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Options Straddle

Volatility trading with options strategies

Straddle Backtest Results

Analysis of long straddle performance on STOXX 50 options with different strike prices. The strategy tests various entry points based on call-put price convergence.

Options Straddle Payoff Diagram

Options Straddle Payoff Diagram

Payoff diagram showing profit/loss zones. Green areas indicate profitable regions when spot price moves significantly away from strike price, while red areas show loss zones near the strike price where time decay dominates.

Options Straddle Theory

Options straddle is a volatility trading strategy that enables traders to profit from significant price movements regardless of direction. The strategy involves simultaneously buying a call option and a put option with the same strike price, same expiration date, and preferably similar option prices.

Long straddle has unlimited profit potential for upside movements and limited loss risk. The maximum loss occurs when the underlying asset closes exactly at the strike price at expiration, resulting in both options expiring worthless. The strategy is particularly effective for event-driven trading such as earnings announcements, political events, or major economic releases.

The core challenge lies in strike price selection and timing. Since option prices incorporate market consensus about future volatility, success depends on finding optimal strike prices that minimize the loss bandwidth. This is where fundamental analysis becomes crucial - economists provide base case outlooks plus best/worst scenarios to guide strike price selection.

Unlike common misconceptions about quantitative trading, Option Greeks are not a silver bullet. The most effective approach combines quantitative analysis with fundamental research (quantamental) to create robust trading strategies.

Mathematical Foundation

1
Straddle Payoff Function

P(ST)=max⁡(ST−K,0)+max⁡(K−ST,0)−C−PP(S_T) = \max(S_T - K, 0) + \max(K - S_T, 0) - C - P

Where P(S_T) is the payoff at expiration, S_T is the spot price at expiration, K is the strike price, C is the call premium, and P is the put premium.

2
Breakeven Points

BEupper=K+(C+P),BElower=K−(C+P)BE_{upper} = K + (C + P), \quad BE_{lower} = K - (C + P)

The strategy becomes profitable when the underlying price moves beyond either breakeven point. The distance between breakevens represents the range where the strategy loses money.

3
Maximum Loss

Max Loss=C+PMax\,Loss = C + P

Maximum loss occurs when the underlying closes exactly at the strike price at expiration, making both options worthless. This represents the total premium paid.

4
Signal Generation Threshold

∣C−P∣<θ|C - P| < \theta

Trading signal is generated when the absolute difference between call and put option prices is less than threshold θ, indicating price convergence and balanced market sentiment.

Algorithm Implementation

The options straddle algorithm focuses on identifying optimal entry points where call and put option prices converge, indicating balanced market sentiment and fair pricing.

Strike Price Filtering

python
def find_strike_price(df):
    temp = [re.search('\d{4}', i).group() for i in df.columns]
    target = []
    
    for i in set(temp):
        if temp.count(i) > 1:
            target.append(i)
            
    return target

Extracts strike prices that have both call and put options available. This is fundamental for straddle implementation as we need matching option pairs.

Data Preparation

python
def straddle(options, spot, contractsize, strikeprice):
    option = options[[i for i in options.columns if strikeprice in i]]
    df = pd.merge(spot, option, left_index=True, right_index=True)
    
    # Label columns appropriately
    temp = []
    for i in df.columns:
        if 'C' + strikeprice in i:
            temp.append('call')
        elif 'P' + strikeprice in i:
            temp.append('put')
        elif 'Index' in i:
            temp.append('spot')
        else:
            temp.append(i)
    
    df.columns = temp
    df['spot'] = df['spot'].apply(lambda x: x * contractsize)
    return df

Merges option price data with spot prices and normalizes column names. Contract size adjustment ensures proper profit/loss calculations for visualization.

Signal Generation

python
def signal_generation(df, threshold):
    df['signals'] = np.where(
        np.abs(df['call'] - df['put']) < threshold,
        1, 0
    )
    return df

Generates trading signals when call and put option prices converge within the specified threshold. This indicates balanced market sentiment and optimal entry conditions.

Profit Calculation

python
def calculate_profit(df, strikeprice, contractsize, entry_index):
    profit = np.abs(
        df['spot'].iloc[-1] - int(strikeprice) * contractsize
    ) - df['call'][entry_index] - df['put'][entry_index]
    
    return profit

Calculates the theoretical profit/loss based on final spot price relative to strike price, minus the total premium paid for both options.

Implementation Steps

  1. 1Identify available strike prices that offer both call and put options in the options chain
  2. 2Filter option pairs where call and put prices are converging (within threshold)
  3. 3Calculate contract size adjustments for proper position sizing and P&L calculations
  4. 4Generate trading signals when price convergence criteria are met
  5. 5Monitor position through expiration and calculate final profit/loss
  6. 6Plot payoff diagrams to visualize profit zones and breakeven points
  7. 7Implement risk management rules for early exit if volatility collapses

Key Metrics

Maximum loss limited to total premium paid (C + P)
Unlimited profit potential for significant price movements
Two breakeven points: Strike ± Total Premium
Time decay (theta) works against the position
Volatility expansion increases position value
Best suited for high volatility events and announcements

Risk Considerations

Time decay erodes option value as expiration approaches
Requires significant price movement to overcome premium costs
Volatility collapse can result in maximum loss even with price movement
Limited time horizon due to option expiration constraints
Early assignment risk for American-style options
Liquidity issues in options with wide bid-ask spreads
Model risk in options pricing and implied volatility calculations
Event risk where anticipated volatility fails to materialize

Practice Implementation

Prerequisites

Mathematical Background

  • • Linear regression and OLS estimation
  • • Time series analysis (stationarity, unit roots)
  • • Hypothesis testing and p-values
  • • Basic econometrics (error correction models)

Technical Skills

  • • Python programming (pandas, numpy)
  • • Statistical libraries (statsmodels)
  • • Data visualization (matplotlib)
  • • Financial data handling (yfinance)

Complete Implementation

Access the full Python implementation from the original quantitative trading repository:

bash
# Complete pair trading implementation
git clone https://github.com/je-suis-tm/quant-trading.git
cd quant-trading
python "Pair trading backtest.py"
# Modify tickers and parameters for your own analysis

Learning Checkpoints

1

Understand Cointegration

Can you explain why two assets might be cointegrated and what breaks this relationship?

2

Interpret Statistical Tests

Practice reading ADF test results and understanding when to accept/reject cointegration.

3

Signal Generation

Implement Z-score calculations and understand threshold selection (±1σ vs ±2σ).

4

Risk Management

Understand position sizing, monitoring regime changes, and exit strategies.

Recommended Learning Path

Immediate Actions

  • Download and run the Python script
  • Test with different asset pairs
  • Experiment with threshold parameters

Advanced Studies

  • Learn Johansen cointegration test
  • Study Vector Error Correction Models
  • Explore multiple asset pair trading

Important Disclaimer

This strategy involves significant risk. Historical cointegration relationships can break permanently. Always use proper risk management, position sizing, and never risk more than you can afford to lose. Paper trade extensively before using real capital.

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