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213bb911e3
scoring.py docstring promises: 'Missing indicators are excluded and the remaining weights renormalised — so a single dead API doesn't drag the whole score toward zero.' But _stablecoin_supply_signal and _open_interest_signal returned 0.0 (not None) as placeholders — so they stayed in the alive set, kept their combined 0.10 weight in the denominator, and contributed 0, systematically compressing the composite toward zero by ~10%. Example: ahr999 deeply cheap (+1) alone should score +100, but the two 0.0 placeholders dragged it to 80. Now they return None → excluded + renormalised, matching the module's own stated design. Verified: same input now scores 100. Re-activate either by returning a real [-1,+1] signal once a trend lookup is wired in. 72 tests pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
156 lines
6.2 KiB
Python
156 lines
6.2 KiB
Python
"""Composite "regime" score over the macro indicators.
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Goal: one -100..+100 number that says whether the overall macro setup tilts
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risk-on (positive) or risk-off (negative). Used purely as advisory display on
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the BTC page — does NOT (yet) feed sizing or trade decisions.
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Design choices:
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* Each indicator contributes a per-indicator signal in [-1, +1].
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* Weights sum to 1.0 across the indicators we got. Missing indicators are
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excluded and the remaining weights are renormalised — so a single dead
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API doesn't drag the whole score toward zero.
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* The contrarian indicators (fear/greed, AHR999, funding) are intentionally
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inverted: extreme fear / cheap AHR999 / extreme funding all add positive
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score (buy-the-fear logic).
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The weights here are seeded by hand from rough conviction. Re-tune after a
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few weeks of live data — see `composite_score` history vs realised forward
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returns.
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"""
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from __future__ import annotations
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from typing import Optional
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# (weight, signal_function) per indicator. signal_function takes the raw value
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# and returns a number in [-1, +1].
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#
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# Inputs may be None if the upstream fetch failed; the orchestrator filters
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# them out and renormalises before summing.
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def _ahr999_signal(v: Optional[float]) -> Optional[float]:
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"""< 0.45 cheap → +1; 0.45–1.2 neutral; > 1.2 expensive → -1."""
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if v is None: return None
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if v < 0.45: return 1.0
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if v > 1.2: return -1.0
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# Linear interpolation between 0.45 and 1.2 → +1 to -1.
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return 1.0 - 2 * ((v - 0.45) / (1.2 - 0.45))
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def _altseason_signal(v: Optional[float]) -> Optional[float]:
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"""High altseason index = risk-on (+1), low = BTC season (defensive but
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not bearish, so a mild positive)."""
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if v is None: return None
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if v >= 75: return 0.7 # altseason — generally bullish risk
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if v <= 25: return 0.3 # BTC-only — defensive but not a bear signal
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return (v - 50) / 50 # linear, -0.5 to +0.5
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def _fear_greed_signal(v: Optional[int]) -> Optional[float]:
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"""Contrarian: extreme fear → buy (+1), extreme greed → sell (-1)."""
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if v is None: return None
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# Map 0..100 → +1..-1 (inverted, contrarian).
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return (50 - v) / 50
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def _btc_dominance_signal(v: Optional[float]) -> Optional[float]:
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"""Hard to read in isolation — only inform on extremes.
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Very high dominance often signals fear (risk-off into BTC) → mild bearish.
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Very low = altseason froth → also mild bearish (cycle late). Mid = neutral."""
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if v is None: return None
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if v >= 60: return -0.3
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if v <= 40: return -0.3
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return 0.0
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def _eth_btc_signal(v: Optional[float]) -> Optional[float]:
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"""Rising ETH/BTC = risk-on. No persistent absolute level matters; this is
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really a trend indicator. We approximate with absolute thresholds for the
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current cycle (2025-2026): > 0.04 risk-on, < 0.025 risk-off."""
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if v is None: return None
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if v >= 0.04: return 0.7
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if v <= 0.025: return -0.7
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return (v - 0.0325) / 0.0075 * 0.7 # linear in the middle
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def _stablecoin_supply_signal(v: Optional[float]) -> Optional[float]:
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"""Absolute supply tells us little day-over-day; we need the delta, which
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this snapshot-only scorer doesn't have yet.
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Return None (NOT 0.0) so this indicator is EXCLUDED from the weighted sum
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and its weight is renormalised away — exactly the "a dead indicator must
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not drag the score toward zero" rule stated in the module docstring.
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Returning 0.0 would keep its 0.05 weight in the denominator and silently
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compress every other indicator's contribution. Wire in a trend lookup to
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re-activate it."""
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return None
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def _etf_flow_signal(v: Optional[float]) -> Optional[float]:
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"""Net inflow = institutional bid → +1, outflow → -1. Scale by magnitude."""
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if v is None: return None
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# Daily prints over $200M are notable; over $500M unusually large.
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abs_v = abs(v)
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sign = 1 if v > 0 else (-1 if v < 0 else 0)
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if abs_v >= 500_000_000: return 1.0 * sign
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if abs_v >= 200_000_000: return 0.7 * sign
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if abs_v >= 50_000_000: return 0.4 * sign
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return 0.1 * sign
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def _open_interest_signal(v: Optional[float]) -> Optional[float]:
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"""OI in isolation doesn't tell us direction — we'd need OI vs price
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correlation. Return None (NOT 0.0) until we have a trend window, so this
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indicator is excluded + its weight renormalised away rather than diluting
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every other indicator. (Same reasoning as _stablecoin_supply_signal.)"""
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return None
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# Weights (sum to 1.0 across all). When an indicator is missing, we drop its
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# weight and renormalise the rest.
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WEIGHTS = {
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"ahr999": 0.20,
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"altcoin_season": 0.10,
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"fear_greed": 0.20,
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"btc_dominance": 0.05,
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"eth_btc": 0.15,
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"stablecoin_supply": 0.05,
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"etf_flow": 0.20,
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"btc_open_interest": 0.05,
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}
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def compute_composite(values: dict) -> tuple[Optional[float], Optional[str]]:
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"""Return (score in [-100, +100], regime_label) or (None, None) if there
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isn't enough data to score.
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`values` keys must match WEIGHTS keys (without "_signal" suffix).
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"""
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pairs = [
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("ahr999", _ahr999_signal(values.get("ahr999"))),
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("altcoin_season", _altseason_signal(values.get("altcoin_season_index"))),
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("fear_greed", _fear_greed_signal(values.get("fear_greed"))),
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("btc_dominance", _btc_dominance_signal(values.get("btc_dominance_pct"))),
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("eth_btc", _eth_btc_signal(values.get("eth_btc_ratio"))),
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("stablecoin_supply", _stablecoin_supply_signal(values.get("stablecoin_supply_usd"))),
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("etf_flow", _etf_flow_signal(values.get("etf_flow_net_usd_1d"))),
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("btc_open_interest", _open_interest_signal(values.get("btc_open_interest_usd"))),
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]
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alive = [(k, s) for k, s in pairs if s is not None]
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if not alive:
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return None, None
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total_w = sum(WEIGHTS[k] for k, _ in alive)
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if total_w <= 0:
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return None, None
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score = sum(WEIGHTS[k] * s for k, s in alive) / total_w * 100
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if score >= 60: label = "BULL"
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elif score >= 20: label = "BULLISH"
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elif score > -20: label = "NEUTRAL"
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elif score > -60: label = "BEARISH"
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else: label = "BEAR"
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return round(score, 1), label
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