e385765fda
- Bracket table now shows Avg 30d, 90d, 180d, and 1yr columns - Signal events show all 4 timeframes - Current context shows all 4 average returns - Comparable examples show all available timeframes - Updated backtest screenshot
418 lines
14 KiB
Python
418 lines
14 KiB
Python
"""Historical backtest engine for Bitcoin Accumulation Zone scoring."""
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import json
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import logging
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import os
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import sys
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from collections import defaultdict
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from datetime import datetime, timedelta
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log = logging.getLogger(__name__)
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BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, BASE_DIR)
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HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
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CACHE_PATH = os.path.join(BASE_DIR, "data", "cache.json")
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# Score brackets matching the dashboard assessment levels
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BRACKETS = [
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(0, 20, "Extreme Caution"),
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(21, 40, "Caution"),
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(41, 55, "Neutral"),
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(56, 70, "Moderate Opportunity"),
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(71, 85, "Strong Accumulation"),
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(86, 100, "Extreme Accumulation"),
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]
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# Scoring thresholds — replicated from scoring/engine.py for standalone use
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METRIC_SCORERS = {
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"fear_greed": {
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"ranges": [[None, 10, 10], [10, 25, 7], [25, 45, 4], [45, 55, 2], [55, 75, 1], [75, None, 0]],
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},
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"puell_multiple": {
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"ranges": [[None, 0.3, 10], [0.3, 0.5, 8], [0.5, 0.8, 5], [0.8, 1.2, 3], [1.2, 2.0, 1], [2.0, None, 0]],
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},
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"mvrv_zscore": {
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"ranges": [[None, 0, 10], [0, 0.5, 8], [0.5, 1.5, 5], [1.5, 3, 2], [3, 5, 1], [5, None, 0]],
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},
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"reserve_risk": {
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"ranges": [[None, 0.002, 10], [0.002, 0.005, 7], [0.005, 0.01, 4], [0.01, 0.02, 2], [0.02, None, 0]],
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},
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"rhodl_ratio": {
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"ranges": [[None, 100, 10], [100, 500, 7], [500, 2000, 4], [2000, 10000, 1], [10000, None, 0]],
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},
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"nupl": {
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"ranges": [[None, 0, 10], [0, 0.25, 7], [0.25, 0.5, 4], [0.5, 0.75, 1], [0.75, None, 0]],
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},
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}
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# Ratio-based metrics: score based on price vs reference value
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RATIO_SCORERS = {
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"price_vs_200w_sma": {
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# pct_above ranges
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"ranges": [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, 100, 1], [100, None, 0]],
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"price_key": "btc_price",
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"ref_key": "200w_sma",
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},
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"lth_realized_price": {
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"ranges": [[None, 0, 10], [0, 20, 6], [20, 50, 3], [50, None, 1]],
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"price_key": "btc_price",
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"ref_key": "lth_realized_price",
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},
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}
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# Drawdown scoring
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DRAWDOWN_RANGES = [[70, None, 10], [50, 70, 8], [30, 50, 6], [20, 30, 4], [10, 20, 2], [None, 10, 0]]
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def _score_range(value, ranges):
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"""Score a value using range-based thresholds."""
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if value is None:
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return None
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for low, high, score in ranges:
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low_ok = low is None or value >= low
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high_ok = high is None or value < high
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if low_ok and high_ok:
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return score
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return 0
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def _build_daily_index(history):
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"""Build a dict mapping metric_key -> {date_str: value} for fast lookup."""
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index = {}
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for key, data in history.items():
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if key.startswith("_") or not isinstance(data, dict) or "dates" not in data:
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continue
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lookup = {}
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for d, v in zip(data["dates"], data["values"]):
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lookup[d] = v
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index[key] = lookup
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return index
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def _get_all_dates(index):
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"""Get sorted union of all dates across all metrics."""
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all_dates = set()
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for lookup in index.values():
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all_dates.update(lookup.keys())
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return sorted(all_dates)
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def _last_known_value(lookup, date, max_lookback=30):
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"""Get value for date, or most recent prior value within lookback window."""
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if date in lookup:
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return lookup[date]
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d = datetime.strptime(date, "%Y-%m-%d")
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for i in range(1, max_lookback + 1):
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prev = (d - timedelta(days=i)).strftime("%Y-%m-%d")
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if prev in lookup:
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return lookup[prev]
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return None
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def _compute_ath_series(price_lookup, dates):
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"""Compute running ATH and drawdown for each date."""
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ath = 0
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drawdowns = {}
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for d in dates:
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p = price_lookup.get(d)
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if p is None:
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continue
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if p > ath:
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ath = p
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if ath > 0:
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drawdowns[d] = ((ath - p) / ath) * 100
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return drawdowns
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def score_day(date, index, drawdowns):
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"""Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics)."""
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scores = []
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details = {}
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# Simple range-based metrics
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for metric_key, cfg in METRIC_SCORERS.items():
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val = _last_known_value(index.get(metric_key, {}), date)
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if val is not None:
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s = _score_range(val, cfg["ranges"])
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if s is not None:
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scores.append(s)
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details[metric_key] = {"value": val, "score": s}
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# Ratio-based metrics (price vs reference)
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for metric_key, cfg in RATIO_SCORERS.items():
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price_val = _last_known_value(index.get(cfg["price_key"], {}), date)
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# Try alternate price keys
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if price_val is None:
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for pk in ["btc_price_coingecko", "btc_price_sma", "btc_price_lth"]:
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price_val = _last_known_value(index.get(pk, {}), date)
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if price_val is not None:
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break
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ref_val = _last_known_value(index.get(cfg["ref_key"], {}), date)
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if price_val is not None and ref_val is not None and ref_val > 0:
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pct_above = ((price_val - ref_val) / ref_val) * 100
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s = _score_range(pct_above, cfg["ranges"])
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if s is not None:
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scores.append(s)
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details[metric_key] = {"value": pct_above, "score": s}
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# Drawdown
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dd = drawdowns.get(date)
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if dd is not None:
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s = _score_range(dd, DRAWDOWN_RANGES)
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if s is not None:
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scores.append(s)
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details["drawdown"] = {"value": dd, "score": s}
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if not scores:
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return None, details, 0
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composite = sum(scores) / len(scores) * 10
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return round(composite, 1), details, len(scores)
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def compute_forward_returns(price_lookup, dates_sorted):
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"""Precompute forward returns for all dates."""
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periods = [30, 90, 180, 365]
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returns = {}
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for d in dates_sorted:
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p0 = price_lookup.get(d)
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if p0 is None or p0 <= 0:
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continue
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r = {}
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dt = datetime.strptime(d, "%Y-%m-%d")
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for days in periods:
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future = (dt + timedelta(days=days)).strftime("%Y-%m-%d")
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pf = price_lookup.get(future)
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if pf is not None:
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r[f"{days}d"] = round(((pf - p0) / p0) * 100, 2)
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if r:
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returns[d] = r
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return returns
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def compute_max_drawdown_forward(price_lookup, date, window=90):
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"""Compute max drawdown within N days after a given date."""
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dt = datetime.strptime(date, "%Y-%m-%d")
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p0 = price_lookup.get(date)
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if p0 is None or p0 <= 0:
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return None
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peak = p0
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max_dd = 0
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for i in range(1, window + 1):
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future = (dt + timedelta(days=i)).strftime("%Y-%m-%d")
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pf = price_lookup.get(future)
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if pf is None:
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continue
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if pf > peak:
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peak = pf
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dd = ((peak - pf) / peak) * 100
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if dd > max_dd:
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max_dd = dd
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return round(max_dd, 2) if max_dd > 0 else 0
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def run_backtest():
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"""Run the full backtest and return comprehensive results."""
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log.info("Loading historical data...")
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if not os.path.exists(HISTORY_PATH):
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return {"error": "No historical data found. Run history collector first."}
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with open(HISTORY_PATH) as f:
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history = json.load(f)
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index = _build_daily_index(history)
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# Build price lookup (prefer coingecko for completeness)
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price_lookup = {}
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for pk in ["btc_price_coingecko", "btc_price", "btc_price_sma", "btc_price_lth"]:
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if pk in index:
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for d, v in index[pk].items():
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if d not in price_lookup:
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price_lookup[d] = v
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all_dates = _get_all_dates(index)
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if not all_dates:
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return {"error": "No date data available."}
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log.info("Date range: %s to %s (%d days)", all_dates[0], all_dates[-1], len(all_dates))
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# Compute drawdowns
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drawdowns = _compute_ath_series(price_lookup, all_dates)
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# Precompute forward returns
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log.info("Computing forward returns...")
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fwd_returns = compute_forward_returns(price_lookup, all_dates)
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# Score each day
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log.info("Scoring %d days...", len(all_dates))
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daily_scores = []
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for d in all_dates:
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composite, details, n_metrics = score_day(d, index, drawdowns)
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if composite is not None and n_metrics >= 3: # Require at least 3 metrics
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price = price_lookup.get(d)
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entry = {
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"date": d,
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"score": composite,
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"n_metrics": n_metrics,
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"price": price,
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"forward_returns": fwd_returns.get(d, {}),
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}
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daily_scores.append(entry)
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if not daily_scores:
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return {"error": "No scored days (insufficient metric overlap)."}
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log.info("Scored %d days with 3+ metrics", len(daily_scores))
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# --- Bracket statistics ---
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bracket_stats = []
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for low, high, label in BRACKETS:
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days_in = [d for d in daily_scores if low <= d["score"] <= high]
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if not days_in:
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bracket_stats.append({
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"range": f"{low}-{high}", "label": label, "days": 0,
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})
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continue
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stats = {"range": f"{low}-{high}", "label": label, "days": len(days_in)}
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for period in ["30d", "90d", "180d", "365d"]:
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returns = [d["forward_returns"][period] for d in days_in if period in d["forward_returns"]]
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if returns:
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returns_sorted = sorted(returns)
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stats[f"avg_{period}"] = round(sum(returns) / len(returns), 2)
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stats[f"median_{period}"] = round(returns_sorted[len(returns_sorted) // 2], 2)
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stats[f"win_rate_{period}"] = round(len([r for r in returns if r > 0]) / len(returns) * 100, 1)
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stats[f"max_gain_{period}"] = round(max(returns), 2)
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stats[f"max_loss_{period}"] = round(min(returns), 2)
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stats[f"n_{period}"] = len(returns)
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# Average max drawdown within 90 days
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dd_list = []
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for d in days_in:
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dd = compute_max_drawdown_forward(price_lookup, d["date"], 90)
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if dd is not None:
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dd_list.append(dd)
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if dd_list:
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stats["avg_max_drawdown_90d"] = round(sum(dd_list) / len(dd_list), 2)
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bracket_stats.append(stats)
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# --- Peak signal events ---
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signal_events = []
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thresholds = [90, 80, 70]
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for thresh in thresholds:
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prev_score = 0
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for d in daily_scores:
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if d["score"] >= thresh and prev_score < thresh:
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event = {
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"date": d["date"],
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"score": d["score"],
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"threshold": thresh,
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"price": d["price"],
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"forward_returns": d["forward_returns"],
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}
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# Add future prices
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if d["price"]:
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dt = datetime.strptime(d["date"], "%Y-%m-%d")
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for days_ahead in [30, 90, 365]:
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future = (dt + timedelta(days=days_ahead)).strftime("%Y-%m-%d")
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fp = price_lookup.get(future)
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if fp:
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event[f"price_{days_ahead}d"] = round(fp, 2)
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signal_events.append(event)
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prev_score = d["score"]
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signal_events.sort(key=lambda e: e["date"])
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# --- Current signal context ---
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all_scores_list = [d["score"] for d in daily_scores]
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all_scores_list.sort()
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# Get current score from cache
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current_score = None
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current_price = None
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if os.path.exists(CACHE_PATH):
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try:
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with open(CACHE_PATH) as f:
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cache = json.load(f)
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scored = cache.get("_scored", {})
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current_score = scored.get("composite_score")
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current_price = cache.get("price", {}).get("price")
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except Exception:
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pass
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# If no cache, use latest daily score
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if current_score is None and daily_scores:
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current_score = daily_scores[-1]["score"]
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current_price = daily_scores[-1].get("price")
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current_context = None
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if current_score is not None:
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# Percentile
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below = len([s for s in all_scores_list if s <= current_score])
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percentile = round(below / len(all_scores_list) * 100, 1)
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# Find comparable historical periods
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comparable = []
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margin = 5
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for d in daily_scores:
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if abs(d["score"] - current_score) <= margin and d["forward_returns"]:
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comparable.append(d)
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avg_returns = {}
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if comparable:
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for period in ["30d", "90d", "180d", "365d"]:
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vals = [d["forward_returns"][period] for d in comparable if period in d["forward_returns"]]
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if vals:
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avg_returns[period] = round(sum(vals) / len(vals), 2)
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avg_1yr = avg_returns.get("365d")
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# Best comparable examples (most recent 5)
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examples = []
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for d in comparable[-5:]:
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examples.append({
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"date": d["date"],
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"score": d["score"],
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"price": d["price"],
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"forward_returns": d["forward_returns"],
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})
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current_context = {
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"current_score": current_score,
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"current_price": current_price,
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"percentile": percentile,
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"comparable_days": len(comparable),
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"avg_1yr_return": avg_1yr,
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"avg_30d_return": avg_returns.get("30d"),
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"avg_90d_return": avg_returns.get("90d"),
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"avg_180d_return": avg_returns.get("180d"),
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"examples": examples,
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}
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# --- Build time series for charting ---
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# Downsample to weekly for chart efficiency
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chart_data = []
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for i, d in enumerate(daily_scores):
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# Include every 7th day + last day
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if i % 7 == 0 or i == len(daily_scores) - 1:
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chart_data.append({
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"date": d["date"],
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"score": d["score"],
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"price": d["price"],
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})
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result = {
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"date_range": {"start": daily_scores[0]["date"], "end": daily_scores[-1]["date"]},
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"total_days_scored": len(daily_scores),
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"bracket_stats": bracket_stats,
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"signal_events": signal_events,
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"current_context": current_context,
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"chart_data": chart_data,
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"computed_at": datetime.utcnow().isoformat() + "Z",
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}
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log.info("Backtest complete: %d days, %d signal events", len(daily_scores), len(signal_events))
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return result
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