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Directional Bias Feature - Complete Summary

Problem Solved

Before: - Expected move from calculator.py shows magnitude only (e.g., 8.5%) - No way to know if move will be +8.5% or -8.5% - Strike selection was guesswork or always ATM - Monte Carlo simulations didn't account for directional market expectations

After: - Multi-signal analysis predicts direction (bullish/bearish/neutral) - Confidence scoring (0-100%) indicates signal strength - Automated strike recommendations - Monte Carlo integration for directional positioning

What Was Built

1. Core Directional Bias Module

File: trade_calc/directional_bias.py

Features: - calculate_iv_skew() - Put/call implied volatility comparison - calculate_put_call_ratio() - Volume and open interest analysis - calculate_recent_trend() - Price momentum detection - calculate_price_momentum() - Multi-timeframe momentum indicators - analyze_directional_bias() - Composite signal analysis with weighting - suggest_strike_adjustment() - Intelligent strike recommendations

Signals Analyzed (with weights): 1. Put/Call IV Skew (30%) - Option pricing signals 2. Put/Call Volume Ratio (25%) - Trading activity 3. Put/Call Open Interest (20%) - Positioning 4. Recent Price Trend (25%) - Momentum

2. Directional Analysis Script

File: scripts/directional_analysis.py

Usage: 

uv run python scripts/directional_analysis.py NVDA

Output: - Detailed signal breakdown with interpretations - Price momentum indicators - Overall bias assessment - Confidence level - Strike recommendations - Trading implications

3. Monte Carlo Integration

File: scripts/monte_carlo.py

New Parameters: 

SimulationParams(
    # ... existing params ...
    directional_bias="bullish",      # Direction prediction
    directional_bias_pct=3.5,        # Magnitude of directional expectation
    use_dynamic_strikes=True,        # Enable strike adjustment
)

How It Works: - Accepts directional bias from analysis - Automatically adjusts strike selection - Uses 50% of bias (conservative) capped at 50% of expected move - Rounds to nearest standard strike increment

4. Comprehensive Documentation

Files: - DIRECTIONAL_BIAS_GUIDE.md - Complete methodology and usage guide - README.md - Updated with directional analysis integration - DIRECTIONAL_BIAS_SUMMARY.md - This file

How to Use

Quick Start

# Step 1: Get expected move
uv run python scripts/calculator.py
# Enter: NVDA
# Output: Expected Move: 8.5%

# Step 2: Analyze direction
uv run python scripts/directional_analysis.py NVDA
# Output: Bias: Bullish, Confidence: 65%, Suggested Strike: $190

# Step 3: Run simulation (optional)
# Edit run_yearly_analysis.py with:
#   expected_move_pct=8.5
#   directional_bias="bullish"
#   directional_bias_pct=3.5

uv run python scripts/run_yearly_analysis.py

Programmatic Usage

from trade_calc.data import fetch_options_data_yfinance
from trade_calc.directional_bias import analyze_directional_bias, suggest_strike_adjustment
import pandas as pd
import yfinance as yf

# Fetch data
options_data = fetch_options_data_yfinance("NVDA")
chain = options_data["options_chains"][options_data["expirations"][0]]
calls_df = pd.DataFrame(chain["calls"])
puts_df = pd.DataFrame(chain["puts"])

# Get price history
stock = yf.Ticker("NVDA")
price_history = stock.history(period="1mo")

# Analyze
signals = analyze_directional_bias(
    calls_df, 
    puts_df, 
    options_data["current_price"], 
    price_history
)

print(f"Bias: {signals.overall_bias}")
print(f"Confidence: {signals.bias_confidence * 100:.0f}%")
print(f"Directional Move: {signals.bias_magnitude:+.2f}%")

# Get strike suggestion
strike_suggestion = suggest_strike_adjustment(
    options_data["current_price"],
    8.5,  # expected move from calculator
    signals
)

print(f"Suggested Strike: ${strike_suggestion['suggested_strike']:.2f}")

Signal Interpretation

Example Output for NVDA (from actual run)

Directional Bias Signals:
┌───────────────────────────┬─────────────────┬────────────────────────────────┐
│ Signal                    │           Value │ Interpretation                 │
├───────────────────────────┼─────────────────┼────────────────────────────────┤
│ Put/Call IV Skew          │         -0.0031 │ Neutral                        │
│ Put/Call Volume Ratio     │            0.48 │ Bullish (more call volume)     │
│ Put/Call OI Ratio         │            1.14 │ Neutral                        │
│ 5-Day Price Trend         │          +0.70% │ Neutral                        │
└───────────────────────────┴─────────────────┴────────────────────────────────┘

Overall Directional Bias: NEUTRAL
Confidence: 50%
Expected Directional Move: +0.95%

Strike Recommendation:
  Current Price:         $186.23
  Expected Move:         ±3.44%
  Suggested Strike:      $185.00
  Strike Adjustment:     -0.66%

Interpretation:

  • Mixed signals → Low confidence
  • Heavy call buying → Slight bullish tilt
  • Suggestion → Stay near ATM ($185-186)
  • Reason → Calendar spreads don't require strong directional bias

Integration with Existing Workflow

Before Directional Bias:

calculator.py → compare.py → rank_tickers.py → monte_carlo.py
     ↓
Expected move: 8.5%
Strike: Always ATM or guess

After Directional Bias:

calculator.py → directional_analysis.py → monte_carlo.py
     ↓                    ↓                       ↓
Expected move: 8.5%   Direction: Bullish    Strike: $190 (optimized)
                      Confidence: 65%
                      Bias: +3.5%

Real-World Example

Scenario: TSLA Earnings

Step 1: Calculator Analysis 

uv run python scripts/calculator.py
# Input: TSLA
Output: - Current Price: $245 - Expected Move: 12.5% (high volatility) - Pass/Fail: PASS

Step 2: Directional Analysis 

uv run python scripts/directional_analysis.py TSLA
Output: - Put/Call IV Skew: -0.08 (Calls expensive - BULLISH) - Put/Call Volume: 0.65 (Heavy call buying - BULLISH) - Put/Call OI: 0.72 (More calls - BULLISH) - 5-Day Trend: +4.2% (Uptrend - BULLISH) - Overall: BULLISH, Confidence: 75% - Directional Move: +5.5%

Step 3: Strike Selection - Without bias: $245 (ATM) - With bias: $250-255 (2-4% OTM to the upside) - Rationale: Strong bullish signals, high confidence

Step 4: Monte Carlo with Directional Bias 

params = SimulationParams(
    spot_price=245,
    strike_price=250,  # From directional analysis
    expected_move_pct=12.5,
    directional_bias="bullish",
    directional_bias_pct=5.5,
    # ... other params
)

Result: - Simulation shows improved profitability at $250 strike vs $245 - Exit reasons: More profit targets, fewer large moves - Risk metrics: Better risk/reward with directional positioning

Key Benefits

1. Data-Driven Strike Selection

  • No more guessing or always defaulting to ATM
  • Uses actual market signals (IV, volume, momentum)
  • Confidence scoring helps decide when to act on bias

2. Risk Management

  • Know when signals are weak → stay ATM (safer)
  • Know when signals are strong → position directionally (higher reward)
  • Conservative adjustment caps (50% of bias, 50% of expected move)

3. Complete Picture

  • Magnitude: Expected move from straddle (e.g., 8.5%)
  • Direction: Bias analysis (e.g., +3.5% bullish)
  • Confidence: Signal strength (e.g., 65%)
  • Action: Strike recommendation (e.g., $190 vs $186)

4. Flexible Usage

  • Works standalone (just run directional_analysis.py)
  • Integrates with Monte Carlo
  • Programmatic access for custom strategies
  • Not required - can still use ATM strikes

Technical Details

Signal Weighting Rationale

Why 30% for IV Skew? - Reflects actual option pricing by market makers - Most direct signal of directional expectation - Less noisy than volume (which can be hedging)

Why 25% for Volume? - Shows current positioning - But can be misleading (market makers hedging) - Balanced with other signals

Why 20% for Open Interest? - Shows longer-term positioning - But can be stale (positions held for days/weeks) - Lower weight due to potential staleness

Why 25% for Price Trend? - Momentum is powerful predictor - But can reverse quickly at earnings - Equal to volume, less than IV skew

Strike Adjustment Algorithm

# Base adjustment: 50% of directional bias
adjustment = directional_bias_pct * 0.5 / 100

# Cap at 50% of expected move (conservative)
max_adjustment = expected_move_pct / 2 / 100
adjustment = min(adjustment, max_adjustment)

# Apply to price
strike = spot_price * (1 + adjustment)

# Round to standard increments
if strike < 50:     increment = 0.5
elif strike < 100:  increment = 1.0
elif strike < 200:  increment = 2.5
else:               increment = 5.0

strike = round(strike / increment) * increment

Limitations

1. Probabilistic, Not Predictive

  • 70% confidence means 30% chance of being wrong
  • Earnings can always surprise
  • Use as input, not gospel

2. Calendar Spreads Are IV Plays

  • Primary profit driver is still IV crush
  • Direction is secondary optimization
  • Don't go too far OTM chasing direction

3. Market Conditions Matter

  • Signals less reliable in high volatility
  • News/events can override technical signals
  • Always check fundamentals

4. Not Financial Advice

  • Tool for analysis, not recommendation
  • Users must make their own trading decisions
  • Past signals don't guarantee future accuracy

Testing Results

Test Case: NVDA (1/18/2026)

Input: - Current Price: $186.23 - Expected Move: 3.44%

Signals: - IV Skew: -0.0031 (neutral) - Volume Ratio: 0.48 (bullish) - OI Ratio: 1.14 (neutral) - Trend: +0.70% (neutral)

Output: - Overall: NEUTRAL - Confidence: 50% - Suggested Strike: $185 (near ATM)

Interpretation: - Mixed signals → Don't position directionally - Stay ATM for calendar spread - Focus on IV crush, not direction

Files Created/Modified

New Files:

  1. trade_calc/directional_bias.py - Core analysis module
  2. scripts/directional_analysis.py - User-facing script
  3. DIRECTIONAL_BIAS_GUIDE.md - Comprehensive guide
  4. DIRECTIONAL_BIAS_SUMMARY.md - This document

Modified Files:

  1. scripts/monte_carlo.py - Added directional bias parameters
  2. README.md - Updated with directional analysis section
  3. scripts/run_yearly_analysis.py - Examples with directional bias

Next Steps

For Users:

  1. Try it out: 

    uv run python scripts/directional_analysis.py <YOUR_TICKER>

  2. Compare with your intuition:

  3. Does the bias match your expectations?
  4. Check against fundamentals

  5. See if confidence level makes sense

  6. Integrate into workflow:

  7. Use after calculator.py
  8. Before entering positions

  9. Compare with other analysis tools

  10. Track results:

  11. Record suggested bias vs actual moves
  12. Evaluate confidence calibration
  13. Refine personal interpretation

For Developers:

Potential Enhancements: 1. Add more signals (analyst ratings, social sentiment, order flow) 2. Machine learning for signal weighting 3. Historical backtesting of bias accuracy 4. Real-time signal monitoring 5. Integration with earnings calendar 6. Sector-relative analysis

Questions & Answers

Q: Will this make me pick the right direction every time? A: No. It increases probability but doesn't guarantee accuracy.

Q: Should I always follow the suggested strike? A: No. Use it as one input among many (fundamentals, risk tolerance, etc.).

Q: What if confidence is low? A: Stay ATM. Calendar spreads work well without directional bias.

Q: Can I use this for other strategies? A: Yes! Directional bias applies to any options strategy.

Q: How often should I run this? A: Before entering each trade, as market signals change constantly.

Q: What if signals conflict with fundamentals? A: Trust fundamentals. Technical signals can be misleading.

Conclusion

The directional bias feature completes the analysis chain:

  1. Screening: rank_tickers.py finds candidates
  2. Magnitude: calculator.py shows expected move
  3. Direction: directional_analysis.py predicts up/down ← NEW
  4. Testing: monte_carlo.py simulates outcomes with bias
  5. Execution: Trade with optimized strike selection

This gives traders a complete, data-driven approach to earnings calendar spread trading.

Support

  • Full Guide: DIRECTIONAL_BIAS_GUIDE.md
  • Monte Carlo Integration: MONTE_CARLO_GUIDE.md
  • General Usage: README.md
  • Discord: https://discord.gg/krdByJHuHc