Gamma Transmission Strategy Simulator: A Deep Dive into Options in Stock Market Microstructure and Momentum Flow
- Bryan Downing
- 11 minutes ago
- 9 min read
Gamma Transmission Strategy Simulator: A Deep Dive into Options Market Microstructure and Momentum Flow
Introduction: The Hidden Engine of Market Moves
In the fast-moving world of financial markets, especially within high-growth indices like the NASDAQ-100 (NAS100), price movements are rarely random. Behind every surge or plunge lies a complex web of institutional behavior, algorithmic trading, and mechanical market dynamics — many of which remain invisible to the average trader.
One such hidden driver is gamma transmission: the process by which sharp price moves in major index components trigger cascading hedging flows from options market makers, ultimately influencing the broader index. This phenomenon is not speculative — it's structural. And understanding it can give traders a powerful edge.
The Gamma Transmission Strategy Simulator, built using Streamlit, is an advanced educational tool that models this very mechanism. Designed for traders, quants, and students of market microstructure, it provides a hands-on simulation of how gamma squeezes in key NAS100 stocks like Apple (AAPL), Microsoft (MSFT), and NVIDIA (NVDA) can create predictable momentum that transmits across the entire index.
This article offers a comprehensive analysis of the readme.txt associated with app2.py, unpacking its technical architecture, strategic logic, mathematical foundations, and educational value. By the end, you'll understand not only how this simulator works — but why it matters in today’s algorithm-driven markets.
1. What Is the Gamma Transmission Strategy?
At its core, the Single-Stock Gamma Transmission Strategy exploits a well-documented market inefficiency rooted in options market microstructure. It hinges on one key insight:
When a heavily weighted stock in the NAS100 experiences a gamma squeeze, options dealers are forced to hedge — and their hedging behavior creates momentum that lifts the entire index.
Let’s break that down.
The Role of Market Makers and Gamma Exposure
Options market makers (dealers) earn profits from bid-ask spreads, but they do so while remaining delta-neutral — meaning they don’t want exposure to directional price moves. To maintain neutrality, they dynamically adjust their hedges based on their Greek exposures, particularly gamma.
Gamma measures how fast an option’s delta changes with respect to the underlying asset’s price.
High gamma means small price moves cause large changes in delta.
When gamma is concentrated (e.g., at key strike prices), even minor price breaks can force dealers to buy or sell aggressively to rebalance.
This creates a positive feedback loop:
Price rises → Dealer delta increases → Dealer buys more to stay neutral → Price rises further → Repeat.
This is the gamma squeeze.
Transmission to the Index
Now, imagine this happens in AAPL, which accounts for over 12% of the NAS100’s weight. As dealers hedge their short gamma positions in AAPL, they buy shares — pushing the stock higher.
But because AAPL is a major component, its upward momentum drags the NAS100 index higher, even if other stocks are flat.
Traders who recognize this chain reaction can ride the wave by entering long positions in NAS100 futures, ETFs (like QQQ), or CFDs as the squeeze unfolds — not after.
That’s the essence of the gamma transmission strategy.
2. Why Build a Simulator? The Need for Education and Insight
Despite the power of this strategy, most retail traders never encounter it — not because it’s rare, but because it’s invisible without the right tools.
Real-time gamma data, options chain analytics, and component-level impact modeling are typically locked behind expensive institutional platforms. Even when data is available, interpreting it requires deep knowledge of derivatives pricing, hedging mechanics, and index composition.
Enter the Gamma Transmission Strategy Simulator — a free, open-source, interactive tool built in Python using Streamlit. It democratizes access to institutional-grade concepts by simulating:
Realistic price behavior
Synthetic options chains
Gamma exposure concentration
Signal detection based on momentum, volume, and proximity to key strikes
Backtested performance of the strategy
It’s not designed for live trading — it’s an educational sandbox where users can experiment, learn, and develop intuition for how options market structure shapes price action.
3. Technical Architecture: How the App Works
Let’s now dive into the technical blueprint of app2.py, as outlined in the readme.txt. We’ll explore each section, from setup to visualization, explaining both the code and its strategic implications.
3.1 Configuration & Imports (Lines 1–8)
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import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from scipy.stats import norm
from datetime import datetime, timedelta
import random
This foundational block loads the essential libraries:
Streamlit: Enables rapid development of interactive web apps in Python.
Pandas/NumPy: Handle data frames, time series, and numerical operations.
Plotly: Generates dynamic, zoomable charts — crucial for visualizing price action and signals.
SciPy: Powers statistical functions like the normal distribution (norm) used in Black-Scholes option pricing.
Datetime: Manages timestamps, day counts, and simulation timelines.
Random: Adds stochastic (random) elements to simulate real-world market noise.
This stack ensures the app is both computationally efficient and visually engaging — critical for user retention and learning.
3.2 UI Setup & Strategy Overview (Lines 10–35)
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st.set_page_config(page_title="Gamma Transmission Strategy Simulator", layout="wide")
st.title("🎯 Single-Stock Gamma Transmission Strategy Simulator")
The user interface begins with a clean, professional layout. Using layout="wide" maximizes screen real estate — essential for displaying multiple charts and data tables side by side.
The title uses emojis (🎯) to draw attention, signaling this is not just a dry model — it’s a strategy simulator with actionable insights.
Additionally, the app likely includes a sidebar with:
Strategy explanation
Parameter controls
Educational tooltips
This UX design prioritizes accessibility, helping users grasp complex concepts even if they’re new to options or quant finance.
3.3 Parameter Configuration (Lines 37–70)
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component_weights = {
'AAPL': 0.128, 'MSFT': 0.118, 'NVDA': 0.075, 'AMZN': 0.062,
'META': 0.045, 'GOOGL': 0.041, 'TSLA': 0.030, 'AVGO': 0.029
}
This dictionary is critical — it reflects the real-world weightings of top NAS100 components. These weights determine how much impact a single stock’s move has on the index.
For example:
A 5% surge in AAPL could lift NAS100 by 0.64% (5% × 12.8%)
The same move in TSLA only adds 0.15%
The app uses these weights to aggregate component momentum into index-level impact, making the simulation economically realistic.
Other configurable parameters include:
Simulation duration (e.g., 60 days)
Volatility regime (low, medium, high)
Squeeze frequency (how often gamma events occur)
Entry/exit thresholds for signals
This flexibility allows users to test the strategy under different market conditions — a core principle of robust backtesting.
3.4 Core Mathematical Functions (Expanded) – The Quantitative Engine in Action
This section of the code forms the analytical backbone of the entire simulation. It's where abstract financial theory meets practical trading strategy. While the app provides a user-friendly interface, the real intelligence — the ability to detect gamma squeezes, model hedging pressure, and simulate momentum transmission — lives in these core functions.
Let’s break them down in detail.
Black-Scholes Greeks Calculation: Modeling Dealer Risk Exposure
The function calculate_greeks() implements the Black-Scholes-Merton option pricing model, which has been the cornerstone of derivatives valuation since the 1970s. While modern markets have evolved beyond its assumptions (constant volatility, no jumps, continuous hedging), it remains incredibly useful for estimating Greeks, especially gamma.
Here’s the full function with enhanced commentary:
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def calculate_greeks(S, K, T, r, sigma, option_type='call'):
"""
Calculate Delta and Gamma for European options using Black-Scholes.
Parameters:
S (float): Current spot price of the underlying asset
K (float): Strike price of the option
T (float): Time to expiration in years (e.g., 7 days = 7/365)
r (float): Risk-free interest rate (annualized)
sigma (float): Implied volatility (annualized)
option_type (str): 'call' or 'put'
Returns:
delta (float): Sensitivity of option price to underlying price
gamma (float): Rate of change of delta per unit move in underlying
"""
d1 = (np.log(S / K) + (r + 0.5 sigma * 2) T) / (sigma np.sqrt(T))
delta = norm.cdf(d1) if option_type == 'call' else -norm.cdf(-d1)
gamma = norm.pdf(d1) / (S sigma np.sqrt(T))
return delta, gamma, 0
Understanding Each Component
1. d1 – The Key Intermediate Variable
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d1 = (np.log(S / K) + (r + 0.5 sigma * 2) T) / (sigma np.sqrt(T))
np.log(S / K) → Logarithmic moneyness: tells us how far in/out-of-the-money the option is.
(r + 0.5 * sigma^2) → Drift term that adjusts for expected return and volatility drag.
T → Time decay factor.
sigma * sqrt(T) → Volatility scaled by time (standard deviation of returns).
d1 essentially measures how many standard deviations the current price is from the strike, adjusted for drift.
🔍 Insight: When S ≈ K (at-the-money), d1 ≈ 0, meaning norm.cdf(d1) ≈ 0.5. This is why at-the-money options have deltas near 0.5 (for calls) — and crucially, their gamma is maximized.
2. Delta – The First Derivative of Option Value
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delta = norm.cdf(d1) if option_type == 'call' else -norm.cdf(-d1)
For a call, delta = probability (under risk-neutral measure) that the option expires in-the-money.
For a put, it’s negative: -norm.cdf(-d1).
Why it matters for dealers:
Market makers sell options (e.g., calls) and hedge by buying delta shares.
If delta = 0.6, they buy 60 shares per 100-call contract to stay neutral.
But as the stock moves, delta changes — hence the need for gamma-aware hedging.
3. Gamma – The Second Derivative, the Heart of the Strategy
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gamma = norm.pdf(d1) / (S sigma np.sqrt(T))
norm.pdf(d1) → Height of the standard normal curve at d1. Peaks when d1 = 0, i.e., near-the-money.
Denominator scales gamma inversely with spot price, volatility, and time.
💡 Key Insight:Gamma is highest when:
The option is at-the-money (S ≈ K)
Expiration is near (T → 0)
Volatility is low
This means short-dated, at-the-money options have the highest gamma — and thus create the most hedging pressure when the underlying price moves.
Why Vega and Theta Are Ignored
The function returns 0 for the third value (likely meant for vega or theta), indicating they are not used in this simulation.
Vega (sensitivity to volatility) affects pricing but not dealer hedging behavior in the same mechanical way.
Theta (time decay) is important for PnL but doesn't drive intraday momentum.
In this context, gamma is the star — because it directly determines how aggressively dealers must buy or sell to maintain delta neutrality.
How the Simulator Uses calculate_greeks()
The app likely loops over a synthetic options chain (generated by generate_options_chain()) and computes gamma for every option — then aggregates it by strike level.
For example:
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total_gamma_per_strike = {}
for strike in strikes:
call_gamma = calculate_greeks(S, strike, T, r, sigma, 'call')[1] * open_interest_call[strike]
put_gamma = calculate_greeks(S, strike, T, r, sigma, 'put')[1] * open_interest_put[strike]
total_gamma_per_strike[strike] = call_gamma + put_gamma
Then, it identifies "gamma walls" — strikes with exceptionally high gamma. These become key levels where:
A break above triggers buying pressure (dealers cover short gamma by buying stock)
A break below triggers selling pressure
This is the foundation of squeeze detection.
Synthetic Market Data Generation: Simulating Realistic Price Action
Now let’s turn to the simulate_gamma_squeeze() function — the engine that brings the market to life.
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def simulate_gamma_squeeze(base_price, days, volatility, squeeze_freq):
# Base price movement with random walks
# Adds gamma squeeze events with specific patterns
# Returns DataFrame with price and volume data
This function does much more than generate noise — it creates a behaviorally rich, structurally accurate simulation of how stocks behave around gamma events.
Let’s reconstruct what this function likely contains.
Step 1: Generate Base Random Walk (No Squeeze)
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np.random.seed(42) # For reproducibility
dt = 1/24 # Hourly steps
steps = int(days * 24)
returns = np.random.normal(0, volatility * np.sqrt(dt), steps)
price = base_price * np.exp(np.cumsum(returns))
This creates a geometric Brownian motion (GBM) path — the standard model for stock prices. It captures:
Drift (zero mean, since we're focusing on deviations)
Volatility scaling
Continuous compounding
But this alone isn’t enough. Markets aren’t just random — they exhibit regimes, momentum, and structural breaks.
Step 2: Inject Gamma Squeeze Events
The magic happens here. The simulator artificially injects squeeze-like price action based on squeeze frequency:
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num_squeezes = int(days * squeeze_freq)
squeeze_times = np.random.choice(range(100, steps-100), size=num_squeezes, replace=False)
for t in squeeze_times:
# Only trigger if price is near a high-gamma strike
if abs(price[t] - key_strike) < 0.02 * price[t]: # Within 2%
# Accelerate price upward with increasing volume
ramp_duration = 6 # Next 6 hours
for i in range(ramp_duration):
if t + i < steps:
# Supercharge returns
returns[t + i] += np.random.normal(0.008, 0.002) # +0.8% per hour
volume[t + i] *= 3 # Volume spike
# Reset afterward
price = base_price * np.exp(np.cumsum(returns))
This models the feedback loop of a gamma squeeze:
Price approaches a high-gamma strike
Dealers are short gamma → fear losses
Any upward move forces them to buy spot to hedge
Their buying pushes price up → more hedging → more buying
The result? A self-reinforcing rally — exactly what we see in
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