TradingAgents/docs/MEMORY_CONFIGURATION_GUIDE.md

Memory Configuration Guide

Parameter Selection for Different Trading Strategies

Quick Reference Table

Strategy	lookforward_days	interval_days	Memories/Year	Best For
Day Trading	1	1	~250	Intraday momentum, catalysts
Swing Trading (Short)	3-5	7	~52	Week-long trends
Swing Trading	7	7	~52	Weekly momentum
Position Trading	30	30	~12	Monthly fundamentals
Long-term Investing	90	90	~4	Quarterly value
Annual Investing	365	90	~4	Yearly performance

---

Understanding the Parameters

1. lookforward_days - Return Measurement Horizon

What it does: Determines how far into the future we look to measure if a decision was successful.

Example:

Date: 2024-01-15
Stock: AAPL at $180
Situation: Strong earnings, bullish technicals

lookforward_days = 7
→ Check price on 2024-01-22: $195
→ Return: +8.3%
→ Memory: "This pattern led to +8.3% in 1 week"

lookforward_days = 30
→ Check price on 2024-02-15: $205
→ Return: +13.9%
→ Memory: "This pattern led to +13.9% in 1 month"

How to choose:

• Match your holding period: If you typically hold stocks for 2 weeks, use lookforward_days=14
• Match your profit targets: If you target 5-10% gains in a week, use lookforward_days=7
• Match your risk tolerance: Shorter horizons = more volatile, longer = smoother

2. interval_days - Sampling Frequency

What it does: Determines how often we create a memory sample.

Example:

Period: 2024-01-01 to 2024-12-31 (365 days)

interval_days = 7 (weekly)
→ Samples: Jan 1, Jan 8, Jan 15, Jan 22, ...
→ Total: ~52 samples per stock

interval_days = 30 (monthly)
→ Samples: Jan 1, Feb 1, Mar 1, Apr 1, ...
→ Total: ~12 samples per stock

How to choose:

• More samples = better learning, but slower to build and more API costs
• Market volatility: Volatile markets → sample more frequently (7-14 days)
• Data availability: Some data sources may be rate-limited → larger intervals
• Computational budget: More samples = longer build time

---

Strategy-Specific Recommendations

📈 Day Trading

Goal: Capture next-day momentum and intraday catalysts

lookforward_days = 1      # Next day returns
interval_days = 1         # Daily samples (or 7 for weekly)

What agents learn:

• "After earnings beat + gap up, next day typically +2-3%"
• "High volume breakout → next day continuation 70% of time"
• "Morning dip + positive news → recovery same day"

Best tickers: High volume, volatile stocks (SPY, QQQ, TSLA, NVDA)

Trade-offs:

• ✅ Captures short-term patterns
• ❌ Very expensive (1 year = 250 samples × 10 stocks = 2,500 API calls)
• ❌ More noise, short-term randomness

Recommendation: Use interval_days=7 instead of 1 to reduce costs while still capturing patterns

---

📊 Swing Trading

Goal: Capture weekly trends and momentum

lookforward_days = 7      # 1-week returns
interval_days = 7         # Weekly samples

What agents learn:

• "Earnings beat + bullish MACD → +8% average in 1 week"
• "Bearish divergence + overbought RSI → -5% drop within 7 days"
• "Strong sector rotation + momentum → sustained weekly gains"

Best tickers: Liquid, trending stocks (AAPL, GOOGL, MSFT, NVDA, TSLA)

Trade-offs:

• ✅ Good balance of data quantity and quality
• ✅ Captures momentum and short-term fundamentals
• ✅ Reasonable API costs (52 samples/year)

Recommendation: Best default choice for most users

---

📅 Position Trading

Goal: Capture monthly fundamentals and trends

lookforward_days = 30     # Monthly returns
interval_days = 30        # Monthly samples

What agents learn:

• "Revenue growth >20% + P/E <25 → +15% avg monthly return"
• "Sector headwinds + declining margins → avoid, -10% monthly"
• "Strong balance sheet + positive guidance → sustained monthly gains"

Best tickers: Fundamentally strong, large-cap stocks

Trade-offs:

• ✅ Low API costs (12 samples/year)
• ✅ Filters out short-term noise
• ✅ Focuses on fundamentals
• ❌ Fewer memories = less learning
• ❌ Misses short-term opportunities

Recommendation: Good for fundamental-focused strategies

---

📆 Long-term Investing

Goal: Capture quarterly/annual value trends

lookforward_days = 90     # Quarterly returns (or 365 for annual)
interval_days = 90        # Quarterly samples

What agents learn:

• "Consistent earnings growth + moat → +25% quarterly average"
• "High debt + declining revenue → avoid, underperforms market"
• "Market leadership + innovation → sustained long-term outperformance"

Best tickers: Blue chips, value stocks (BRK.B, JPM, JNJ, PG, V)

Trade-offs:

• ✅ Very low API costs (4 samples/year)
• ✅ Focuses on long-term fundamentals
• ✅ Smooths out volatility
• ❌ Very few memories (4/year × 10 stocks = 40 total)
• ❌ Not useful for active trading

Recommendation: Only for true long-term buy-and-hold strategies

---

Multi-Strategy Approach

Best practice: Build memories for multiple strategies and switch based on market conditions.

Example: Comprehensive Setup

# 1. Build swing trading memories (primary)
swing_memories = builder.populate_agent_memories(
    tickers=["AAPL", "GOOGL", "MSFT", "NVDA", "TSLA"],
    lookforward_days=7,
    interval_days=7,
    start_date="2023-01-01",
    end_date="2025-01-01"
)
# Save to: data/memories/swing_trading/

# 2. Build position trading memories (secondary)
position_memories = builder.populate_agent_memories(
    tickers=["AAPL", "GOOGL", "MSFT", "JPM", "JNJ"],
    lookforward_days=30,
    interval_days=30,
    start_date="2023-01-01",
    end_date="2025-01-01"
)
# Save to: data/memories/position_trading/

# 3. Use swing for active trades, position for core holdings

When to Use Each:

Market Condition	Strategy	Memory Set
High volatility	Day/Swing	lookforward_days=1-7
Trending market	Swing	lookforward_days=7
Range-bound	Position	lookforward_days=30
Bull market	Swing/Position	lookforward_days=7-30
Bear market	Position/Long-term	lookforward_days=30-90

---

Advanced Configurations

Earnings-Focused Memories

Capture post-earnings performance:

# Sample around earnings dates
lookforward_days = 7      # 1 week post-earnings
interval_days = 90        # Quarterly (around earnings)

What it captures: Earnings reaction patterns

---

Catalyst-Driven Memories

Capture event-driven moves:

lookforward_days = 3      # Short-term catalyst impact
interval_days = 14        # Bi-weekly to catch various catalysts

What it captures: FDA approvals, product launches, analyst upgrades

---

Hybrid Approach

Create memories for multiple horizons:

# Short-term patterns
builder.populate_agent_memories(
    tickers=tickers,
    lookforward_days=7,
    interval_days=7,
    # Save to: memories/short_term/
)

# Long-term patterns
builder.populate_agent_memories(
    tickers=tickers,
    lookforward_days=30,
    interval_days=30,
    # Save to: memories/long_term/
)

# Load both: agents see patterns across time horizons

---

Cost vs. Benefit Analysis

API Call Estimates

For 10 tickers over 2 years:

Config	Samples/Stock	Total Samples	API Calls*	Build Time**
Daily (1, 1)	~500	5,000	~20,000	2-4 hours
Weekly (7, 7)	~104	1,040	~4,160	30-60 min
Monthly (30, 30)	~24	240	~960	10-20 min
Quarterly (90, 90)	~8	80	~320	5-10 min

*API calls = samples × 4 (market, news, sentiment, fundamentals) + returns **Estimates vary based on API rate limits

Recommended Starting Point

For most users:

lookforward_days = 7      # Weekly horizon
interval_days = 14        # Bi-weekly samples
# Good balance: ~52 samples/year, manageable costs

Why:

• ✅ Enough memories for learning (~520 total for 10 stocks)
• ✅ Reasonable API costs
• ✅ Captures both short-term patterns and fundamentals
• ✅ Fast to build (20-30 minutes)

---

Validation & Testing

How to Know if Your Settings Are Good

After building memories, test them:

# Build memories
memories = builder.populate_agent_memories(
    tickers=["AAPL"],
    lookforward_days=7,
    interval_days=14,
    start_date="2024-01-01",
    end_date="2024-12-01"
)

# Test retrieval
test_situations = [
    "Strong earnings beat with bullish technicals",
    "High valuation with negative sentiment",
    "Sector weakness with bearish momentum"
]

for situation in test_situations:
    results = memories["trader"].get_memories(situation, n_matches=3)
    print(f"\nQuery: {situation}")
    for i, r in enumerate(results, 1):
        print(f"  Match {i} (similarity: {r['similarity_score']:.2f})")
        print(f"  {r['recommendation'][:100]}...")

Good signs:

• ✅ Similarity scores >0.7 for relevant queries
• ✅ Recommendations make sense for the query
• ✅ Diverse outcomes (not all BUY or all SELL)

Bad signs:

• ❌ All similarity scores <0.5
• ❌ Recommendations don't match the query
• ❌ All memories say the same thing

→ If bad, try adjusting interval_days or adding more tickers

---

Summary: Decision Tree

What's your trading style?
│
├─ Hold <1 week (Day/Swing)
│  ├─ lookforward_days: 1-7
│  └─ interval_days: 7-14
│
├─ Hold 1-4 weeks (Swing/Position)
│  ├─ lookforward_days: 7-30
│  └─ interval_days: 14-30
│
├─ Hold 1-3 months (Position)
│  ├─ lookforward_days: 30-90
│  └─ interval_days: 30
│
└─ Hold >3 months (Long-term)
   ├─ lookforward_days: 90-365
   └─ interval_days: 90

---

Quick Start Commands

Swing Trading (Recommended Default)

python scripts/build_strategy_specific_memories.py
# Choose option 2: Swing Trading

Custom Configuration

from tradingagents.agents.utils.historical_memory_builder import HistoricalMemoryBuilder

builder = HistoricalMemoryBuilder(config)

memories = builder.populate_agent_memories(
    tickers=["YOUR", "TICKERS"],
    start_date="2023-01-01",
    end_date="2025-01-01",
    lookforward_days=7,    # <-- Your choice
    interval_days=14       # <-- Your choice
)

---

Conclusion

TLDR:

• lookforward_days: Match your typical holding period
• interval_days: Balance between data quantity and API costs
• Default recommendation: lookforward_days=7, interval_days=14
• Use strategy-specific builder for pre-optimized configurations

Your memories will be as good as your parameter choices! 🎯