How to Build a Python Event-Driven Trading Bot: The NVDA Earnings Momentum Strategy

Note: This article is an expanded masterclass based on a recent breakdown posted on my Substack. You can read the original post here: Building a Quick Event-Driven Algo: The NVDA Earnings Momentum Bot. I highly encourage you to subscribe to the Quantlabs Substack, as it is the fastest-growing platform I engage with, and it’s where I drop all my latest code, strategies, and market analysis first.

Introduction: The Power of Automation During Market Catalysts

Whenever a massive market catalyst approaches—like an NVIDIA (NVDA) earnings call, a Federal Reserve FOMC rate decision, or a critical CPI inflation print—the market braces for impact. Volatility spikes, liquidity thins out just before the announcement, and then, in a fraction of a second, the market reprices based on the new information.

Discretionary traders sit at their screens, their fingers hovering nervously over the buy and sell buttons, trying to read the tape, interpret the news, and execute a trade all at once. It is a high-stress, error-prone environment. Human reaction times are simply too slow to capture the initial explosive move, and human emotions are too fragile to manage the subsequent whiplash.

But as quantitative traders, we can do better. We can build, test, and deploy a Python event-driven trading bot to capture momentum before human reaction times even register.

Today, we are going to take a massive deep dive into a specific script: the Nasdaq-100 AI Momentum Bot. I wrote this Python event-driven trading bot specifically as a beta play for NVDA earnings. This script is a perfect example of a “rapid-deployment” algorithm. It’s something you can spin up quickly to capitalize on a specific macroeconomic event or earnings call without needing a massive, machine-learning-driven black box.

📊 Quick Reader Survey: Where Do You Learn Best?

Before we dive into the architecture of this Python event-driven trading bot, I want to ask you a quick question. What is your favorite platform to engage with or learn quantitative finance and algorithmic trading?

Are you a visual learner who prefers YouTube tutorials? Do you like the real-time chat of Discord communities? Or do you prefer long-form, code-heavy newsletters like Substack?

Please drop a comment at the end of this article (or directly on my Substack post) and let me know! Your feedback helps me tailor where and how I release these open-source trading scripts. Currently, Substack is the fastest-growing platform I engage with, but I always want to hear directly from the community.

Part 1: The Philosophy of Event-Driven Trading for NVDA Earnings Momentum Strategy

Before we look at the code, we need to understand the why. Why trade NVDA earnings using Nasdaq-100 (NQ) futures? Why use a Python event-driven trading bot instead of just buying call options?

The Catalyst: NVDA as the Market General

In recent years, NVIDIA has become the undisputed bellwether for the entire artificial intelligence sector, and by extension, the broader tech market. When NVDA reports earnings, it is not just reporting on its own balance sheet; it is providing a real-time health check on global AI infrastructure spending.

If NVDA issues a guidance "beat," it acts as a massive risk-on signal for the entire market. Short sellers are forced to cover, institutional managers are forced to chase performance, and retail traders pile into the momentum.

The Instrument: Why NQ Futures?

Instead of trading NVDA stock directly, this Python event-driven trading bot trades E-mini Nasdaq-100 (NQ) or Micro E-mini Nasdaq-100 (MNQ) futures. Why?

1. Leverage and Capital Efficiency: Futures provide significant leverage. You can control a large notional value of the tech market with a relatively small margin deposit.

2. Liquidity: The NQ futures market is incredibly liquid, even in the after-hours session when earnings are released. This ensures tight bid-ask spreads and minimal slippage.

3. Beta Play: Trading the index rather than the individual stock protects you from idiosyncratic risks (e.g., NVDA beats on revenue but misses on gross margins, causing a weird stock reaction, while the broader tech market still rallies on the AI narrative).

The Edge: Speed and Discipline

A Python event-driven trading bot has two massive advantages over a human trader: speed and discipline. The bot does not hesitate. If the predefined conditions are met, it fires the order. Furthermore, it manages risk automatically. If the trade goes against us, the bot cuts the loss at exactly −3%-3\%−3%. No hoping, no praying, no moving the stop loss.

Part 2: Deconstructing the Strategy Logic

The logic behind this Python event-driven trading bot is simple but highly effective for capturing momentum bursts. It relies on a combination of fundamental triggers and technical confirmations.

1. The Trigger: Fundamental Catalyst

• NVDA announces a guidance “BEAT”. The bot requires this fundamental catalyst to even consider looking for a trade.

2. The Confirmation: Technical Confluence A fundamental beat is not enough. The market must agree with the news. We use three technical indicators to confirm the market's risk-on appetite:

• RSI(14) > 50: The Relative Strength Index must be above 505050, indicating that the immediate momentum is bullish.

• Bullish MACD Crossover: The Moving Average Convergence Divergence (MACD) line must be above the signal line, confirming upward acceleration.

• VWAP Pullback: We don't want to buy the absolute top of the initial spike. The bot waits for the price to pull back within 0.5%0.5\%0.5% of the 20-period Volume Weighted Average Price (VWAP). This ensures we are getting a fair average price relative to the volume traded.

3. The Execution: Position Sizing

• Go LONG 5 E-mini Nasdaq-100 (NQ) contracts (or 10 Micro MNQ contracts for smaller accounts).

4. The Risk: Capital Preservation

• Strict −3%-3\%−3% stop loss.

• +6%+6\%+6% primary target.

• +2.5%+2.5\%+2.5% session target (if the move happens quickly within the first 8 hours, we take the money and run).

• A circuit breaker to halt trading if the bot experiences a 5%5\%5% maximum drawdown.

Part 3: Code Breakdown - Under the Hood

Let’s dive into the code that makes this Python event-driven trading bot function. This is a robust, object-oriented script designed for rapid deployment.

1. Configuration and Risk Parameters

At the very top of the script, we define our rules of engagement. Hardcoding these parameters at the module level allows us to quickly tweak the bot for different volatility environments.

# Risk parameters

TARGET_PCT = 0.06              # +6% target

STOP_LOSS_PCT = 0.03           # -3% stop loss

TARGET_SESSION_PCT = 0.025     # +2.5% session target (post-earnings)

# Technical indicators

RSI_PERIOD = 14

RSI_THRESHOLD = 50             # RSI > 50 for entry

VWAP_PERIOD = 20

Why these specific numbers? The 6%6\%6% target and 3%3\%3% stop give us a 2:12:12:1 reward-to-risk ratio. This means we only need to be right 33%33\%33% of the time to break even. Notice the TARGET_SESSION_PCT. Post-earnings moves can be explosive, but they can also fade fast. The bot is programmed to take a quick 2.5%2.5\%2.5% profit if it happens within the first 8 hours.

2. The Circuit Breaker: Your Algorithmic Kill Switch

When trading highly volatile events, your Python event-driven trading bot needs a kill switch. I implemented a CircuitBreaker class to protect capital at all costs.

class CircuitBreaker:

    def record_trade(self, pnl_pct: float):

        # ... calculates equity curve ...

        if drawdown_pct > self.max_drawdown:

            self.is_paused = True

            logger.warning(f"Circuit breaker triggered: Max drawdown exceeded")

If the bot hits a 5%5\%5% max drawdown or suffers two consecutive losses, it automatically pauses. No emotional revenge trading allowed.

3. The Alpha Logic: Checking the Signal

The core brain of the Python event-driven trading bot lives in the check_momentum_signal method. It doesn’t just blindly buy because NVDA beat earnings; it waits for the market structure to confirm the risk-on appetite.

def check_momentum_signal(self, market_data: MarketData) -> MomentumSignal:

    signal = MomentumSignal()

    # 1. Did NVDA beat? (Fundamental Trigger)

    signal.nvda_beat = market_data.nvda_guidance == NVDAGuidance.BEAT

    # 2. Are we in an uptrend? (Technical Confirmation)

    signal.rsi_above_50 = market_data.rsi > RSI_THRESHOLD

    signal.macd_bullish = market_data.macd > market_data.macd_signal

    # 3. Are we getting a good entry price? (Execution Optimization)

    if market_data.vwap > 0:

        vwap_distance = abs(market_data.nq_price - market_data.vwap) / market_data.vwap

        signal.price_at_vwap = vwap_distance <= 0.005 # Within 0.5% of VWAP

    signal.signal_valid = (signal.nvda_beat and signal.rsi_above_50 and 

                           signal.macd_bullish and signal.price_at_vwap)

    return signal

4. Execution Architecture: Redis Pub/Sub

To make this Python event-driven trading bot fast and scalable, it doesn’t execute trades directly via a slow API loop. Instead, it uses Redis Pub/Sub to instantly broadcast trading commands to an execution engine.

def execute_entry(self, market_data: MarketData, signal: MomentumSignal):

    order = {

        'symbol': SYMBOL,

        'exchange': EXCHANGE,

        'side': 'BUY',

        'quantity': CONTRACTS,

        'order_type': 'MARKET',

        'action': 'MOMENTUM_ENTRY'

    }

    # Instantly publish the order to the execution channel

    self.r.publish(TRADING_COMMANDS_CHANNEL, json.dumps(order))

This decoupled architecture means you can run the strategy logic on one server and the API execution on another, minimizing latency to near-zero.

Part 4: Expanding and Scaling the Bot

This script is just a sampling of what is possible. Once you understand this architecture, you can expand your Python event-driven trading bot infinitely.

• Adapting to Other Macro Events: Change the trigger from NVDA earnings to the Federal Reserve Interest Rate Decision. If rates are cut, and RSI > 505050, go long.

• Adding Machine Learning: Replace static rules with an ML model trained on historical 1-minute data surrounding the last 20 earnings seasons.

• Cloud Deployment: Deploy this bot on an AWS EC2 instance to ensure the bot runs 24/7 with a local Redis server.

Conclusion: Join the Quant Community

You don’t always need a massive, machine-learning-driven black box built by a team of PhDs to find an edge in the market. Sometimes, combining a known binary event with standard momentum indicators, strict risk management, and a fast execution architecture is all you need.

This Python event-driven trading bot is just one of many examples of algorithms I deploy in my own trading. By automating the execution, we remove human emotion, eliminate hesitation, and capitalize on the predictable mechanics of market microstructure.

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