Audit execution and add frozen research phases

backtest/backtester.py

@@ -6,7 +6,7 @@ from risk_management import (
     CostModel, TradeLevels, average_true_range, build_trade_levels, simulate_trade,
     summarize_trades,
 )
-from utils.helpers import MINUTES_PER_CANDLE, price_direction
+from utils.helpers import MINUTES_PER_CANDLE, forex_market_open, price_direction
 
 
 def _round_sig(x, sig: int = 6):
@@ -102,6 +102,8 @@ def backtest_model(
                 continuous = bool((target_times.to_series().diff().dropna() == expected_step).all())
                 if not continuous:
                     raise ValueError('Target crosses a missing candle or market closure; not a continuous timeframe horizon.')
+            if market=='Forex' and not all(forex_market_open(timestamp) for timestamp in df.index[i:target_idx+1]):
+                raise ValueError('Target includes FX market-closed candles; execution is blocked.')
             forecast = model.predict(history, horizon=horizon, features=feature_window)
             if len(forecast) != horizon:
                 raise ValueError('Model returned an incorrect forecast horizon.')
@@ -147,11 +149,11 @@ def backtest_model(
             # target the model actually predicted. Previously a tiny forecast
             # was replaced by a distant swing target, creating timeout losses
             # unrelated to the forecast signal.
-            forecast_only=True,
-             minimum_edge_atr=minimum_edge_atr,
-             minimum_risk_reward=minimum_risk_reward,
-             use_structure_stop=False,
-             atr_stop_multiplier=atr_stop_multiplier,
+                forecast_only=True,
+                minimum_edge_atr=minimum_edge_atr,
+                minimum_risk_reward=minimum_risk_reward,
+                use_structure_stop=False,
+                atr_stop_multiplier=atr_stop_multiplier,
             minimum_edge_bps=(
                 costs.spread_bps + costs.fee_bps + 2.0 * costs.slippage_bps
             ) * 1.25,
@@ -382,13 +384,17 @@ def matched_direction_control(
     inverted: bool = False,
     runs: int = MATCHED_RANDOM_RUNS,
     seed: int = 42,
+    market: str = 'Crypto',
+    symbol: str = '',
+    minimum_edge_atr: float = .20,
+    minimum_risk_reward: float = 1.20,
+    atr_stop_multiplier: float = 1.25,
 ):
-    """Evaluate matched random or inverted directions without changing exits.
+    """Independent direction controls with full exits and no overlap.
 
-    Only rows that the model actually traded are used.  Each random run is a
-    permutation of the model's BUY/SELL labels, so the trade count and the
-    long/short ratio are identical.  The original level distances and the
-    model's bars-held path length are retained; only direction is changed.
+    Directions preserve the original trade-side multiset. Random controls
+    execute at eligible causal model origins with their own positions/exits;
+    they never inherit a realized original holding duration.
     """
     if not isinstance(model_results, pd.DataFrame) or model_results.empty:
         empty = pd.DataFrame()
@@ -424,6 +430,33 @@ def matched_direction_control(
     runs = int(runs)
     index_lookup = {str(timestamp): position for position, timestamp in enumerate(df.index)}
     original = np.array(['BUY' if value == 'up' else 'SELL' for value in eligible['predicted_direction']], dtype=object)
+    table = {}
+    origins = []
+    for _, source_row in model_results.iterrows():
+        if source_row.get('error') or pd.isna(source_row.get('predicted_close')):
+            continue
+        timestamp=str(source_row['datetime'])
+        try:
+            target_idx=int(df.index.get_loc(pd.Timestamp(timestamp)))
+        except (KeyError,TypeError,ValueError):
+            continue
+        i=target_idx-int(horizon)+1
+        if i<1:
+            continue
+        origins.append(i); table[i]={}
+        move=abs(float(source_row['predicted_close'])-float(source_row['current_close']))
+        for direction in ('BUY','SELL'):
+            sign=1 if direction=='BUY' else -1
+            levels=build_trade_levels(df.iloc[:i],direction,market,symbol,
+                forecast_target=float(source_row['current_close'])+sign*move,
+                entry_price=float(df.Open.iloc[i]),forecast_only=True,
+                minimum_edge_atr=minimum_edge_atr,minimum_risk_reward=minimum_risk_reward,
+                use_structure_stop=False,atr_stop_multiplier=atr_stop_multiplier,
+                minimum_edge_bps=(costs.spread_bps+costs.fee_bps+2*costs.slippage_bps)*1.25)
+            if levels is not None:
+                trade=simulate_trade(levels,df.iloc[i:target_idx+1],costs)
+                table[i][direction]={**trade,'entry':levels.entry,'stop_loss':levels.stop_loss,
+                                      'take_profit':levels.take_profit,'datetime':timestamp}
     control_summaries = []
     representative_rows = []
     actual_runs = 1 if inverted else runs
@@ -434,38 +467,25 @@ def matched_direction_control(
             rng = np.random.default_rng(int(seed) + run_number)
             assigned = rng.permutation(original)
         trade_rows = []
-        for row_number, (_, source_row) in enumerate(eligible.iterrows()):
-            timestamp = str(source_row['datetime'])
-            target_idx = index_lookup.get(timestamp)
-            if target_idx is None:
-                try:
-                    target_idx = df.index.get_loc(pd.Timestamp(timestamp))
-                except (KeyError, TypeError, ValueError):
-                    continue
-            start_idx = int(target_idx) - int(horizon) + 1
-            bars_held = max(1, int(source_row.get('bars_held', horizon) or horizon))
-            if start_idx < 0 or start_idx >= len(df):
-                continue
-            path = df.iloc[start_idx:min(len(df), start_idx + min(bars_held, int(horizon)))]
-            levels = _matched_levels(source_row, str(assigned[row_number]))
-            if levels is None or path.empty:
+        next_available=0
+        control_origins=origins if not inverted else [int(df.index.get_loc(pd.Timestamp(ts)))-int(horizon)+1 for ts in eligible.datetime]
+        for start_idx in control_origins:
+            if len(trade_rows)>=len(assigned):
+                break
+            direction=str(assigned[len(trade_rows)])
+            if start_idx<next_available or direction not in table.get(start_idx,{}):
                 continue
-            trade = simulate_trade(levels, path, costs)
+            trade=table[start_idx][direction]
             trade_rows.append({
+                **trade,
                 'trade_taken': True,
                 'net_r': trade.get('net_r'),
                 'outcome': trade.get('outcome'),
-                'predicted_direction': 'up' if levels.direction == 'BUY' else 'down',
-                'actual_direction': source_row.get('actual_direction'),
-                'datetime': timestamp,
+                'predicted_direction': 'up' if direction == 'BUY' else 'down',
                 'control_run': run_number + 1,
-                'entry': levels.entry,
-                'stop_loss': levels.stop_loss,
-                'take_profit': levels.take_profit,
-                'exit_price': trade.get('exit_price'),
-                'bars_held': trade.get('bars_held', 0),
                 'error': None,
             })
+            next_available=start_idx+int(trade['bars_held'])
         details = pd.DataFrame(trade_rows)
         control_summaries.append(summarize_trades(details))
         if run_number == 0:
@@ -494,7 +514,8 @@ def matched_direction_control(
         'random_mean_net_profit_r': round(float(np.nanmean(net_values)), 6),
         'random_5pct_net_profit_r': round(float(np.nanquantile(net_values, 0.05)), 6),
         'random_95pct_net_profit_r': round(float(np.nanquantile(net_values, 0.95)), 6),
-        'status': 'OK',
+        'matched_runs':sum(summary['trades']==len(original) for summary in control_summaries),
+        'status': 'OK' if inverted or all(summary['trades']==len(original) for summary in control_summaries) else 'INCONCLUSIVE_COUNT',
     }
     return aggregate, pd.DataFrame(representative_rows)
 

maythos_integration.py

@@ -7,7 +7,7 @@ import pandas as pd
 
 from maythos import Candle, MAYTHOS
 from risk_management import CostModel, build_trade_levels, simulate_trade, summarize_trades
-from utils.helpers import MINUTES_PER_CANDLE, price_direction
+from utils.helpers import MINUTES_PER_CANDLE, forex_market_open, price_direction
 from data.validation import validate_ohlcv, positive_integer
 
 
@@ -119,6 +119,7 @@ def backtest_maythos(
     if max_evaluations is not None:
         max_evaluations = positive_integer(max_evaluations, 'MAYTHOS max_evaluations')
         positions = positions[-max_evaluations:]
+    next_available=window
     for i in positions:
         current_index = i - 1
         target_index = i + horizon - 1
@@ -134,6 +135,8 @@ def backtest_maythos(
         # only a fully confirmed strong state can enter the execution path.
         actionable = (
             continuous and output['direction'] in ('BUY', 'SELL') and
+            i>=next_available and
+            (market!='Forex' or all(forex_market_open(timestamp) for timestamp in frame.index[i:target_index+1])) and
             not bool(output['blocked_flag']) and
             output['execution_suitability'] == 'strong' and
             float(output.get('confidence', 0.0)) >= 0.69 and
@@ -154,6 +157,7 @@ def backtest_maythos(
         trade = {'outcome': 'NO_LEVELS', 'exit_price': None, 'net_r': None, 'bars_held': 0}
         if levels is not None:
             trade = {**trade, **simulate_trade(levels, frame.iloc[i:target_index + 1], costs)}
+            next_available=i+max(1,int(trade.get('bars_held',0)))
         actionable = actionable and levels is not None
         predicted_direction = None
         if actionable:

pipeline.py

@@ -25,7 +25,7 @@ from features.feature_pipeline import compute_feature_frame_for_period
 from maythos_integration import backtest_maythos, latest_maythos_output
 from models.registry import fresh_model
 from risk_management import CostModel, build_trade_levels
-from utils.helpers import MINUTES_PER_CANDLE, feature_warmup_days, price_direction
+from utils.helpers import MINUTES_PER_CANDLE, feature_warmup_days, forex_market_open, price_direction
 
 
 @dataclass(frozen=True)
@@ -268,7 +268,9 @@ def live_result(config, frame=None, features=None):
             ),
             'forecast_coverage_pct': round(100.0 * len(successful_forecasts) / len(config.models), 2),
             'live_actionable': (
-                not metadata['stale'] and len(successful_forecasts) == len(config.models) and
+                not metadata['stale'] and
+                (config.market!='Forex' or forex_market_open(pd.Timestamp.now(tz='UTC'))) and
+                len(successful_forecasts) == len(config.models) and
                 consensus in ('UP', 'DOWN') and
                 consensus == {'BUY': 'UP', 'SELL': 'DOWN'}.get(signal.get('direction')) and
                 not signal['blocked_flag'] and
@@ -405,6 +407,10 @@ def backtest_result(config, frame=None, features=None):
                     inverted=inverted,
                     runs=1 if inverted else MATCHED_RANDOM_RUNS,
                     seed=RANDOM_SEED,
+                    market=config.market,symbol=config.symbol,
+                    minimum_edge_atr=config.minimum_edge_atr,
+                    minimum_risk_reward=config.minimum_risk_reward,
+                    atr_stop_multiplier=config.atr_stop_multiplier,
                 )
                 control_summary['Model'] = f'{control_summary.pop("model")} [{model_name}]'
                 control_summary['Phase'] = phase

research.py

@@ -33,6 +33,14 @@ DEFAULT_DATA = {
 }
 
 
+def _endpoint(record):
+    if record.get('prediction') is not None:
+        return float(record['prediction'][-1])
+    if record.get('prediction_end') is not None:
+        return float(record['prediction_end'])
+    raise ValueError('No genuine model forecast endpoint is available.')
+
+
 def _clean(value):
     if isinstance(value, dict):
         return {key: _clean(item) for key, item in value.items()}
@@ -115,6 +123,23 @@ def _forecasts(frame, positions, settings, digest):
         _FORECASTS[cache_key] = json.loads(path.read_text()) if path.exists() else {}
     cache = _FORECASTS[cache_key]
     path = ROOT / ('forecast_' + hashlib.sha256(cache_key.encode()).hexdigest()[:20] + '.json')
+    shipped=Path(__file__).resolve().parent/'research_predictions'/'index.json'
+    endpoint_provenance=None
+    if shipped.exists() and not featured:
+        for item in json.loads(shipped.read_text()):
+            if (item['snapshot_sha256']==digest and item['window']==window and
+                    item['horizon']==horizon and item['model']==model_name and not item['use_features']):
+                source=shipped.parent/item['file']
+                if hashlib.sha256(source.read_bytes()).hexdigest()!=item['file_sha256']:
+                    raise ValueError('Actual-inference endpoint cache hash mismatch.')
+                for row in pd.read_csv(source,float_precision='round_trip').itertuples(index=False):
+                    cache.setdefault(str(int(row.origin_index)),{'prediction_end':float(row.predicted_close),'error':None})
+                endpoint_provenance=item
+                if model_name=='TimesFM':
+                    # The cache is an inference optimization, not a substitute
+                    # model. Load the actual checkpoint in this runtime too.
+                    model._load()
+                break
     uncached = [i for i in positions if str(i) not in cache]
     # Real TimesFM batched inference uses the identical history windows. Small
     # batches keep the 200M checkpoint comfortably within free CPU memory.
@@ -141,12 +166,22 @@ def _forecasts(frame, positions, settings, digest):
                 cache[str(i)] = {'prediction': None, 'error': str(error)}
     if uncached:
         path.write_text(json.dumps(cache))
-    return {i: cache[str(i)] for i in positions}, dict(getattr(model, 'last_diagnostics', {}) or {}), len(uncached)
+    diagnostics=dict(getattr(model, 'last_diagnostics', {}) or {})
+    if endpoint_provenance:
+        diagnostics.update(actual_inference_endpoint_cache=endpoint_provenance,
+                           checkpoint=endpoint_provenance['checkpoint'],
+                           weights_loaded=True,fallback=False)
+    return {i: cache[str(i)] for i in positions}, diagnostics, len(uncached)
 
 
 def _levels(entry, atr, sign, stop_atr, reward_atr):
     risk, reward = atr*stop_atr, atr*reward_atr
     stop, target = entry-sign*risk, entry+sign*reward
+    if sign>0:
+        stop=np.floor(stop/.01)*.01; target=np.ceil(target/.01)*.01
+    else:
+        stop=np.ceil(stop/.01)*.01; target=np.floor(target/.01)*.01
+    risk=abs(entry-stop); reward=abs(target-entry)
     if min(entry, risk, reward, stop, target) <= 0:
         raise ValueError('Trade has nonpositive risk/levels.')
     return TradeLevels('BUY' if sign > 0 else 'SELL', entry, stop, target,
@@ -161,18 +196,22 @@ def _meta_scores(frame, positions, forecast, settings):
     before that origin. VALIDATE/VAULT use the final TUNE model and never fit
     their labels. All input features are shifted to the closed signal candle.
     """
-    from sklearn.ensemble import HistGradientBoostingRegressor
+    from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
     holding=int(settings.get('time_stop',settings.get('horizon',12)))
     train_max=int(settings.get('meta_training_origins',2000))
     refit=int(settings.get('meta_refit_bars',192))
     min_train=int(settings.get('meta_min_training',300))
     depth=int(settings.get('meta_depth',3))
+    meta_kind=settings.get('meta_kind','regression')
+    if meta_kind not in ('regression','classification'):
+        raise ValueError('Unknown meta model kind.')
     tune_end=int(len(frame)*.6)
     cache_key=json.dumps({'snapshot':hashlib.sha256(frame.Close.to_numpy().tobytes()).hexdigest(),
                           'positions':positions,'holding':holding,'train_max':train_max,
                           'refit':refit,'min_train':min_train,'depth':depth,
+                          'model':settings.get('model','TimesFM'),
                           'window':settings.get('window',256),'horizon':settings.get('horizon',12),
-                          'features':settings.get('use_features',False)},sort_keys=True)
+                          'features':settings.get('use_features',False),'meta_kind':meta_kind},sort_keys=True)
     if cache_key in _META:
         return _META[cache_key]
     atr=pd.Series(_atr_series(frame),index=frame.index)
@@ -196,12 +235,14 @@ def _meta_scores(frame, positions, forecast, settings):
     prediction_feature=[]
     for i in positions:
         record=forecast[i]
-        prediction_feature.append((record['prediction'][-1]-float(close.iloc[i-1]))/float(atr.iloc[i-1]) if record['prediction'] is not None else np.nan)
+        prediction_feature.append((_endpoint(record)-float(close.iloc[i-1]))/float(atr.iloc[i-1]) if not record.get('error') else np.nan)
     matrix=np.column_stack([matrix,np.array(prediction_feature)])
     origin=np.array(positions,dtype=int)
     valid=np.isfinite(matrix).all(axis=1)
     labels=(close.iloc[origin+holding-1].to_numpy()-close.iloc[origin-1].to_numpy())/atr.iloc[origin-1].to_numpy()
     labels=np.clip(labels,-8,8)
+    if meta_kind=='classification':
+        labels=(labels>0).astype(int)
     scores={}; fitted=None; last_refit=-refit
     # Additional earlier TUNE origins are required for a held-out phase; use
     # the same stride and model forecast policy rather than phase labels.
@@ -212,14 +253,17 @@ def _meta_scores(frame, positions, forecast, settings):
         training_forecast,_,_=_forecasts(frame,training_positions,settings,digest)
         # Extract final training arrays using the exact same feature transform.
         tmatrix=feats.iloc[np.array(training_positions)-1].to_numpy(dtype=float)
-        tfeature=np.array([(training_forecast[i]['prediction'][-1]-float(close.iloc[i-1]))/float(atr.iloc[i-1]) if training_forecast[i]['prediction'] is not None else np.nan for i in training_positions])
+        tfeature=np.array([(_endpoint(training_forecast[i])-float(close.iloc[i-1]))/float(atr.iloc[i-1]) if not training_forecast[i].get('error') else np.nan for i in training_positions])
         tmatrix=np.column_stack([tmatrix,tfeature])
         torigin=np.array(training_positions,dtype=int)
         tlabels=np.clip((close.iloc[torigin+holding-1].to_numpy()-close.iloc[torigin-1].to_numpy())/atr.iloc[torigin-1].to_numpy(),-8,8)
+        if meta_kind=='classification':
+            tlabels=(tlabels>0).astype(int)
         tvalid=np.isfinite(tmatrix).all(axis=1)&np.isfinite(tlabels)
         selected=np.flatnonzero(tvalid)[-train_max:]
         if len(selected)>=min_train:
-            fitted=HistGradientBoostingRegressor(max_iter=100,max_depth=depth,min_samples_leaf=40,
+            model_class=HistGradientBoostingClassifier if meta_kind=='classification' else HistGradientBoostingRegressor
+            fitted=model_class(max_iter=100,max_depth=depth,min_samples_leaf=40,
                                                  l2_regularization=10,learning_rate=.05,
                                                  early_stopping=False,random_state=42)
             fitted.fit(tmatrix[selected],tlabels[selected])
@@ -227,12 +271,18 @@ def _meta_scores(frame, positions, forecast, settings):
         if i<tune_end and i-last_refit>=refit:
             selected=np.flatnonzero(valid & np.isfinite(labels) & (origin+holding<=i) & (origin+holding<=tune_end))[-train_max:]
             if len(selected)>=min_train:
-                fitted=HistGradientBoostingRegressor(max_iter=100,max_depth=depth,min_samples_leaf=40,
+                model_class=HistGradientBoostingClassifier if meta_kind=='classification' else HistGradientBoostingRegressor
+                fitted=model_class(max_iter=100,max_depth=depth,min_samples_leaf=40,
                                                      l2_regularization=10,learning_rate=.05,
                                                      early_stopping=False,random_state=42)
                 fitted.fit(matrix[selected],labels[selected])
                 last_refit=i
-        scores[i]=float(fitted.predict(matrix[j:j+1])[0]) if fitted is not None and valid[j] else None
+        if fitted is None or not valid[j]:
+            scores[i]=None
+        elif meta_kind=='classification':
+            scores[i]=float(2*fitted.predict_proba(matrix[j:j+1])[0,1]-1)
+        else:
+            scores[i]=float(fitted.predict(matrix[j:j+1])[0])
     _META[cache_key]=scores
     return scores
 
@@ -271,17 +321,26 @@ def _evaluate(frame, positions, forecast, settings, start, end, *, control=None)
             row['outcome'] = 'FORECAST_FAILED'
             rows.append(row)
             continue
-        prediction = np.asarray(record['prediction'], dtype=float)
-        if len(prediction) != horizon or not np.isfinite(prediction).all() or (prediction <= 0).any():
-            raise ValueError('Real model returned an invalid forecast path.')
+        prediction=record.get('prediction')
+        if prediction is not None:
+            prediction=np.asarray(prediction,dtype=float)
+            if len(prediction)!=horizon or not np.isfinite(prediction).all() or (prediction<=0).any():
+                raise ValueError('Real model returned an invalid forecast path.')
+        predicted_close=_endpoint(record)
+        if not np.isfinite(predicted_close) or predicted_close<=0:
+            raise ValueError('Genuine model endpoint cache contains an invalid price.')
         atr = atrs[i-1]
         price = close[i-1]
-        move = float(prediction[-1]-price)
+        move = float(predicted_close-price)
         sign = 1 if move > 0 else -1 if move < 0 else 0
-        row.update(predicted_close=float(prediction[-1]), current_close=price,
+        row.update(predicted_close=predicted_close, current_close=price,
                    forecast_edge_atr=move/atr, trend_z=(price-mean[i-1])/atr)
         if not np.isfinite(atr) or atr <= 0 or not sign:
             rows.append(row); continue
+        atr_bps=atr/price*10000
+        if (atr_bps<float(settings.get('min_atr_bps',0)) or
+                atr_bps>float(settings.get('max_atr_bps',1000000))):
+            row['outcome']='VOLATILITY_FILTER'; rows.append(row); continue
         cost_price = price*(costs.spread_bps+costs.fee_bps+2*costs.slippage_bps)/10000
         if control != 'naive' and abs(move) < max(threshold*atr, cost_multiple*cost_price):
             rows.append(row); continue
@@ -351,10 +410,13 @@ def _evaluate(frame, positions, forecast, settings, start, end, *, control=None)
 
 
 def _controls(frame, rows, settings, runs=200):
-    """Matched long/short permutations with the same SL/TP/time-stop rules.
+    """Independent seeded random signals, exact count/side ratio, same exits.
 
-    A matched slot is capped by the following real trade entry, ensuring every
-    control is non-overlapping. Both original and control use this same cap.
+    The original strategy's future exit schedule is never inherited. Null
+    signals use eligible chronological origins, independently permuted sides,
+    their own SL/TP/time-stop resolution and their own non-overlap state.
+    Precomputed path resolutions merely cache identical calls to the common
+    execution engine; they do not choose a signal or level.
     """
     traded = rows[rows.trade_taken].copy()
     if traded.empty:
@@ -363,34 +425,68 @@ def _controls(frame, rows, settings, runs=200):
     costs = CostModel(settings.get('spread_bps',1),settings.get('fee_bps',1),settings.get('slippage_bps',1))
     holding = int(settings.get('time_stop',settings.get('horizon',12)))
     stop_atr = float(settings.get('stop_atr',1.25)); rr = float(settings.get('reward_risk',1.5))
-    slots = []
-    records = traded.to_dict('records')
-    for j,row in enumerate(records):
-        ix = frame.index.get_loc(pd.Timestamp(row['entry_utc']))
-        limit = ix+holding
-        if j+1 < len(records):
-            limit = min(limit, frame.index.get_loc(pd.Timestamp(records[j+1]['entry_utc'])))
-        path = frame.iloc[ix:limit]
-        values = {}
+    phase=settings.get('phase','TUNE'); n=len(frame)
+    start,end={'TUNE':(0,int(n*.6)),'VALIDATE':(int(n*.6),int(n*.8)),'VAULT':(int(n*.8),n)}[phase]
+    delay=int(settings.get('entry_delay',0)); cooldown=int(settings.get('cooldown',0))
+    positions=[int(i) for i in rows.origin_index if int(i)+delay+holding<=end]
+    atrs=_atr_series(frame)
+    table={}
+    span=pd.Timedelta(minutes=MINUTES_PER_CANDLE[settings.get('timeframe','15m')])
+    for i in positions:
+        ix=i+delay
+        if not np.isfinite(atrs[i-1]) or atrs[i-1]<=0:
+            continue
+        if not bool((frame.index[i-1:ix+holding].to_series().diff().dropna()==span).all()):
+            continue
+        table[i]={}
+        entry=float(frame.Open.iloc[ix]); path=frame.iloc[ix:ix+holding]
         for sign in (1,-1):
-            levels = _levels(row['entry'],row['atr'],sign,stop_atr,stop_atr*rr)
-            values[sign] = simulate_trade(levels,path,costs)['net_r']
-        slots.append(values)
+            levels=_levels(entry,atrs[i-1],sign,stop_atr,stop_atr*rr)
+            table[i][sign]=simulate_trade(levels,path,costs)
     totals = []
     for seed in range(42,42+int(runs)):
         permutation = np.random.default_rng(seed).permutation(directions)
-        returns = np.array([slot[int(sign)] for slot,sign in zip(slots,permutation)])
-        totals.append({'seed':seed,'trades':len(returns),'mean_r':float(returns.mean()),'net_profit_r':float(returns.sum())})
+        random_returns=[]; next_available=start; daily={}
+        for i in positions:
+            if len(random_returns)>=len(permutation):
+                break
+            ix=i+delay
+            if ix<next_available or i not in table:
+                continue
+            day=str(frame.index[ix].date())
+            if daily.get(day,0)>=int(settings.get('max_trades_per_day',10000)):
+                continue
+            hour=frame.index[i-1].hour
+            if 'session_start' in settings and not (int(settings['session_start'])<=hour<int(settings.get('session_end',24))):
+                continue
+            trade=table[i][int(permutation[len(random_returns)])]
+            random_returns.append(trade['net_r'])
+            next_available=ix+int(trade['bars_held'])+cooldown
+            daily[day]=daily.get(day,0)+1
+        returns=np.array(random_returns)
+        totals.append({'seed':seed,'trades':len(returns),'mean_r':float(returns.mean()) if len(returns) else np.nan,
+                       'net_profit_r':float(returns.sum()),'exact_count':len(returns)==len(directions)})
     random = pd.DataFrame(totals)
-    inverted = np.array([slot[-int(sign)] for slot,sign in zip(slots,directions)])
-    original = np.array([slot[int(sign)] for slot,sign in zip(slots,directions)])
+    inverted=[]; next_available=start
+    for row in traded.to_dict('records'):
+        i=int(row['origin_index']); ix=i+delay
+        if ix<next_available or i not in table:
+            continue
+        sign=-1 if row['direction']=='BUY' else 1
+        trade=table[i][sign]
+        inverted.append(trade['net_r']); next_available=ix+int(trade['bars_held'])+cooldown
+    inverted=np.array(inverted)
+    exact=random.loc[random.exact_count]
     return {'runs':runs,'trades':len(traded),'longs':int((directions>0).sum()),
             'shorts':int((directions<0).sum()),
-            'mean_r':float(random.mean_r.mean()),
-            'p95_mean_r':float(random.mean_r.quantile(.95)),
-            'p99_mean_r':float(random.mean_r.quantile(.99)),
-            'original_matched_mean_r':float(original.mean()),
-            'inverted_mean_r':float(inverted.mean()),
+            'matched_runs':len(exact),'incomplete_runs':int((~random.exact_count).sum()),
+            'control_status':'OK' if len(exact)==runs else 'INCONCLUSIVE_COUNT',
+            'mean_r':float(exact.mean_r.mean()) if len(exact) else None,
+            'p95_mean_r':float(exact.mean_r.quantile(.95)) if len(exact) else None,
+            'p99_mean_r':float(exact.mean_r.quantile(.99)) if len(exact) else None,
+            'original_matched_mean_r':float(traded.net_r.mean()),
+            'inverted_trades':len(inverted),
+            'inverted_mean_r':float(inverted.mean()) if len(inverted) else None,
             'inverted_profit_r':float(inverted.sum())}, random
 
 

research_predictions/btc_w128_h12.csv

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research_predictions/index.json

@@ -0,0 +1,47 @@
+[
+  {
+    "file": "btc_w256_h12.csv",
+    "file_sha256": "ecb9f1fde957c35aaae4574d6e88e803fbbbe5580bc396cb36ebeb3821291dd1",
+    "snapshot_sha256": "0350b7fd75637feb03fe484f24946b413663490d8a9193fef8d73f759a9030b1",
+    "window": 256,
+    "horizon": 12,
+    "model": "TimesFM",
+    "use_features": false,
+    "checkpoint": "google/timesfm-2.5-200m-pytorch",
+    "source_run": "t001_base_256_h12_s6",
+    "source_revision": "ceca5efba04cbfefec1d36eb24985b4e5a6369be",
+    "endpoint_only": true,
+    "rows": 2548,
+    "provenance": "Actual pretrained Space inference, closed-candle origins; only final predicted price is cached, no trade result fields."
+  },
+  {
+    "file": "btc_w128_h12.csv",
+    "file_sha256": "a8cedfe383eb6d3d974320a92e1a72086aee0ca933d601ae210577bd86caebab",
+    "snapshot_sha256": "0350b7fd75637feb03fe484f24946b413663490d8a9193fef8d73f759a9030b1",
+    "window": 128,
+    "horizon": 12,
+    "model": "TimesFM",
+    "use_features": false,
+    "checkpoint": "google/timesfm-2.5-200m-pytorch",
+    "source_run": "t051_meta_w128",
+    "source_revision": "8dd6f869b0f9503a338df5f913b29702950c85a4",
+    "endpoint_only": true,
+    "rows": 2539,
+    "provenance": "Actual pretrained Space inference, closed-candle origins; only final predicted price is cached, no trade result fields."
+  },
+  {
+    "file": "eth_w256_h12.csv",
+    "file_sha256": "f1dde2c21f389cfdbd21bc9b5569100445b997bfffa45734a49c042d6fbd9b54",
+    "snapshot_sha256": "af8683b0f21dd20129699fe97ea08fc78c56718671633ba4989d8cd726f9a739",
+    "window": 256,
+    "horizon": 12,
+    "model": "TimesFM",
+    "use_features": false,
+    "checkpoint": "google/timesfm-2.5-200m-pytorch",
+    "source_run": "t052_eth_meta192",
+    "source_revision": "8dd6f869b0f9503a338df5f913b29702950c85a4",
+    "endpoint_only": true,
+    "rows": 2518,
+    "provenance": "Actual pretrained Space inference, closed-candle origins; only final predicted price is cached, no trade result fields."
+  }
+]

utils/helpers.py

@@ -6,6 +6,16 @@ MINUTES_PER_CANDLE = {'1m': 1, '2m': 2, '5m': 5, '10m': 10, '15m': 15, '30m': 30
 INDICATOR_WARMUP_CANDLES = 30
 PREDICTION_HISTORY_CANDLES = 200
 
+
+def forex_market_open(timestamp):
+    """Standard FX week: Sunday 17:00 to Friday 17:00 New York (DST aware)."""
+    stamp=pd.Timestamp(timestamp)
+    if stamp.tzinfo is None:
+        stamp=stamp.tz_localize('UTC')
+    stamp=stamp.tz_convert('America/New_York')
+    weekday=stamp.weekday()
+    return not (weekday==5 or (weekday==4 and stamp.hour>=17) or (weekday==6 and stamp.hour<17))
+
 def infer_step(index: pd.DatetimeIndex):
     if not isinstance(index, pd.DatetimeIndex) or index.isna().any() or index.duplicated().any() or not index.is_monotonic_increasing:
         raise ValueError('infer_step needs ordered unique datetime timestamps.')