dennisthiessen
dc08a805a8
Replace inert ±0.25 bands with ~p25/p75 cutoffs from the prod snapshot (−0.08 / 0.00). Document zero-return dilution and left-skewed distribution.
650 lines
20 KiB
Python
650 lines
20 KiB
Python
"""Technical Analysis service.
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Computes indicators from OHLCV data. Each indicator function is a pure
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function that takes a list of OHLCV-like records and returns raw values
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plus a normalized 0-100 score. The service layer handles DB fetching,
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caching, and minimum-data validation.
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"""
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from __future__ import annotations
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from datetime import date
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from typing import Any
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.cache import indicator_cache
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from app.exceptions import ValidationError
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from app.services.price_service import query_ohlcv
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# ---------------------------------------------------------------------------
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# Minimum data requirements per indicator
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# ---------------------------------------------------------------------------
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MIN_BARS: dict[str, int] = {
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"adx": 28,
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"ema": 0, # dynamic: period + 1
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"rsi": 15,
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"atr": 15,
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"volume_profile": 20,
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"pivot_points": 5,
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"fip_id": 253, # 12-1 formation: need index i-252
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}
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DEFAULT_PERIODS: dict[str, int] = {
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"adx": 14,
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"ema": 20,
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"rsi": 14,
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"atr": 14,
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}
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# ---------------------------------------------------------------------------
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# Pure computation helpers
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# ---------------------------------------------------------------------------
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def _ema(values: list[float], period: int) -> list[float]:
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"""Compute EMA series. Returns list same length as *values*."""
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if len(values) < period:
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return []
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k = 2.0 / (period + 1)
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ema_vals: list[float] = [sum(values[:period]) / period]
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for v in values[period:]:
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ema_vals.append(v * k + ema_vals[-1] * (1 - k))
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return ema_vals
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def compute_adx(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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period: int = 14,
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) -> dict[str, Any]:
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"""Compute ADX from high/low/close arrays.
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Returns dict with ``adx``, ``plus_di``, ``minus_di``, ``score``.
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"""
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n = len(closes)
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if n < 2 * period:
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raise ValidationError(
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f"ADX requires at least {2 * period} bars, got {n}"
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)
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# True Range, +DM, -DM
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tr_list: list[float] = []
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plus_dm: list[float] = []
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minus_dm: list[float] = []
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for i in range(1, n):
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h, low_val, pc = highs[i], lows[i], closes[i - 1]
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tr_list.append(max(h - low_val, abs(h - pc), abs(low_val - pc)))
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up = highs[i] - highs[i - 1]
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down = lows[i - 1] - lows[i]
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plus_dm.append(up if up > down and up > 0 else 0.0)
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minus_dm.append(down if down > up and down > 0 else 0.0)
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# Smoothed TR, +DM, -DM (Wilder smoothing)
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def _smooth(vals: list[float], p: int) -> list[float]:
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s = [sum(vals[:p])]
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for v in vals[p:]:
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s.append(s[-1] - s[-1] / p + v)
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return s
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s_tr = _smooth(tr_list, period)
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s_plus = _smooth(plus_dm, period)
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s_minus = _smooth(minus_dm, period)
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# +DI, -DI, DX
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dx_list: list[float] = []
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plus_di_last = 0.0
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minus_di_last = 0.0
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for i in range(len(s_tr)):
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tr_v = s_tr[i] if s_tr[i] != 0 else 1e-10
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pdi = 100.0 * s_plus[i] / tr_v
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mdi = 100.0 * s_minus[i] / tr_v
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denom = pdi + mdi if (pdi + mdi) != 0 else 1e-10
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dx_list.append(100.0 * abs(pdi - mdi) / denom)
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plus_di_last = pdi
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minus_di_last = mdi
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# ADX = smoothed DX
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if len(dx_list) < period:
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adx_val = sum(dx_list) / len(dx_list) if dx_list else 0.0
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else:
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adx_vals = _smooth(dx_list, period)
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adx_val = adx_vals[-1]
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score = max(0.0, min(100.0, adx_val))
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return {
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"adx": round(adx_val, 4),
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"plus_di": round(plus_di_last, 4),
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"minus_di": round(minus_di_last, 4),
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"score": round(score, 4),
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}
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def compute_ema(
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closes: list[float],
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period: int = 20,
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) -> dict[str, Any]:
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"""Compute EMA for *closes* with given *period*.
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Score: normalized position of latest close relative to EMA.
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Above EMA → higher score, below → lower.
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"""
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min_bars = period + 1
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if len(closes) < min_bars:
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raise ValidationError(
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f"EMA({period}) requires at least {min_bars} bars, got {len(closes)}"
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)
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ema_vals = _ema(closes, period)
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latest_ema = ema_vals[-1]
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latest_close = closes[-1]
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# Score: 50 = at EMA, 100 = 5%+ above, 0 = 5%+ below
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if latest_ema == 0:
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pct = 0.0
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else:
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pct = (latest_close - latest_ema) / latest_ema * 100.0
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score = max(0.0, min(100.0, 50.0 + pct * 10.0))
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return {
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"ema": round(latest_ema, 4),
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"period": period,
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"latest_close": round(latest_close, 4),
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"score": round(score, 4),
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}
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def _rsi_to_score(rsi: float) -> float:
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"""Map RSI to a 'healthy momentum' score, penalizing extremes.
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Raw RSI as a score treats 90 as maximally bullish, but RSI 90 is extreme
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overbought (exhaustion/reversal risk), not a green light. This peaks around
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RSI 60 (healthy uptrend) and falls off toward both ends, with the overbought
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side penalized harder than oversold (oversold can mean-revert upward).
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"""
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peak = 60.0
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if rsi <= peak:
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score = 90.0 - (peak - rsi) * 0.9 # 60→90, 30→63, 0→36
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else:
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score = 90.0 - (rsi - peak) * 1.6 # 60→90, 80→58, 90→42, 100→26
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return max(0.0, min(100.0, score))
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def compute_rsi(
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closes: list[float],
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period: int = 14,
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) -> dict[str, Any]:
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"""Compute RSI. Score is a peaked mapping (see _rsi_to_score), not raw RSI."""
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n = len(closes)
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if n < period + 1:
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raise ValidationError(
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f"RSI requires at least {period + 1} bars, got {n}"
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)
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deltas = [closes[i] - closes[i - 1] for i in range(1, n)]
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gains = [d if d > 0 else 0.0 for d in deltas]
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losses = [-d if d < 0 else 0.0 for d in deltas]
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avg_gain = sum(gains[:period]) / period
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avg_loss = sum(losses[:period]) / period
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for i in range(period, len(deltas)):
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avg_gain = (avg_gain * (period - 1) + gains[i]) / period
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avg_loss = (avg_loss * (period - 1) + losses[i]) / period
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if avg_loss == 0:
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rsi = 100.0
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else:
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rs = avg_gain / avg_loss
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rsi = 100.0 - 100.0 / (1.0 + rs)
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return {
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"rsi": round(rsi, 4),
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"period": period,
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"score": round(_rsi_to_score(rsi), 4),
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}
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def compute_atr(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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period: int = 14,
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) -> dict[str, Any]:
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"""Compute ATR. Score = normalized inverse (lower ATR = higher score)."""
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n = len(closes)
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if n < period + 1:
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raise ValidationError(
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f"ATR requires at least {period + 1} bars, got {n}"
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)
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tr_list: list[float] = []
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for i in range(1, n):
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h, low_val, pc = highs[i], lows[i], closes[i - 1]
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tr_list.append(max(h - low_val, abs(h - pc), abs(low_val - pc)))
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# Wilder smoothing
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atr = sum(tr_list[:period]) / period
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for tr in tr_list[period:]:
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atr = (atr * (period - 1) + tr) / period
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# Score: inverse normalized. ATR as % of price; lower = higher score.
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latest_close = closes[-1]
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if latest_close == 0:
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atr_pct = 0.0
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else:
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atr_pct = atr / latest_close * 100.0
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# 0% ATR → 100 score, 10%+ ATR → 0 score
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score = max(0.0, min(100.0, 100.0 - atr_pct * 10.0))
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return {
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"atr": round(atr, 4),
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"period": period,
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"atr_percent": round(atr_pct, 4),
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"score": round(score, 4),
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}
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def compute_volume_profile(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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volumes: list[int],
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num_bins: int = 20,
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) -> dict[str, Any]:
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"""Compute Volume Profile: POC, Value Area, HVN, LVN.
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Volume is assigned to the bin containing each bar's **close** (no
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double-counting across the high–low span).
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HVN = local peaks in the volume histogram (not every bin above mean).
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LVN = local valleys in the histogram.
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Score: proximity of latest close to POC (closer = higher).
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"""
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n = len(closes)
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if n < 20:
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raise ValidationError(
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f"Volume Profile requires at least 20 bars, got {n}"
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)
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price_min = min(lows)
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price_max = max(highs)
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if price_max == price_min:
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price_max = price_min + 1.0 # avoid zero-width range
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bin_width = (price_max - price_min) / num_bins
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bins: list[float] = [0.0] * num_bins
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bin_prices: list[float] = [
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price_min + (i + 0.5) * bin_width for i in range(num_bins)
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]
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# Assign each bar's full volume to the close's bin only.
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for i in range(n):
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c = closes[i]
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if c <= price_min:
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b = 0
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elif c >= price_max:
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b = num_bins - 1
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else:
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b = int((c - price_min) / bin_width)
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if b >= num_bins:
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b = num_bins - 1
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bins[b] += volumes[i]
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total_vol = sum(bins)
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if total_vol == 0:
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total_vol = 1.0
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# POC = bin with highest volume
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poc_idx = bins.index(max(bins))
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poc = round(bin_prices[poc_idx], 4)
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# Value Area: 70% of total volume around POC
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sorted_bins = sorted(range(num_bins), key=lambda i: bins[i], reverse=True)
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va_vol = 0.0
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va_indices: list[int] = []
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for idx in sorted_bins:
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va_vol += bins[idx]
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va_indices.append(idx)
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if va_vol >= total_vol * 0.7:
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break
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va_low = round(price_min + min(va_indices) * bin_width, 4)
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va_high = round(price_min + (max(va_indices) + 1) * bin_width, 4)
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# HVN / LVN: local peaks / valleys (require above/below mean to skip noise)
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avg_vol = total_vol / num_bins
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hvn: list[float] = []
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lvn: list[float] = []
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for i in range(num_bins):
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left = bins[i - 1] if i > 0 else bins[i]
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right = bins[i + 1] if i < num_bins - 1 else bins[i]
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if bins[i] > left and bins[i] > right and bins[i] > avg_vol:
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hvn.append(round(bin_prices[i], 4))
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elif bins[i] < left and bins[i] < right and bins[i] < avg_vol:
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lvn.append(round(bin_prices[i], 4))
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# Score: proximity of latest close to POC
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latest = closes[-1]
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price_range = price_max - price_min
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if price_range == 0:
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score = 100.0
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else:
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dist_pct = abs(latest - poc) / price_range
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score = max(0.0, min(100.0, 100.0 * (1.0 - dist_pct)))
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return {
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"poc": poc,
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"value_area_low": va_low,
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"value_area_high": va_high,
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"hvn": hvn,
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"lvn": lvn,
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"score": round(score, 4),
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}
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def compute_pivot_points(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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window: int = 2,
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min_prominence: float | None = None,
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) -> dict[str, Any]:
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"""Detect swing highs/lows as pivot points.
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A swing high at index *i* means highs[i] >= all highs in [i-window, i+window].
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When *min_prominence* is set, only keep swings whose window range
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(max high − min low) is at least that amount — filters tiny noise fractals.
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Score: based on number of pivots near current price.
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"""
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n = len(closes)
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if n < 5:
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raise ValidationError(
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f"Pivot Points requires at least 5 bars, got {n}"
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)
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swing_highs: list[float] = []
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swing_lows: list[float] = []
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for i in range(window, n - window):
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lo = i - window
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hi = i + window + 1
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# Swing high
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if all(highs[i] >= highs[j] for j in range(lo, hi)):
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if min_prominence is None or min_prominence <= 0:
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swing_highs.append(round(highs[i], 4))
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else:
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depth = highs[i] - min(lows[j] for j in range(lo, hi))
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if depth >= min_prominence:
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swing_highs.append(round(highs[i], 4))
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# Swing low
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if all(lows[i] <= lows[j] for j in range(lo, hi)):
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if min_prominence is None or min_prominence <= 0:
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swing_lows.append(round(lows[i], 4))
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else:
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depth = max(highs[j] for j in range(lo, hi)) - lows[i]
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if depth >= min_prominence:
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swing_lows.append(round(lows[i], 4))
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all_pivots = swing_highs + swing_lows
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latest = closes[-1]
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# Score: fraction of pivots within 2% of current price → 0-100
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if not all_pivots or latest == 0:
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score = 0.0
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else:
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near = sum(1 for p in all_pivots if abs(p - latest) / latest <= 0.02)
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score = min(100.0, (near / max(len(all_pivots), 1)) * 100.0)
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return {
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"swing_highs": swing_highs,
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"swing_lows": swing_lows,
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"pivot_count": len(all_pivots),
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"score": round(score, 4),
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}
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# Path labels for display only. Calibrated on the ~505-name prod snapshot
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# (2026-07, n=502 with full history): empirical p25 ≈ −0.082, p75 ≈ +0.004,
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# mean ≈ −0.043. Paper-style |ID| ≳ 0.25 almost never appears in live equities
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# (only ~0.2% of names); real momentum winners cluster around −0.04…−0.12.
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# Thresholds are therefore ~quartile cutoffs, not ±0.25 textbook extremes.
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# Distribution is left-skewed (bullish sample → more "continuous" than "discrete"),
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# so the discrete band is not symmetric.
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FIP_PATH_CONTINUOUS_MAX = -0.08 # ~p25: smoother quartile
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FIP_PATH_DISCRETE_MIN = 0.00 # ~p75: less-continuous quartile
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def compute_fip_id(closes: list[float], as_of_index: int | None = None) -> dict[str, Any]:
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"""Da/Gurun/Warachka information discreteness over the 12-1 formation window.
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Display / research context only — **not** used by the activation gate or
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production rank. Same window as residual 12-1 momentum: cumulative return
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from close[i-252] to close[i-21] (skip last month).
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ID = sign(PRET) × (%neg − %pos)
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Lower ID ⇒ smoother / more continuous path (for a winner: many small up days).
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Higher ID ⇒ jumpy / discrete path (few large moves).
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Zero-return days count in neither numerator but remain in the denominator
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(paper definition). Quirk: a flat series with one big jump can still land
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near zero ("mixed") because zeros dilute %pos/%neg — faithful to the paper
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and to real equities (exact zero daily returns are rare). Synthetic jump
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tests assert ordering vs a steady climber, not the discrete label itself.
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"""
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i = len(closes) - 1 if as_of_index is None else as_of_index
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if i < 252 or closes[i - 252] <= 0 or closes[i - 21] <= 0:
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raise ValidationError(
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f"FIP ID requires at least 253 bars with positive formation closes, "
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f"got {len(closes)}"
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)
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pret = closes[i - 21] / closes[i - 252] - 1.0
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rets: list[float] = []
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for k in range(i - 251, i - 20):
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prev = closes[k - 1]
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if prev <= 0:
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raise ValidationError("FIP ID requires positive closes in the formation window")
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rets.append(closes[k] / prev - 1.0)
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if len(rets) < 200:
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raise ValidationError(
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f"FIP ID requires ≥200 daily returns in formation, got {len(rets)}"
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)
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n = len(rets)
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pct_pos = sum(1 for r in rets if r > 0) / n
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pct_neg = sum(1 for r in rets if r < 0) / n
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if pret > 0:
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sign = 1.0
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elif pret < 0:
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sign = -1.0
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else:
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sign = 0.0
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fip = sign * (pct_neg - pct_pos)
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# Map observed ID range (~[-0.3, 0.15]) loosely to 0–100 for the card chrome;
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# lower ID (smoother) → higher score. Display only.
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score = max(0.0, min(100.0, 50.0 * (1.0 - fip)))
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if fip <= FIP_PATH_CONTINUOUS_MAX:
|
||
path = "continuous"
|
||
path_label = "smooth grind (continuous information)"
|
||
elif fip >= FIP_PATH_DISCRETE_MIN:
|
||
path = "discrete"
|
||
path_label = "jumpy path (discrete information)"
|
||
else:
|
||
path = "mixed"
|
||
path_label = "mixed path"
|
||
return {
|
||
"fip_id": round(fip, 4),
|
||
"pret_12_1": round(pret, 4),
|
||
"pct_up_days": round(pct_pos * 100.0, 1),
|
||
"pct_down_days": round(pct_neg * 100.0, 1),
|
||
"formation_days": n,
|
||
"path": path,
|
||
"path_label": path_label,
|
||
"display_only": True,
|
||
"note": "Not used by the production gate or rank — context only.",
|
||
"score": round(score, 4),
|
||
}
|
||
|
||
|
||
def compute_ema_cross(
|
||
closes: list[float],
|
||
short_period: int = 20,
|
||
long_period: int = 50,
|
||
tolerance: float = 1e-6,
|
||
) -> dict[str, Any]:
|
||
"""Compare short EMA vs long EMA.
|
||
|
||
Returns signal: bullish (short > long), bearish (short < long),
|
||
neutral (within tolerance).
|
||
"""
|
||
min_bars = long_period + 1
|
||
if len(closes) < min_bars:
|
||
raise ValidationError(
|
||
f"EMA Cross requires at least {min_bars} bars, got {len(closes)}"
|
||
)
|
||
|
||
short_ema_vals = _ema(closes, short_period)
|
||
long_ema_vals = _ema(closes, long_period)
|
||
|
||
short_ema = short_ema_vals[-1]
|
||
long_ema = long_ema_vals[-1]
|
||
|
||
diff = short_ema - long_ema
|
||
if abs(diff) <= tolerance:
|
||
signal = "neutral"
|
||
elif diff > 0:
|
||
signal = "bullish"
|
||
else:
|
||
signal = "bearish"
|
||
|
||
return {
|
||
"short_ema": round(short_ema, 4),
|
||
"long_ema": round(long_ema, 4),
|
||
"short_period": short_period,
|
||
"long_period": long_period,
|
||
"signal": signal,
|
||
}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Supported indicator types
|
||
# ---------------------------------------------------------------------------
|
||
|
||
INDICATOR_TYPES = {
|
||
"adx",
|
||
"ema",
|
||
"rsi",
|
||
"atr",
|
||
"volume_profile",
|
||
"pivot_points",
|
||
"fip_id",
|
||
}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Service-layer functions (DB + cache + validation)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _extract_ohlcv(records: list) -> tuple[
|
||
list[float], list[float], list[float], list[float], list[int]
|
||
]:
|
||
"""Extract parallel arrays from OHLCVRecord list."""
|
||
opens = [float(r.open) for r in records]
|
||
highs = [float(r.high) for r in records]
|
||
lows = [float(r.low) for r in records]
|
||
closes = [float(r.close) for r in records]
|
||
volumes = [int(r.volume) for r in records]
|
||
return opens, highs, lows, closes, volumes
|
||
|
||
|
||
async def get_indicator(
|
||
db: AsyncSession,
|
||
symbol: str,
|
||
indicator_type: str,
|
||
start_date: date | None = None,
|
||
end_date: date | None = None,
|
||
period: int | None = None,
|
||
) -> dict[str, Any]:
|
||
"""Compute a single indicator for *symbol*.
|
||
|
||
Checks cache first; stores result after computing.
|
||
"""
|
||
indicator_type = indicator_type.lower()
|
||
if indicator_type not in INDICATOR_TYPES:
|
||
raise ValidationError(
|
||
f"Unknown indicator type: {indicator_type}. "
|
||
f"Supported: {', '.join(sorted(INDICATOR_TYPES))}"
|
||
)
|
||
|
||
cache_key = (symbol.upper(), str(start_date), str(end_date), indicator_type)
|
||
cached = indicator_cache.get(cache_key)
|
||
if cached is not None:
|
||
return cached
|
||
|
||
records = await query_ohlcv(db, symbol, start_date, end_date)
|
||
_, highs, lows, closes, volumes = _extract_ohlcv(records)
|
||
n = len(records)
|
||
|
||
if indicator_type == "adx":
|
||
p = period or DEFAULT_PERIODS["adx"]
|
||
result = compute_adx(highs, lows, closes, period=p)
|
||
elif indicator_type == "ema":
|
||
p = period or DEFAULT_PERIODS["ema"]
|
||
result = compute_ema(closes, period=p)
|
||
elif indicator_type == "rsi":
|
||
p = period or DEFAULT_PERIODS["rsi"]
|
||
result = compute_rsi(closes, period=p)
|
||
elif indicator_type == "atr":
|
||
p = period or DEFAULT_PERIODS["atr"]
|
||
result = compute_atr(highs, lows, closes, period=p)
|
||
elif indicator_type == "volume_profile":
|
||
result = compute_volume_profile(highs, lows, closes, volumes)
|
||
elif indicator_type == "pivot_points":
|
||
result = compute_pivot_points(highs, lows, closes)
|
||
elif indicator_type == "fip_id":
|
||
result = compute_fip_id(closes)
|
||
else:
|
||
raise ValidationError(f"Unknown indicator type: {indicator_type}")
|
||
|
||
response = {
|
||
"indicator_type": indicator_type,
|
||
"values": {k: v for k, v in result.items() if k != "score"},
|
||
"score": result["score"],
|
||
"bars_used": n,
|
||
}
|
||
|
||
indicator_cache.set(cache_key, response)
|
||
return response
|
||
|
||
|
||
async def get_ema_cross(
|
||
db: AsyncSession,
|
||
symbol: str,
|
||
start_date: date | None = None,
|
||
end_date: date | None = None,
|
||
short_period: int = 20,
|
||
long_period: int = 50,
|
||
) -> dict[str, Any]:
|
||
"""Compute EMA cross signal for *symbol*."""
|
||
cache_key = (
|
||
symbol.upper(),
|
||
str(start_date),
|
||
str(end_date),
|
||
f"ema_cross_{short_period}_{long_period}",
|
||
)
|
||
cached = indicator_cache.get(cache_key)
|
||
if cached is not None:
|
||
return cached
|
||
|
||
records = await query_ohlcv(db, symbol, start_date, end_date)
|
||
_, _, _, closes, _ = _extract_ohlcv(records)
|
||
|
||
result = compute_ema_cross(closes, short_period, long_period)
|
||
|
||
indicator_cache.set(cache_key, result)
|
||
return result
|