100.0, high=120.0, low=80.0))
+        snap_wide = mgr_wide.get_snapshot("X")
+
+        assert snap_tight is not None and snap_wide is not None
+        assert snap_tight.atr is not None and snap_wide.atr is not None
+        assert snap_wide.atr > snap_tight.atr