Claude-skill-registry alpaca-algo-plus-data

Use Alpaca Algo Trader Plus for 4+ years of historical data. Trigger when: (1) increasing lookback, (2) data source selection, (3) yfinance comparison.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/alpaca-algo-plus-data" ~/.claude/skills/majiayu000-claude-skill-registry-alpaca-algo-plus-data && rm -rf "$T"

manifest: skills/data/alpaca-algo-plus-data/SKILL.md

Alpaca Algo Trader Plus Data Access

Experiment Overview

Item	Details
Date	2024-12-29
Goal	Leverage Algo Trader Plus subscription for extended historical data
Environment	training notebook, DataFetcher, Alpaca API
Status	Success

Context

Default Alpaca free tier limits historical data to ~2 years. The Algo Trader Plus subscription ($99/month) provides:

5+ years of historical bars for equities
Extended crypto history
Higher API rate limits

This enables training on 4 years of data (1460 days) for more robust models.

Verified Workflow

1. Configure Lookback Period (notebook)

# Data configuration
LOOKBACK_DAYS = 1460              # 4 years of data (Algo Trader Plus subscription)
# LOOKBACK_DAYS = 730             # 2 years (free tier limit)

2. Data Fetcher Configuration

from alpaca_trading.data.fetcher import DataFetcher

# Initialize with API keys
fetcher = DataFetcher(keys_file='API_key.txt')

# Fetch extended history
df = fetcher.get_bars(
    symbol='AAPL',
    timeframe='1Hour',
    lookback_days=1460,  # 4 years
    use_cache=True
)

# Expected: ~10,000 1Hour bars (6.5 hours/day * ~252 days/year * 4 years)
print(f'Fetched {len(df):,} bars')

3. API Key Environment Variables

# Set in environment or notebook
import os
os.environ['APCA_API_KEY_ID'] = 'your_key_id'
os.environ['APCA_API_SECRET_KEY'] = 'your_secret_key'

# Or use keys file
os.environ['ALPACA_KEYS_FILE'] = 'API_key.txt'

4. Cache Configuration for Large Data

# Google Drive cache for Colab
DRIVE_DATA_DIR = '/content/drive/MyDrive/Colab_Projects/training_data'
SELECTION_CACHE_EXPIRY_DAYS = 3   # Shorter for fresh rankings
TRAINING_CACHE_EXPIRY_DAYS = 7    # Longer for stability

# Save 4 years of data to cache
save_to_cache(symbol, df, timeframe, lookback_days=1460)

5. Validate Data Availability

# Check date range
print(f'Date range: {df.index[0]} to {df.index[-1]}')

# Expected for 4 years:
# Start: ~2021-01-XX
# End: 2024-12-XX

# Verify bar count
expected_bars = 6.5 * 252 * 4  # ~6,552 for 1Hour
print(f'Expected ~{expected_bars:,.0f} bars, got {len(df):,}')

Data Source Comparison

Feature	yfinance (Free)	Alpaca Free	Algo Trader Plus
Equity History	5+ years	~2 years	5+ years
Crypto History	Limited	~2 years	Extended
Intraday Bars	Limited	1Hour+	1Min+
API Rate Limit	Low	200/min	Higher
Data Quality	Inconsistent	Clean	Clean
Zero-Volume Bars	Common	Rare	Rare
Cost	Free	Free	$99/month

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
yfinance for crypto training	Zero-volume bars, gaps	Alpaca required for crypto
5 years lookback	Some symbols have <5 years	4 years is safe for most
No cache for 4-year data	API timeouts, rate limits	Always cache large fetches
Assuming all symbols available	Some delisted/missing	Check bar count after fetch

Final Parameters

# Recommended settings for Algo Trader Plus
lookback_days: 1460             # 4 years
min_bars_required: 500          # For selection
min_bars_training: 2000         # For training
cache_expiry_days: 7            # Weekly refresh
timeframe: '1Hour'              # Base timeframe

# Expected bar counts (1Hour, equities)
1_year: ~1,638 bars
2_years: ~3,276 bars
4_years: ~6,552 bars
5_years: ~8,190 bars

Key Insights

4 years is the sweet spot: Enough data for robust training, available for most symbols
yfinance is unreliable for crypto: Zero-volume bars break training
Cache aggressively: 4-year fetches are slow without caching
Not all symbols have full history: Check bar count after fetch
Cost-benefit: $99/month for quality data saves debugging time

Subscription Tiers

Tier	Historical Data	Best For
Free	~2 years	Testing, paper trading
Algo Trader Plus	5+ years	Production training
Market Data Pro	Real-time	Live trading optimization

References

```
notebooks/training.ipynb
```
: LOOKBACK_DAYS configuration
```
alpaca_trading/data/fetcher.py
```
: DataFetcher class
Alpaca Subscription Plans