Multi-Timeframe Training Pattern

Experiment Overview

Item	Details
Date	2024-12-29
Goal	Train separate models for each timeframe using resampled data from base timeframe
Environment	training notebook, multi_timeframe.py, GPUVectorizedTradingEnv
Status	Success

Context

Single-timeframe training limits strategy adaptability. Different timeframes capture different market dynamics:

•1Hour: Short-term momentum, intraday patterns
•4Hour: Medium-term trends, reduced noise
•1Day: Long-term trends, swing trading

Solution: Train separate models for each timeframe, resampling from cached 1Hour data.

Verified Workflow

1. Configure Training Timeframes (notebook)

python

# Data configuration
TIMEFRAME = '1Hour'               # Base timeframe for data fetching
TRAINING_TIMEFRAMES = ['1Hour']   # Single timeframe (default)
# TRAINING_TIMEFRAMES = ['1Hour', '4Hour']  # Multi-TF training

2. Build Training Combinations

python

# Build training combinations (symbol x timeframe)
training_combinations = [(s, tf) for tf in TRAINING_TIMEFRAMES for s in SYMBOLS_TO_TRAIN_FINAL]
total_combinations = len(training_combinations)

print(f'TRAINING {len(SYMBOLS_TO_TRAIN_FINAL)} SYMBOLS × {len(TRAINING_TIMEFRAMES)} TIMEFRAMES = {total_combinations} models')

3. Resample Data with Proper Alignment

python

from alpaca_trading.signals.multi_timeframe import resample_to_timeframe

# Load base data (1Hour)
df_base = PREFETCHED_DATA[symbol]

# Resample if training on different timeframe
if training_tf != TIMEFRAME:
    df = resample_to_timeframe(df_base, training_tf)
    print(f'  Resampled {len(df_base)} bars -> {len(df)} bars for {training_tf}')
else:
    df = df_base

4. Resampling Function (multi_timeframe.py)

python

def resample_to_timeframe(df: pd.DataFrame, timeframe: str) -> pd.DataFrame:
    """Resample OHLCV data to a different timeframe."""
    if df.empty:
        return df

    # Ensure datetime index
    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    # Map timeframe to pandas frequency (pandas 2.2+ uses lowercase 'h')
    freq_map = {
        "1Min": "1min",
        "5Min": "5min",
        "15Min": "15min",
        "30Min": "30min",
        "1Hour": "1h",
        "2Hour": "2h",
        "4Hour": "4h",
        "1Day": "1D",
        "1Week": "1W",
    }

    freq = freq_map.get(timeframe, "1min")

    # Resample OHLCV with origin='start' to preserve alignment
    # CRITICAL: origin='start' ensures bars align with market open (9:30)
    # not clock-hour boundaries (10:00, 11:00)
    resampled = pd.DataFrame()
    resampled["open"] = df["open"].resample(freq, origin='start').first()
    resampled["high"] = df["high"].resample(freq, origin='start').max()
    resampled["low"] = df["low"].resample(freq, origin='start').min()
    resampled["close"] = df["close"].resample(freq, origin='start').last()

    if "volume" in df.columns:
        resampled["volume"] = df["volume"].resample(freq, origin='start').sum()

    return resampled.dropna()

5. Model Naming Convention

python

# Models named with timeframe suffix
model_filename = f'{symbol}_{training_tf}.pt'
# Examples: GOOGL_1Hour.pt, GOOGL_4Hour.pt

# Save paths
local_model_path = f'models/rl_symbols/{model_filename}'
drive_model_path = f'{DRIVE_MODEL_DIR}/{model_filename}'

6. Live Trader Model Loading

python

# Load models organized by symbol and timeframe
for model_file in model_dir.glob('*.pt'):
    filename = model_file.stem
    parts = filename.split('_')
    symbol = parts[0]
    timeframe = parts[1] if len(parts) > 1 else '1Hour'

    model = load_native_model(str(model_file))

    if symbol not in models_by_symbol:
        models_by_symbol[symbol] = {}
    models_by_symbol[symbol][timeframe] = model

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
Resampling without origin='start'	Bars aligned to clock hours, not market open	Use origin='start' for equity market alignment
Using 'H' instead of 'h'	Deprecated in pandas 2.2+	Use lowercase 'h' for hours
Training all timeframes in parallel	OOM errors	Train sequentially, clean up between
Mixing 1Hour and 4Hour data sources	Data inconsistency	Always resample from same base data
Not cleaning GPU memory between TFs	Memory accumulation, crash	Call cleanup_gpu_memory() between each

Final Parameters

yaml

# Timeframe configuration
base_timeframe: '1Hour'       # Data fetched at this resolution
training_timeframes:          # Train models at these resolutions
  - '1Hour'                   # Default (single TF)
  # - '4Hour'                 # Add for multi-TF
  # - '1Day'                  # Add for swing trading

# Resampling rules
origin: 'start'               # Align to first bar (market open)
open: 'first'                 # First open in period
high: 'max'                   # Max high in period
low: 'min'                    # Min low in period
close: 'last'                 # Last close in period
volume: 'sum'                 # Total volume in period

# pandas frequency map
1Hour: '1h'
2Hour: '2h'
4Hour: '4h'
1Day: '1D'
1Week: '1W'

Key Insights

•origin='start' is CRITICAL: Without it, 4Hour bars start at 00:00, not market open
•Lowercase frequency strings: pandas 2.2+ deprecated uppercase 'H'
•Sequential training: GPU memory doesn't allow parallel multi-TF training
•Same base data: All timeframes should resample from same cached data
•Consistent naming: SYMBOL_TIMEFRAME.pt enables auto-discovery

Data Requirements

Timeframe	Min Bars (training)	From 1Hour (4 years)
1Hour	2000	~10,000 bars
4Hour	500	~2,500 bars
1Day	125	~1,000 bars

Multi-TF Strategy (future)

Once models exist for multiple timeframes:

•Use MultiTimeframePricePredictor for signal aggregation
•Higher timeframes get more weight (filter noise)
•Require alignment (e.g., 1Hour + 4Hour both bullish) for entries

References

•notebooks/training.ipynb: TRAINING_TIMEFRAMES configuration
•alpaca_trading/signals/multi_timeframe.py: resample_to_timeframe()
•scripts/live_trader.py: Multi-TF model loading pattern