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name: live-trader-prediction-fixes description: "Fix live trader prediction issues: zeros returned, excessive data fetching, insufficient bars. Trigger when predictions show all zeros, excessive API calls, or Markov features show uniform priors." author: Claude Code date: 2026-01-26

Live Trader Prediction Fixes (v3.6.1)

Experiment Overview

Item	Details
Date	2026-01-26
Goal	Fix live trader returning all-zero predictions and continuously fetching thousands of bars
Environment	Python 3.10, alpaca_trading v3.6.0
Status	Success

Context

The live trader exhibited three interconnected issues:

1. All predictions returned signal=0, confidence=0.0 for most symbols
2. The trader continuously fetched 8000+ bars on every update cycle
3. Some symbols showed confidence values but still returned signal=0 (HOLD)

Symptoms in gate_status.json

{
  "CMF": { "signal": 0, "confidence": 0.0, "final_status": "HOLD" },
  "LXP": { "signal": 0, "confidence": 0.0, "final_status": "HOLD" },
  "PFE": { "signal": 0, "confidence": 0.549, "final_status": "HOLD" }
}

Most symbols had confidence=0.0, indicating predictions were failing early and returning neutral results.

Root Causes

Issue 1: Markov State Not Persisted

In MultiTimeframePricePredictor._get_rl_prediction(), a new InferenceObservationBuilder was created on every prediction call:

# WRONG - creates new builder each call, resets Markov state
obs_builder = InferenceObservationBuilder(
    window=window,
    use_gpu_markov=True,
    target_features=target_features
)

This reset the Markov chains to uniform priors [0.33, 0.34, 0.33] on every call, making the Markov regime features useless for prediction.

Issue 2: Excessive Data Fetching

In DataFetcher.get_latest_bars(), the lookback was calculated as:

# WRONG - doesn't account for weekends/holidays
delta = _timeframe_to_timedelta(timeframe) * (count * 2)  # count * 2 is naive

For count=8000 1-minute bars, this requested 16,000 minutes (~11 days) of data, but:

• Stocks only trade 6.5 hours/day (390 minutes)
• 8000 1-minute trading bars actually span ~30+ calendar days
• The 2x multiplier was both arbitrary and insufficient

Issue 3: Insufficient Hourly Bars

The prediction pipeline required 120 hourly bars (100 window + 20 buffer), but:

• max_bars=8000 for 1-minute data only produced ~133 hourly bars when resampled
• This was borderline and could fail if any data was missing
• The fallback hourly fetch used lookback_days=15, which only yields ~97 hourly bars for stocks

Verified Solutions

Fix 1: Persist Markov State Across Calls

Store InferenceObservationBuilder instances and reuse them:

# In MultiTimeframePricePredictor.__init__
self._obs_builders: Dict[str, any] = {}  # Stateful builders per timeframe

# In _get_rl_prediction - reuse existing builder
builder_key = f"{timeframe}_{target_features}"
if builder_key not in self._obs_builders:
    logger.info(f"Creating InferenceObservationBuilder for {timeframe}")
    self._obs_builders[builder_key] = InferenceObservationBuilder(
        window=window,
        use_gpu_markov=True,
        target_features=target_features
    )

obs_builder = self._obs_builders[builder_key]  # Reuse!
obs = obs_builder.build(prices=prices, high=high, low=low)

Key insight: The Markov chains need to see historical price evolution to produce meaningful regime probabilities. Resetting them each call defeats their purpose.

Fix 2: Intelligent Lookback Calculation

Replace naive count * 2 with market-aware calculation:

def get_latest_bars(self, symbol, count=750, timeframe="1Min"):
    tf_delta = _timeframe_to_timedelta(timeframe)
    tf_minutes = tf_delta.total_seconds() / 60

    if tf_minutes < 60:  # Intraday
        # Stocks trade ~390 minutes/day (6.5 hours)
        trading_days_needed = max(1, count / 390)
        calendar_days = trading_days_needed * 1.5  # 7/5 = 1.4, plus buffer
        calendar_days = max(calendar_days, 5)  # Minimum 5 days
        delta = timedelta(days=calendar_days)
    elif tf_minutes < 1440:  # Hourly
        trading_days_needed = max(1, count / 6.5)
        calendar_days = trading_days_needed * 1.5 + 2
        delta = timedelta(days=calendar_days)
    else:  # Daily or weekly
        calendar_days = count * 1.5 + 5
        delta = timedelta(days=calendar_days)

Fix 3: Increase Data Buffers

In scripts/live_trader.py:

# Before
max_bars = 8000  # ~133 hourly bars when resampled

# After
max_bars = 12000  # ~200 hourly bars when resampled

In multi_tf_predictor.py fallback fetch:

# Before
lookback_days = 15  # Only ~97 hourly bars for stocks

# After
lookback_days = 40  # ~260 hourly bars for stocks

Failed Attempts

Attempt	Why it Failed	Lesson Learned
Just increase max_bars	Didn't fix zero confidence - Markov state was still resetting	Data quantity isn't the issue if state isn't persisted
Add logging without fixing	Confirmed zeros but didn't solve it	Good for diagnosis, but need to fix root cause
Using build_inference_observation() function	This is stateless - defaults to uniform priors	Must use InferenceObservationBuilder class for stateful inference

Verification

After applying fixes, predictions show real values:

2026-01-26 10:28:21,957 [INFO] CMF: Prediction: dir=0, mag=0.00%, conf=0.547, size=50%, regime=elevated
2026-01-26 10:28:22,479 [INFO] VERX: Prediction: dir=1, mag=0.60%, conf=0.427, size=50%, regime=high

Key indicators of success:

• Non-zero confidence values (0.427, 0.547)
• Direction predictions (VERX shows dir=1 for LONG)
• Markov-derived regime labels (elevated, high)

Files Modified

alpaca_trading/data/fetcher.py:
  - get_latest_bars(): Intelligent lookback calculation

alpaca_trading/prediction/multi_tf_predictor.py:
  - __init__(): Added self._obs_builders dict
  - _get_rl_prediction(): Reuse builders per timeframe
  - predict(): Increased fallback lookback_days from 15 to 40

scripts/live_trader.py:
  - update_bars(): Increased max_bars from 8000 to 12000
  - Warmup section: Added detailed logging

Key Insights

1. Stateful vs Stateless Inference: The standalone build_inference_observation() function is for single-shot inference where Markov state is passed externally. The InferenceObservationBuilder class is for stateful inference where Markov state evolves.

2. Market Hours Matter: Any lookback calculation for trading data must account for:

   • Trading hours (6.5 hours/day for US stocks)
   • Weekends (5 trading days / 7 calendar days)
   • Holidays (add buffer)

3. Borderline Isn't Good Enough: If you need 120 bars minimum, don't fetch 133. Fetch 200+ to handle edge cases.

4. Confidence vs Direction: A symbol can have non-zero confidence but still predict HOLD (dir=0). This is valid - it means the model is confident about staying neutral.

Related Skills

• markov-regime-features: Original diagnosis of Markov uniform prior issue
• persistent-cache-gap-filling: Data caching strategy leveraged by this fix
• data-source-priority: Data fetching hierarchy

References

• alpaca_trading/gpu/inference_obs_builder.py: InferenceObservationBuilder class
• alpaca_trading/prediction/multi_tf_predictor.py: MultiTimeframePricePredictor
• scripts/live_trader.py: Live trading loop