Forward test: record forecasts before their outcomes exist.

`snapshot` writes each tracked model's forecast quantiles at the seven
backtest horizons from the latest price to data/forecasts/<origin>.csv. It
refuses stale data (older than two days) and duplicate dates, so snapshots
can't be reconstructed after the fact; committing them dates them.
`forward` scores every recorded forecast whose target date has passed,
reusing the backtest's scoring (now factored out as evaluate.score).

Tracked: random_walk, drift_rw, cycle, powerlaw, plus powerlaw_ou,
powerlaw_ou_param and cycle_on_powerlaw, which development data couldn't
settle. `just weekly` runs update, snapshot and forward.

First snapshot: 2026-09-23 (BTC $84.4K). The first outcomes are due
2026-10-23.

README.md

@@ -123,6 +123,23 @@ at 2-3 years (mean PIT 0.53-0.56) but with intervals too wide (its 80% interval
 held every 2- and 3-year outcome). The holdout is now spent for these models;
 a new model needs new data to be tested honestly.
 
+### Forward test (from 2026-09-23)
+
+New data is the only honest test left, so forecasts are now recorded before
+their outcomes exist. `just snapshot` writes every tracked model's forecast
+from the latest price to `data/forecasts/<date>.csv`. It refuses if the price
+data is more than two days old or the date already has a snapshot, and the
+files are committed so version control dates them. `just forward` scores
+every recorded forecast whose target date has passed, the same way the
+backtest does.
+
+Tracked: the four registered models plus `powerlaw_ou`, `powerlaw_ou_param`
+and `cycle_on_powerlaw`, which development data couldn't settle. Snapshots
+store the forecasts themselves, so if a model's definition changes it gets a
+new name. The routine is `just weekly` (update, snapshot, score), then commit
+the new snapshot. The first 1-month outcomes arrive 2026-10-23; 1-year results
+need until late 2027, and 4-year ones until 2030.
+
 ## Usage
 
 With Nix:
@@ -133,6 +150,7 @@ just update     # fetch new daily prices from Coinbase
 just backtest   # score models on development data -> output/backtest/
 just forecast   # forecast from the latest price -> output/forecast/
 just ab         # run A/B experiments -> output/ab/
+just weekly     # new prices, a forward-test snapshot, and its scores so far
 just test
 just holdout    # score on held-out outcomes; sparingly
 ```
@@ -149,6 +167,7 @@ btcmodel/
   forecast.py   Forecast (quantiles of log price), CRPS, PIT
   evaluate.py   walk-forward backtest and summary
   experiments.py  A/B tests: hypothesis, control, variants, verdict rule
+  forward.py    forward test: record forecasts, score them as outcomes arrive
   models/       drift, volatility and shape components; register models in __init__.py
   plots.py      fan chart, skill and calibration charts
 ```

btcmodel/__main__.py

@@ -6,6 +6,8 @@ Command line entry point.
     python -m btcmodel backtest --holdout  score models on outcomes after DEV_CUTOFF
     python -m btcmodel forecast            forecast from the latest price
     python -m btcmodel ab [NAME ...]       run A/B experiments on development data
+    python -m btcmodel snapshot            record tracked models' forecasts for the forward test
+    python -m btcmodel forward             score recorded forecasts whose targets have passed
 """
 
 import argparse
@@ -14,7 +16,7 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 
-from . import data, evaluate, experiments, plots
+from . import data, evaluate, experiments, forward, plots
 from .models import MODELS
 
 FORECAST_REPORT_HORIZONS = (182, 365, 730, 1095, 1460)
@@ -36,6 +38,8 @@ def main() -> None:
     commands.add_parser("forecast", help="forecast from the latest price")
     ab = commands.add_parser("ab", help="run A/B experiments on development data")
     ab.add_argument("names", nargs="*", help="experiments to run (default: all)")
+    commands.add_parser("snapshot", help="record forecasts for the forward test")
+    commands.add_parser("forward", help="score recorded forecasts")
     args = parser.parse_args()
     if args.command == "ab" and (unknown := set(args.names) - set(experiments.EXPERIMENTS)):
         parser.error(f"unknown experiments: {', '.join(sorted(unknown))}")
@@ -50,6 +54,10 @@ def main() -> None:
         run_forecast(models, args.output)
     elif args.command == "ab":
         run_ab(args.names or list(experiments.EXPERIMENTS), args.output)
+    elif args.command == "snapshot":
+        run_snapshot()
+    elif args.command == "forward":
+        run_forward(args.output)
 
 
 def run_backtest(models, output: Path, holdout: bool) -> None:
@@ -112,6 +120,46 @@ def run_ab(names: list[str], output: Path) -> None:
     (output / "ab" / "verdicts.txt").write_text(table + "\n")
 
 
+def run_snapshot() -> None:
+    prices = data.load_prices()
+    path = forward.snapshot(prices)
+    recorded = [(m, f) for m, f in forward.load_snapshots() if f.origin == prices.index[-1]]
+    medians = pd.DataFrame(
+        {m: [plots.price_formatter(p) for p in np.exp(f.quantile(0.5))] for m, f in recorded},
+        index=[evaluate.horizon_label(h) for h in evaluate.HORIZONS],
+    ).T
+    print(
+        f"recorded {len(recorded)} models' forecasts from {prices.index[-1]:%Y-%m-%d} in {path}\n"
+    )
+    print("medians:\n" + medians.to_string())
+
+
+def run_forward(output: Path) -> None:
+    prices = data.load_prices()
+    scores = forward.score_snapshots(prices)
+    due = forward.next_due(prices)
+    if scores.empty:
+        when = f"; the first is due {due:%Y-%m-%d}" if due is not None else ""
+        print(f"no recorded forecast has reached its target date yet{when}")
+        return
+    out = output / "forward"
+    out.mkdir(parents=True, exist_ok=True)
+    summary = evaluate.summarize(scores, step_days=forward.SNAPSHOT_STEP_DAYS)
+    report = (
+        f"forward test: {scores.origin.nunique()} snapshots from {scores.origin.min():%Y-%m-%d}, "
+        f"outcomes through {prices.index[-1]:%Y-%m-%d}\n\n" + evaluate.format_summary(summary)
+    )
+    if due is not None:
+        report += f"\n\nnext outcome due {due:%Y-%m-%d}"
+    print(report)
+    (out / "report.txt").write_text(report + "\n")
+    scores.to_csv(out / "scores.csv", index=False)
+    summary.to_csv(out / "summary.csv", index=False)
+    plots.skill_chart(summary, out / "skill.png", "forward test: skill by horizon")
+    plots.calibration_chart(summary, out / "calibration.png", "forward test: interval coverage")
+    print(f"\nwrote {out}/")
+
+
 def run_forecast(models, output: Path) -> None:
     prices = data.load_prices()
     horizons = np.arange(1, max(FORECAST_REPORT_HORIZONS) + 1)

btcmodel/evaluate.py

@@ -12,7 +12,7 @@ influence model design. The holdout run scores only targets after DEV_CUTOFF.
 import numpy as np
 import pandas as pd
 
-from .forecast import crps, pit
+from .forecast import Forecast, crps, pit
 from .models import BASELINE
 
 HORIZONS = np.array([30, 91, 182, 365, 730, 1095, 1460])
@@ -38,31 +38,39 @@ def backtest(
         history = data.loc[:origin]
         outcome = log_close.loc[targets[scored]].to_numpy()
         for model in models:
-            f = model.forecast(history, horizons[scored])
-            row = {
-                "model": model.name,
-                "origin": origin,
-                "horizon": f.horizons,
-                "outcome": outcome,
-                "median": f.quantile(0.5),
-                "crps": crps(f.log_quantiles, outcome),
-                "pit": pit(f.log_quantiles, outcome),
-            }
-            for c in COVERAGES:
-                lo, hi = f.interval(c)
-                row[f"in{c:.0%}"] = (lo <= outcome) & (outcome <= hi)
-            rows.append(pd.DataFrame(row))
+            rows.append(score(model.name, model.forecast(history, horizons[scored]), outcome))
     return pd.concat(rows, ignore_index=True)
 
 
+def score(model: str, f: Forecast, outcome: np.ndarray) -> pd.DataFrame:
+    """Score one forecast against the observed log prices at its horizons."""
+    row = {
+        "model": model,
+        "origin": f.origin,
+        "horizon": f.horizons,
+        "outcome": outcome,
+        "median": f.quantile(0.5),
+        "crps": crps(f.log_quantiles, outcome),
+        "pit": pit(f.log_quantiles, outcome),
+    }
+    for c in COVERAGES:
+        lo, hi = f.interval(c)
+        row[f"in{c:.0%}"] = (lo <= outcome) & (outcome <= hi)
+    return pd.DataFrame(row)
+
+
 def summarize(
-    scores: pd.DataFrame, baseline: str = BASELINE, n_boot: int = 2000, seed: int = 0
+    scores: pd.DataFrame,
+    baseline: str = BASELINE,
+    step_days: int = ORIGIN_STEP_DAYS,
+    n_boot: int = 2000,
+    seed: int = 0,
 ) -> pd.DataFrame:
     """
     Per model and horizon: mean CRPS, skill relative to `baseline`, and coverage.
 
     Skill is 1 - CRPS / baseline CRPS (positive = better than the baseline),
-    with a 90% moving-block bootstrap interval over origins. Forecasts from
+    with a 90% block bootstrap interval over origins spaced `step_days` apart. Forecasts from
     nearby origins overlap heavily, so `windows` (the span covered divided by
     the horizon) is the honest count of independent outcomes. Treat intervals
     with fewer than ~5 windows as optimistic.
@@ -72,7 +80,7 @@ def summarize(
     for horizon, at_h in scores.groupby("horizon"):
         base = at_h[at_h.model == baseline].set_index("origin")["crps"].sort_index()
         span = (base.index[-1] - base.index[0]).days + horizon
-        block = max(1, min(int(np.ceil(horizon / ORIGIN_STEP_DAYS)), len(base) // 2))
+        block = max(1, min(int(np.ceil(horizon / step_days)), len(base) // 2))
         boot_index = _block_bootstrap_indices(len(base), block, n_boot, rng)
         for model, g in at_h.groupby("model", sort=False):
             m = g.set_index("origin")["crps"].reindex(base.index).to_numpy()

btcmodel/forward.py

@@ -0,0 +1,104 @@
+"""
+Forward test: forecasts recorded before their outcomes exist.
+
+`snapshot` stores every tracked model's forecast from the latest price in
+data/forecasts/<origin>.csv. The files are committed, so version control shows
+each one was written before its outcome was known. `score_snapshots` grades
+every recorded forecast whose target date has passed, exactly as the backtest
+does.
+
+Snapshots store the forecast quantiles themselves, so model code can change
+without rewriting the record. If a model's definition changes, give it a new
+name rather than reusing the old one.
+"""
+
+import datetime as dt
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from .data import DATA_DIR
+from .evaluate import HORIZONS, score
+from .experiments import EXPERIMENTS
+from .forecast import LEVELS, Forecast
+from .models import MODELS
+
+FORECASTS_DIR = DATA_DIR / "forecasts"
+# A snapshot must be made from fresh data, not reconstructed after the fact.
+MAX_DATA_AGE_DAYS = 2
+# Intended cadence (weekly), used to size bootstrap blocks when scoring.
+SNAPSHOT_STEP_DAYS = 7
+
+_VARIANTS = {m.name: m for e in EXPERIMENTS.values() for m in e.models}
+# The registered models, plus candidates that could only be settled on new data.
+TRACKED = [
+    *MODELS.values(),
+    *(_VARIANTS[n] for n in ("powerlaw_ou", "powerlaw_ou_param", "cycle_on_powerlaw")),
+]
+
+# Named by percentile: p0.5, p1.5, ..., p99.5 (the grid has no exact median).
+_QUANTILE_COLUMNS = [f"p{100 * level:.1f}" for level in LEVELS]
+
+
+def snapshot(
+    prices: pd.DataFrame,
+    models=TRACKED,
+    directory: Path = FORECASTS_DIR,
+    today: pd.Timestamp | None = None,
+) -> Path:
+    """Record each model's forecast from the last price in `prices`."""
+    origin = prices.index[-1]
+    if today is None:
+        today = pd.Timestamp(dt.datetime.now(dt.UTC).date())
+    if (today - origin).days > MAX_DATA_AGE_DAYS:
+        raise ValueError(
+            f"latest price is from {origin:%Y-%m-%d}; update the data first "
+            "(forecasts must be recorded in real time)"
+        )
+    path = directory / f"{origin:%Y-%m-%d}.csv"
+    if path.exists():
+        raise FileExistsError(f"{path} already exists")
+
+    frames = []
+    for model in models:
+        f = model.forecast(prices, HORIZONS)
+        frame = pd.DataFrame(f.log_quantiles, columns=_QUANTILE_COLUMNS)
+        frame.insert(0, "model", model.name)
+        frame.insert(1, "horizon", f.horizons)
+        frame.insert(2, "target", f.dates.strftime("%Y-%m-%d"))
+        frames.append(frame)
+    directory.mkdir(parents=True, exist_ok=True)
+    pd.concat(frames).to_csv(path, index=False, float_format="%.6f")
+    return path
+
+
+def load_snapshots(directory: Path = FORECASTS_DIR) -> list[tuple[str, Forecast]]:
+    """Every recorded forecast, as (model name, Forecast)."""
+    forecasts = []
+    for path in sorted(directory.glob("*.csv")):
+        origin = pd.Timestamp(path.stem)
+        for model, g in pd.read_csv(path).groupby("model", sort=False):
+            quantiles = g[_QUANTILE_COLUMNS].to_numpy()
+            forecasts.append((model, Forecast(origin, g["horizon"].to_numpy(), quantiles)))
+    return forecasts
+
+
+def score_snapshots(prices: pd.DataFrame, directory: Path = FORECASTS_DIR) -> pd.DataFrame:
+    """Score every recorded forecast horizon whose target date is in `prices`."""
+    log_close = np.log(prices["close"])
+    rows = []
+    for model, f in load_snapshots(directory):
+        due = f.dates <= prices.index[-1]
+        if not due.any():
+            continue
+        due_forecast = Forecast(f.origin, f.horizons[due], f.log_quantiles[due])
+        outcome = log_close.loc[due_forecast.dates].to_numpy()
+        rows.append(score(model, due_forecast, outcome))
+    return pd.concat(rows, ignore_index=True) if rows else pd.DataFrame()
+
+
+def next_due(prices: pd.DataFrame, directory: Path = FORECASTS_DIR) -> pd.Timestamp | None:
+    """The earliest target date not yet observed, if any."""
+    pending = [d for _, f in load_snapshots(directory) for d in f.dates if d > prices.index[-1]]
+    return min(pending) if pending else None

data/forecasts/2026-09-23.csv

@@ -0,0 +1,50 @@
+model,horizon,target,p0.5,p1.5,p2.5,p3.5,p4.5,p5.5,p6.5,p7.5,p8.5,p9.5,p10.5,p11.5,p12.5,p13.5,p14.5,p15.5,p16.5,p17.5,p18.5,p19.5,p20.5,p21.5,p22.5,p23.5,p24.5,p25.5,p26.5,p27.5,p28.5,p29.5,p30.5,p31.5,p32.5,p33.5,p34.5,p35.5,p36.5,p37.5,p38.5,p39.5,p40.5,p41.5,p42.5,p43.5,p44.5,p45.5,p46.5,p47.5,p48.5,p49.5,p50.5,p51.5,p52.5,p53.5,p54.5,p55.5,p56.5,p57.5,p58.5,p59.5,p60.5,p61.5,p62.5,p63.5,p64.5,p65.5,p66.5,p67.5,p68.5,p69.5,p70.5,p71.5,p72.5,p73.5,p74.5,p75.5,p76.5,p77.5,p78.5,p79.5,p80.5,p81.5,p82.5,p83.5,p84.5,p85.5,p86.5,p87.5,p88.5,p89.5,p90.5,p91.5,p92.5,p93.5,p94.5,p95.5,p96.5,p97.5,p98.5,p99.5
+random_walk,30,2026-10-23,11.007321,11.060207,11.087595,11.106893,11.122080,11.134750,11.145711,11.155431,11.164207,11.172239,11.179670,11.186606,11.193124,11.199288,11.205145,11.210737,11.216095,11.221247,11.226215,11.231019,11.235676,11.240199,11.244602,11.248895,11.253088,11.257190,11.261209,11.265151,11.269023,11.272831,11.276580,11.280275,11.283920,11.287520,11.291077,11.294596,11.298080,11.301533,11.304956,11.308354,11.311727,11.315081,11.318415,11.321734,11.325038,11.328332,11.331616,11.334892,11.338164,11.341432,11.344699,11.347968,11.351239,11.354516,11.357800,11.361093,11.364398,11.367716,11.371051,11.374404,11.377778,11.381175,11.384599,11.388051,11.391535,11.395054,11.398612,11.402211,11.405856,11.409551,11.413300,11.417108,11.420980,11.424923,11.428941,11.433044,11.437237,11.441530,11.445932,11.450456,11.455112,11.459916,11.464884,11.470036,11.475394,11.480986,11.486844,11.493007,11.499526,11.506461,11.513892,11.521925,11.530701,11.540420,11.551381,11.564051,11.579238,11.598536,11.625925,11.678811
+random_walk,91,2026-12-23,10.758317,10.850425,10.898127,10.931737,10.958187,10.980254,10.999344,11.016272,11.031556,11.045546,11.058489,11.070568,11.081920,11.092655,11.102857,11.112596,11.121928,11.130901,11.139554,11.147921,11.156030,11.163908,11.171576,11.179053,11.186356,11.193500,11.200500,11.207366,11.214110,11.220742,11.227272,11.233707,11.240055,11.246324,11.252520,11.258649,11.264717,11.270730,11.276692,11.282609,11.288486,11.294325,11.300133,11.305913,11.311669,11.317404,11.323124,11.328830,11.334528,11.340220,11.345911,11.351603,11.357301,11.363008,11.368727,11.374463,11.380218,11.385998,11.391806,11.397646,11.403522,11.409439,11.415401,11.421414,11.427482,11.433611,11.439807,11.446076,11.452424,11.458859,11.465389,11.472021,11.478766,11.485632,11.492631,11.499775,11.507078,11.514555,11.522223,11.530101,11.538211,11.546578,11.555230,11.564203,11.573535,11.583274,11.593476,11.604211,11.615564,11.627642,11.640585,11.654575,11.669859,11.686788,11.705878,11.727944,11.754395,11.788005,11.835706,11.927815
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+powerlaw_ou_param,1095,2029-09-22,11.337474,11.540910,11.646266,11.720499,11.778919,11.827657,11.869820,11.907209,11.940967,11.971865,12.000452,12.027129,12.052204,12.075913,12.098446,12.119956,12.140568,12.160385,12.179496,12.197976,12.215887,12.233287,12.250222,12.266736,12.282866,12.298646,12.314105,12.329270,12.344166,12.358814,12.373236,12.387448,12.401470,12.415315,12.429000,12.442537,12.455940,12.469220,12.482389,12.495457,12.508436,12.521334,12.534161,12.546927,12.559639,12.572307,12.584939,12.597543,12.610128,12.622700,12.635269,12.647841,12.660425,12.673029,12.685661,12.698330,12.711042,12.723808,12.736635,12.749533,12.762512,12.775580,12.788749,12.802029,12.815432,12.828969,12.842653,12.856499,12.870520,12.884733,12.899155,12.913803,12.928699,12.943864,12.959323,12.975103,12.991233,13.007747,13.024682,13.042081,13.059993,13.078473,13.097584,13.117401,13.138013,13.159522,13.182056,13.205765,13.230840,13.257517,13.286104,13.317002,13.350760,13.388149,13.430312,13.479050,13.537469,13.611703,13.717059,13.920495
+powerlaw_ou_param,1460,2030-09-22,11.556555,11.767714,11.877071,11.954123,12.014760,12.065348,12.109112,12.147921,12.182960,12.215032,12.244703,12.272394,12.298420,12.323030,12.346419,12.368745,12.390139,12.410709,12.430546,12.449727,12.468318,12.486378,12.503957,12.521098,12.537840,12.554219,12.570265,12.586006,12.601467,12.616672,12.631641,12.646393,12.660947,12.675318,12.689522,12.703573,12.717485,12.731269,12.744938,12.758503,12.771974,12.785362,12.798676,12.811926,12.825121,12.838270,12.851382,12.864465,12.877527,12.890576,12.903622,12.916672,12.929734,12.942816,12.955928,12.969077,12.982272,12.995522,13.008837,13.022225,13.035696,13.049260,13.062929,13.076714,13.090625,13.104676,13.118880,13.133252,13.147805,13.162558,13.177527,13.192731,13.208193,13.223934,13.239979,13.256358,13.273100,13.290241,13.307820,13.325880,13.344472,13.363653,13.383489,13.404059,13.425453,13.447780,13.471168,13.495778,13.521804,13.549495,13.579167,13.611238,13.646278,13.685086,13.728850,13.779439,13.840076,13.917127,14.026484,14.237643
+cycle_on_powerlaw,30,2026-10-23,11.013061,11.065946,11.093335,11.112633,11.127820,11.140490,11.151451,11.161171,11.169946,11.177979,11.185410,11.192345,11.198864,11.205027,11.210885,11.216477,11.221835,11.226987,11.231955,11.236759,11.241416,11.245939,11.250341,11.254634,11.258828,11.262930,11.266948,11.270891,11.274763,11.278571,11.282320,11.286015,11.289660,11.293259,11.296817,11.300336,11.303820,11.307273,11.310696,11.314093,11.317467,11.320820,11.324155,11.327473,11.330778,11.334071,11.337355,11.340632,11.343903,11.347172,11.350439,11.353707,11.356979,11.360256,11.363539,11.366833,11.370137,11.373456,11.376791,11.380144,11.383518,11.386915,11.390338,11.393791,11.397275,11.400794,11.404351,11.407951,11.411596,11.415291,11.419040,11.422848,11.426720,11.430662,11.434681,11.438783,11.442977,11.447270,11.451672,11.456195,11.460852,11.465656,11.470624,11.475776,11.481134,11.486726,11.492583,11.498747,11.505265,11.512201,11.519632,11.527664,11.536440,11.546160,11.557121,11.569791,11.584978,11.604276,11.631665,11.684550
+cycle_on_powerlaw,91,2026-12-23,10.794570,10.886678,10.934380,10.967990,10.994440,11.016507,11.035597,11.052525,11.067809,11.081799,11.094742,11.106821,11.118173,11.128908,11.139110,11.148849,11.158181,11.167154,11.175807,11.184174,11.192283,11.200161,11.207829,11.215306,11.222609,11.229753,11.236753,11.243619,11.250363,11.256996,11.263525,11.269960,11.276308,11.282577,11.288773,11.294902,11.300970,11.306983,11.312946,11.318863,11.324739,11.330579,11.336386,11.342166,11.347922,11.353657,11.359377,11.365083,11.370781,11.376474,11.382164,11.387856,11.393554,11.399261,11.404980,11.410716,11.416472,11.422251,11.428059,11.433899,11.439775,11.445692,11.451654,11.457667,11.463735,11.469864,11.476060,11.482329,11.488677,11.495113,11.501642,11.508274,11.515019,11.521885,11.528884,11.536028,11.543332,11.550808,11.558476,11.566354,11.574464,11.582831,11.591484,11.600456,11.609788,11.619527,11.629729,11.640464,11.651817,11.663896,11.676838,11.690828,11.706112,11.723041,11.742131,11.764198,11.790648,11.824258,11.871959,11.964068
+cycle_on_powerlaw,182,2027-03-24,10.727596,10.857857,10.925317,10.972849,11.010255,11.041463,11.068460,11.092400,11.114016,11.133800,11.152104,11.169186,11.185241,11.200422,11.214850,11.228623,11.241821,11.254510,11.266747,11.278579,11.290048,11.301189,11.312033,11.322607,11.332935,11.343039,11.352937,11.362648,11.372186,11.381565,11.390799,11.399900,11.408878,11.417743,11.426505,11.435173,11.443755,11.452258,11.460690,11.469058,11.477368,11.485627,11.493840,11.502014,11.510154,11.518266,11.526354,11.534424,11.542482,11.550532,11.558580,11.566630,11.574688,11.582758,11.590847,11.598958,11.607098,11.615272,11.623485,11.631744,11.640054,11.648422,11.656854,11.665357,11.673939,11.682607,11.691369,11.700235,11.709213,11.718313,11.727547,11.736927,11.746465,11.756175,11.766073,11.776177,11.786505,11.797079,11.807923,11.819064,11.830533,11.842366,11.854602,11.867292,11.880489,11.894262,11.908690,11.923871,11.939927,11.957009,11.975313,11.995097,12.016712,12.040653,12.067650,12.098857,12.136263,12.183795,12.251255,12.381516
+cycle_on_powerlaw,365,2027-09-23,10.459584,10.644053,10.739588,10.806900,10.859873,10.904067,10.942299,10.976203,11.006813,11.034831,11.060752,11.084943,11.107680,11.129179,11.149611,11.169115,11.187805,11.205775,11.223105,11.239861,11.256103,11.271880,11.287237,11.302211,11.316837,11.331146,11.345164,11.358915,11.372422,11.385705,11.398782,11.411669,11.424384,11.436938,11.449347,11.461622,11.473775,11.485817,11.497759,11.509609,11.521377,11.533073,11.544704,11.556280,11.567807,11.579294,11.590748,11.602177,11.613588,11.624989,11.636385,11.647786,11.659197,11.670626,11.682080,11.693567,11.705095,11.716670,11.728301,11.739997,11.751765,11.763616,11.775557,11.787599,11.799752,11.812027,11.824436,11.836991,11.849705,11.862592,11.875669,11.888952,11.902459,11.916211,11.930228,11.944537,11.959163,11.974137,11.989494,12.005271,12.021513,12.038270,12.055599,12.073569,12.092259,12.111763,12.132196,12.153695,12.176431,12.200622,12.226543,12.254561,12.285171,12.319075,12.357307,12.401501,12.454474,12.521787,12.617321,12.801790
+cycle_on_powerlaw,730,2028-09-22,10.483953,10.744833,10.879939,10.975133,11.050048,11.112548,11.166616,11.214563,11.257853,11.297476,11.334134,11.368345,11.400499,11.430904,11.459799,11.487383,11.513814,11.539227,11.563735,11.587432,11.610402,11.632714,11.654432,11.675609,11.696293,11.716528,11.736352,11.755800,11.774902,11.793686,11.812180,11.830406,11.848387,11.866142,11.883690,11.901050,11.918237,11.935267,11.952154,11.968913,11.985556,12.002096,12.018545,12.034916,12.051218,12.067463,12.083662,12.099825,12.115962,12.132085,12.148202,12.164325,12.180462,12.196626,12.212824,12.229070,12.245372,12.261742,12.278191,12.294731,12.311374,12.328133,12.345020,12.362050,12.379237,12.396597,12.414146,12.431901,12.449881,12.468107,12.486601,12.505386,12.524488,12.543935,12.563759,12.583994,12.604679,12.625856,12.647573,12.669885,12.692855,12.716552,12.741060,12.766473,12.792905,12.820488,12.849384,12.879788,12.911943,12.946153,12.982811,13.022434,13.065724,13.113671,13.167740,13.230240,13.305154,13.400349,13.535454,13.796334
+cycle_on_powerlaw,1095,2029-09-22,10.671517,10.991027,11.156497,11.273086,11.364838,11.441384,11.507604,11.566327,11.619346,11.667874,11.712771,11.754670,11.794052,11.831289,11.866679,11.900461,11.932833,11.963958,11.993973,12.022997,12.051128,12.078456,12.105054,12.130990,12.156324,12.181107,12.205386,12.229204,12.252599,12.275605,12.298255,12.320578,12.342599,12.364345,12.385837,12.407098,12.428148,12.449005,12.469688,12.490213,12.510597,12.530854,12.551000,12.571050,12.591015,12.610912,12.630751,12.650547,12.670311,12.690057,12.709797,12.729543,12.749307,12.769103,12.788943,12.808839,12.828805,12.848854,12.869000,12.889257,12.909641,12.930166,12.950849,12.971706,12.992756,13.014017,13.035510,13.057255,13.079277,13.101599,13.124249,13.147256,13.170650,13.194468,13.218748,13.243531,13.268864,13.294800,13.321399,13.348726,13.376857,13.405881,13.435896,13.467021,13.499393,13.533175,13.568565,13.605803,13.645184,13.687083,13.731980,13.780508,13.833527,13.892250,13.958470,14.035017,14.126768,14.243357,14.408827,14.728338
+cycle_on_powerlaw,1460,2030-09-22,10.110988,10.479927,10.670995,10.805621,10.911566,10.999954,11.076419,11.144226,11.205447,11.261482,11.313325,11.361706,11.407180,11.450178,11.491042,11.530051,11.567431,11.603371,11.638030,11.671543,11.704026,11.735581,11.766294,11.796243,11.825495,11.854112,11.882147,11.909650,11.936664,11.963230,11.989384,12.015159,12.040588,12.065697,12.090515,12.115065,12.139371,12.163455,12.187337,12.211038,12.234575,12.257966,12.281229,12.304380,12.327434,12.350408,12.373317,12.396175,12.418997,12.441798,12.464591,12.487392,12.510214,12.533072,12.555981,12.578955,12.602010,12.625160,12.648423,12.671814,12.695351,12.719052,12.742934,12.767018,12.791324,12.815874,12.840692,12.865801,12.891230,12.917005,12.943159,12.969725,12.996739,13.024242,13.052277,13.080894,13.110146,13.140095,13.170808,13.202363,13.234846,13.268360,13.303018,13.338958,13.376338,13.415347,13.456212,13.499210,13.544683,13.593064,13.644907,13.700942,13.762163,13.829970,13.906435,13.994823,14.100768,14.235394,14.426462,14.795401

justfile

@@ -36,3 +36,14 @@ clean:
 # Run A/B experiments on development data (default: all)
 ab *names:
     python -m btcmodel ab {{ names }}
+
+# Record tracked models' forecasts for the forward test (commit the new file)
+snapshot:
+    python -m btcmodel snapshot
+
+# Score recorded forecasts whose target dates have passed
+forward:
+    python -m btcmodel forward
+
+# The weekly routine: new prices, a new snapshot, and scores so far
+weekly: update snapshot forward

tests/test_forward.py

@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from btcmodel import forward
+from btcmodel.evaluate import HORIZONS, score
+from btcmodel.models import MODELS
+
+from .test_models import synthetic_prices
+
+MODELS_UNDER_TEST = [MODELS["random_walk"], MODELS["powerlaw"]]
+
+
+@pytest.fixture
+def prices():
+    return synthetic_prices(lambda day: 0.001 + 0 * day, end="2025-03-31", noise=0.03)
+
+
+def test_snapshot_records_every_model_and_horizon(prices, tmp_path):
+    history = prices.loc[:"2024-11-26"]
+    path = forward.snapshot(history, MODELS_UNDER_TEST, tmp_path, pd.Timestamp("2024-11-27"))
+    recorded = pd.read_csv(path)
+    assert path.name == "2024-11-26.csv"
+    assert len(recorded) == len(MODELS_UNDER_TEST) * len(HORIZONS)
+    assert recorded.target.iloc[0] == "2024-12-26"
+
+
+def test_snapshot_refuses_stale_data_and_duplicates(prices, tmp_path):
+    history = prices.loc[:"2024-11-26"]
+    with pytest.raises(ValueError, match="real time"):
+        forward.snapshot(history, MODELS_UNDER_TEST, tmp_path, pd.Timestamp("2024-12-10"))
+    forward.snapshot(history, MODELS_UNDER_TEST, tmp_path, pd.Timestamp("2024-11-27"))
+    with pytest.raises(FileExistsError):
+        forward.snapshot(history, MODELS_UNDER_TEST, tmp_path, pd.Timestamp("2024-11-27"))
+
+
+def test_scores_only_due_horizons_and_match_the_backtest(prices, tmp_path):
+    history = prices.loc[:"2024-11-26"]
+    forward.snapshot(history, MODELS_UNDER_TEST, tmp_path, pd.Timestamp("2024-11-27"))
+
+    assert forward.score_snapshots(prices.loc[:"2024-12-20"], tmp_path).empty
+    assert forward.next_due(prices.loc[:"2024-12-20"], tmp_path) == pd.Timestamp("2024-12-26")
+
+    scores = forward.score_snapshots(prices, tmp_path)
+    assert sorted(scores.horizon.unique()) == [30, 91]
+
+    model = MODELS["powerlaw"]
+    horizons = np.array([30, 91])
+    direct = score(
+        "powerlaw",
+        model.forecast(history, horizons),
+        np.log(
+            prices.close.loc[history.index[-1] + pd.to_timedelta(horizons, unit="D")]
+        ).to_numpy(),
+    )
+    recorded = scores[scores.model == "powerlaw"].sort_values("horizon")
+    np.testing.assert_allclose(recorded.crps, direct.crps, atol=1e-5)