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Algorithmic Trading Basics

A Guide to Algorithmic Options Trading

Shiv Darshan

As we are aware, an investor’s portfolio can include different securities such as stocks, bonds, gold and currencies. However, the investment avenues don’t end there, another security to be considered are Options. Options, as the name suggests, provide a choice to trade in the underlying security. For example, when trading stock options, you do not buy or sell the stocks. Instead, you buy or sell the option to buy and sell the stocks at a fixed price on (or before) a certain fixed date. This is why they are called option contracts.

There are two kinds of Options: Call & Put, and four kinds of Transactions: Buy Call, Sell Call, Buy Put, Sell Put.

The strength of trading options lies in their versatility to dynamically adapt your positions in accordance to the market situation that arises. Options can be used to protect either a long position or a short position on the security, speculate on the movement or volatility of the underlying, and even exploiting market inefficiencies. Options are more complex than other financial instruments and can be very risky, especially if you do not know what you are doing.

On the other hand, algorithmic trading involves using computer programs to execute trading strategies. This approach leverages automation to analyze market data, identify potential trading opportunities, and execute orders without direct human intervention free from biases.

Options and algorithmic trading can be a potent combination, amplifying the strengths of each other while mitigating some of their weaknesses. This is the first article in our series on Algorithmic Options Trading, helping you dive in and navigate the intricacies of Algorithmic Options Trading.

The Intersection of Options and Algorithmic Trading

Developing algorithms empower traders to leverage technology for more efficient and systematic decision-making in the volatile and complex options markets. It enables traders to respond swiftly to market changes, implement sophisticated strategies, and manage risk in a disciplined manner, free from human biases.

The combination can be highly beneficial, as algorithmic strategies can be designed to navigate the complexities of options markets, execute trades efficiently with minimum slippage, and manage risk effectively.

Here are several aspects that highlight the synergy between options trading and algorithmic trading:

1. Automation, Speed and Efficiency: Algorithmic trading involves using computer algorithms to systematically execute trades automatically based on predefined criteria. This automation can be especially beneficial in options trading, where timing and speed are crucial. Algorithms can quickly analyze market data, identify opportunities, and execute trades much faster than a human trader, which is essential in the fast-paced options market.

2. Risk Management: Options trading often involves complex strategies such as Iron Condors, Butterflies, Calendars, Dispersion trading, Volatility Arbitrage with multiple legs, making risk management a critical aspect. Algorithmic trading systems can be programmed to automatically manage risk by setting predefined stop-loss levels, adjusting positional deltas to mitigate directional risk, having allocation and position sizing mechanisms by dynamically adjusting positions based on market conditions, and implementing risk controls.

3. Complex Strategy Execution: Options trading allows for the implementation of various multi-leg complex strategies, such as Volatility Arbitrage, Straddles, Strangles, Iron Condors, Butterflies and Calendars. Algorithmic trading efficiently executes and manages these strategies, considering multiple parameters simultaneously while minimizing slippages and impact costs.

4. Optimization Algorithms: Employing Optimization algorithms fine-tune the parameters of option trading strategies during the optimization phase. These algorithms explore a wide range of parameter combinations to find the most optimum settings for maximizing returns or minimizing risk without overfitting.

5. Dynamic Hedging: Option Greeks such as Delta, Gamma, Theta, Vega play a crucial role in options trading and are essential for understanding and managing the risks associated with option positions. Dynamic hedging involves continuously adjusting a portfolio's Greeks to manage risk in response to changing market conditions. Algorithms can be employed to dynamically hedge first-order or higher-order Greeks in response to changing market conditions.

6. Statistical Analysis: Algorithms can analyze large option datasets and perform statistical analysis to identify patterns, trends, and opportunities in the options market. This information can be used to develop trading strategies based on historical data and market conditions.

7. Market Liquidity: Options can sometimes have lower liquidity compared to stocks. Algorithmic trading can help navigate these liquidity challenges by executing trades in a way that minimizes market impact and slippages.

8. Implied Volatility Trading: There is a popular saying amongst systematic Option Traders “You can afford to be on the wrong side of price and get away but never be on the wrong side of Implied Volatility as the results can be devastating”. Options are sensitive to changes in Implied volatility. Algorithmic trading systems can be designed to adjust trading strategies or position sizing based on implied volatility levels, helping traders take advantage of prevailing market conditions and avoiding larger drawdowns.

9. Arbitrage Opportunities: Algorithmic trading can identify and exploit arbitrage opportunities in the options market requiring sophisticated calculations and rapid execution, making them impractical for manual trading but well-suited for algorithmic approaches. This includes price discrepancies between options and their underlying assets or mispricing among different options contracts.

10. Market Making Algorithms: Market making in options involves continuously quoting buy and sell prices for options with the goal of profiting from the bid-ask spread and is conducted by firms having deep pockets. Market Making Algorithms facilitate quick decision-making, risk management, inventory management, and the ability to adjust to changing market conditions.

11. Real-time Monitoring: Algorithmic trading systems can continuously monitor the market in real-time, making split-second decisions based on changing conditions. This is crucial in options trading where prices can move rapidly.

12. Back-testing: Before deploying a strategy in a live market, algorithmic traders can use historical data to back-test their strategies, assessing the viability and stress-testing performance of their options trading algorithms under various market scenarios.

It's important to note that while algorithmic trading can enhance options trading, it can also introduce risks such as technical glitches and unforeseen market events. Traders should thoroughly test and monitor their algorithms, being aware of the potential risks involved in algorithmic trading.

The roadmap to learning algorithmic options trading in four steps

Adapting Options Trading Strategies for Algorithmic Trading

Adapting existing options trading strategies for algorithmic trading involves translating manual trading rules into automated processes. Let’s discuss the process of adapting an existing options trading strategy for algorithmic trading using the SPY ‘0’ DTE ( Days to Expiry) contracts. SPY 0 DTE contracts have become popular among traders, particularly those engaged in day trading or short-term speculative strategies due to intra-day opportunities, leverage, capital efficiency and high liquidity.

Example: Iron Condor SPY ‘0’ DTE (Days To Expiry)

1. Define the Strategy Rules:

  • This strategy involves selling OTM (out of the money) Calls and Puts and buying further OTM (out of the money) Calls and Put options with all the options contracts expiring today.
  • Rules might include selecting strike prices, expiration dates, and conditions for entering, exiting, or adjusting positions

2. Quantify Decision-Making Criteria:

  • Translate qualitative criteria into quantitative parameters, e.g.Initiate the ‘0’ DTE SPY Iron Condor under these conditions, first, the Implied Volatility Percentile (IVP) of SPY is greater than 50 at 9.35 am and second, the overnight price gap in SPY should be less than 0.3%.
  • Sell calls and puts with a delta of 0.14 and buy calls and puts with delta of .05
  • Set Take Profit parameters at 50% of the credit received and Stop loss as 125%
  • Implement other risk management rules to protect against large losses. This could include allocation methodology, position sizing rules, and other diversification strategies.

3. Data Requirements:

  • Historical Data: Collect historical price and options data for SPY (strike prices, expiration dates, premiums). Ensure the data is clean, accurate, and in a format suitable for algorithmic processing to avoid garbage in garbage out syndrome
  • Real-Time Data: Ensure access to real-time market data for SPY for live trading

4. Back-testing Algorithm Development:

  • Choose a programming language: The choice of language often depends on the specific needs of the trading strategy (like speed, complexity, data analysis capabilities) and the infrastructure of the trading firm (such as existing systems and team expertise). Python and C++ tend to be the most popular due to their respective strengths in ease of use and speed. Python is commonly used due to its extensive libraries for data analysis (Pandas, NumPy)
  • Write the code for the trading algorithm: This involves programming the strategy's decision-making process to include all decision-making criteria such as entries, exits, strike selection and risk management criteria.
  • Simulate Past Performance: Test the algorithm against historical data to see how it would have performed in the past. This step is crucial for identifying any flaws in the strategy. Back-testing must include realistic trading conditions, such as transaction costs, slippage, and market impact. Constantly hunt for any biases in the model. The goal here is to replicate live market conditions to avoid any surprises during live implementation.
  • Key Performance Metrics to Evaluate: Look at metrics like Total return, Annualized Returns, Drawdowns, and Performance ratios such as Sharpe, Calmar, MAR, Omega etc.

5. Optimization:

  • Tweak Parameters: We may adjust parameters like strike price distance, deltas, entry time, exit time, expiration period, take profit, stop loss, position sizing mechanism, and frequency of trading based on back-testing results. The key here is to avoid overfitting!

6. Paper Trading (Forward Testing):

  • Integration with Trading Platform: Use an API to integrate your algorithm with a brokerage platform that supports options trading, like ours
  • Real-time Testing: Before going live with real money, test the algorithm in real time with a simulated account. This will provide insight into how it performs under current market conditions.

7. Going Live and Monitoring:

  • Start Trading: Deploy the algorithm in a live environment with real capital.
  • Continuous Monitoring: Regularly monitor the algorithm's performance and make adjustments as needed,

8. Compliance:

  • Regulatory Adherence: Ensure all trading activities are compliant with financial regulations and trading platform rules. This process requires a careful blend of options trading knowledge, risk management, data analysis, and programming skills.

Risk Management in Options Trading

Risk management in algorithmic options trading involves a set of proactive and reactive strategies, techniques, and precautionary checks to minimize potential losses while maximizing returns.

Volatility Management and Allocation: Options are highly sensitive to the Implied Volatility (IV) of the underlying asset. Algorithms can be designed to quantify the Implied Volatility (IV) regime (High or Low) and allocate strategies in accordance with the prevailing market conditions. This approach would ensure the ‘Best Bang for the Buck’.

For example, it may be wise to allocate more to premium selling strategies such as Credit spreads, Iron Condors, and Butterflies in a high (IV) regime. It may be prudent to allocate more to Debit Spreads, Reverse Iron Condors and Short Butterflies during a low Implied Volatility (IV) regime on the underlying.

Options Greek Management: Option Greeks such as Delta, Gamma, Theta, Vega play a crucial role in options trading and are essential for understanding and managing the risks associated with option positions. Dynamic Hedging involves continuously adjusting a portfolio's positions Greeks to manage risk in response to changing market conditions. Algorithms can be employed to dynamically hedge first-order or higher-order Greeks in response to changing market conditions to keep Portfolio Greeks in Check.

Hitting the Pause Button: Algorithms can be used to systematically ‘Hit the pause button’ or halt trading if the strategy experiences a ‘n’ number of consecutive losers, a daily stop-loss limit hit, or if the underlying has gapped down more than ‘x’%, etc.

Leverage Management: A leverage check is imperative, as options can provide high leverage, which amplifies both gains and losses. Algorithms incorporate risk measures like Value at Risk (VaR) or Expected Shortfall (ES) to estimate potential losses. They adjust leverage dynamically to align with risk tolerance and maintain portfolio resilience

Model Risk, Scenario Analysis and Stress Testing: Model risk is the risk that algorithmic models are based on incorrect assumptions or that they fail to account for certain market conditions and have inherent biases. Continuous back-testing against historical data and stress testing under various market scenarios are essential to mitigate model risk. Regularly testing the algorithms against extreme but plausible market scenarios to understand potential risks and prepare contingency plans for outlier events should be a mandatory practice.

Diversification: Diversifying trading strategies across different underlyings, markets and expiries is a tool to mitigate risk. Trading algorithms facilitate this process with efficiency and accuracy.

Stop-Loss Mechanisms and Position sizing: Trading algorithms can be employed to implement automatic stop-loss orders or other risk-limiting mechanisms to cap losses if the market moves against the position. Proper position and sound allocation sizing must be included to ensure that a loss on a single trade does not significantly impact the overall portfolio.

Margin Monitoring: Algorithms can closely monitor margin requirements to avoid account breaches and maintain adequate liquidity.

Operational Risk: Includes risks from system failures, network latency, and other technical issues. Robust infrastructure with fail-safes, redundancy plans, and rapid response mechanisms is crucial.

Regulatory Compliance: Ensuring that the trading algorithms comply with all relevant regulations to avoid legal and financial penalties.

Market Impact and Liquidity: Algorithmic trading can sometimes impact the market, particularly with large orders. Algorithms should be designed to minimize this impact, often by breaking up large orders into smaller ones. Liquidity is also a crucial factor, as it affects the ease of entering and exiting a position.

Risk management model when trading options algorithmically

By addressing these aspects, traders and firms can better manage the risks inherent in algorithmic options trading and improve their chances of success in this highly competitive field.

Demystifying Black-Scholes and the Greeks


Modern options trading began to take shape in 1973, when the Chicago Board of Options Exchange (CBOE) was formed. In the same year, Fisher Black, and Myron Scholes (of the Black-Scholes Pricing model), devised a mathematical formula that could calculate the price of an Option using specified variables. This had a major impact on the Options trading, and daily volume traded in the options contracts increased. As of January 25, 2024, the daily traded volume of options contracts exceeded 46 million.

The Black-Scholes model is a foundational tool in the world of options trading, playing a crucial role in both pricing and risk management. Here is a breakdown of how it works:

Pricing: The Black-Scholes model provides a theoretical framework for estimating the fair value of an option based on several factors:

  1. Underlying asset price: The current price of the asset the option is based on (e.g., a stock index like SPX).
  2. Strike price: The price at which the option can be exercised to buy or sell the underlying asset.
  3. Time to expiration: The remaining time until the option expires and becomes worthless.
  4. Risk-free interest rate: The prevailing rate of return on a risk-free investment (e.g., government bonds).
  5. Implied Volatility: The expected future fluctuations in the underlying asset price.

Output: The model calculates a theoretical "fair value" for the option based on these factors. This value serves as a starting point for traders to negotiate option prices and assess whether a particular contract is "fairly priced" or worth buying or selling.

The Option Greeks

The Option Greeks are like the language of options, providing crucial insights into how an option's price will react to changes in various market factors. It becomes even more powerful, enabling sophisticated strategies and automated risk management.

Let us delve into the intricate relationship between Option Greeks and Algorithmic trading:

Understanding the Power of Greeks:

  • Delta: Measures the rate of change in an option's price with respect to the underlying asset's price. Algorithmic trading can leverage delta to dynamically adjust positions, hedge exposure, and capitalize on delta-neutral strategies like covered calls.
  • Gamma: Indicates how delta changes as the underlying asset price moves. For algorithms, understanding gamma helps manage delta exposure over time and adjust positions to maintain desired risk profiles.
  • Theta: Represents the time decay of an option's value as expiration approaches. Algorithmic trading can use theta to optimize entry and exit points, dynamically adjust positions based on time remaining, and exploit theta decay in spreads.
  • Vega: Quantifies the sensitivity of an option's price to changes in implied volatility. For algo trading, understanding vega allows for dynamic adjustments to positions based on Implied volatility fluctuations, employing volatility arbitrage strategies, and hedging vega exposure.

Applying Greeks to Algorithmic Applications:

  • Automated Hedging: Algorithms can utilize Greeks like delta and vega to implement dynamic hedging strategies, automatically adjusting positions in other options or the underlying asset to minimize risk.
  • Volatility Targeting: By monitoring vega, algorithms can identify and capitalize on opportunities arising from changes in implied volatility, such as selling options when volatility spikes or buying them when it falls.
  • Greek based selection: Algorithms can analyze options based on their Greek profiles, choosing contracts with desirable delta, theta, and vega characteristics to match specific trading goals.
  • Risk Management: Through Greeks, algorithms can automatically implement stop-loss orders, adjust position sizes, make adjustments and monitor overall portfolio risk exposure, ensuring adherence to pre-defined risk parameters.

Exploring Algorithmic Options Strategies

  1. Directional Strategies: These strategies involve predicting the direction in which the price of an underlying asset will move and can either be Trend Following or Mean Reverting in nature. Subject to Implied Volatility (IV) of the underlying we can use plain vanilla Calls or Puts, the four Vertical Spreads (Bull Call Spread, Bear Put Spread, Bull Put Spread and Bear Call spreads) to trade direction. These generally tend to be low-probability trades with a higher return profile.
  2. Non-Directional Strategies: Non-directional trading strategies are the best option for traders who do not want to bet on the direction of the markets or individual stocks. These are essentially Range-Bound trades where the trader profits irrespective of whether the underlying moves up, down, sideways within the specified range. These are high-probability trades with a higher win rate but lesser profit potential compared to directional low-probability trades. Iron Condors, Butterflies, Calendars, Short Strangle and Short Straddles, etc.
  3. Volatility Trading Strategies: These strategies bet on a sustained unidirectional move on the underlying (either up or down) and include Long Straddles, Long Strangles, Short Butterfly and Short Condor. These Strategies capitalize on Volatility expansion after a sustained contraction.
  4. Implied Volatility Skew Trading Strategies: Implied volatility skew refers to the observation that option prices for different strike prices within the same expiration date don't have the same implied volatility (IV). This creates opportunities for traders to exploit the Volatility skew. Traders identify options where the implied volatility is either too high or too low compared to their historical levels or compared to other strikes or expirations. For example, if the (IV) on out-of-the-money (OTM) calls is unusually high compared to at-the-money (ATM) calls, a trader might sell the OTM calls and buy ATM calls, betting on a reversion to the mean.
  5. Dispersion Trading: Dispersion trading is a way to trade ‘implied correlation’ between ‘implied volatility of an index’ and ‘implied volatility of the index constituents. This correlation is used as a factor to determine the trading signals. It assumes that this implied correlation reverts to its mean. Depending on the value of the correlation, dispersion trading can be carried out by going short (or long) on the index options and long (or short) on the options of the index constituents. Dispersion trading also requires all the trades to be delta-hedged so that the value of the portfolio is not affected by the changes in the prices of the underlying assets.
  6. Market Making: Market making in options offers a fascinating and challenging arena for algorithmic trading. These market makers aim to provide liquidity in the options market by quoting bid and ask prices for various contracts, earning profits from the bid-ask spread and capitalizing on market movements. The key challenge here is Inventory risk and speed of execution.
  7. Delta Hedging: Delta hedging is a risk management strategy used in options trading to reduce or neutralize the directional risk associated with price movements in the underlying asset. It involves creating a hedge against price movements or Implied Volatility (IV) Fluctuations in the underlying asset by taking an offsetting position with opposing Option Greeks.

Tying it Together

The synergy between options trading and algorithmic trading can offer significant benefits for both experienced and aspiring Option Traders. Algorithmic Option Trading strategies can be designed to navigate the complexities of options markets, execute trades efficiently with minimum slippage, and manage risk effectively. This powerful combination enhances the strengths of each approach while mitigating some of their individual weaknesses.

Embarking on the algorithmic Option Trading path might initially seem daunting for option traders or developers lacking experience in options terminology could feel overwhelmed at first glance. However, rest assured that our forthcoming articles will comprehensively guide you through the entire process flow, from backtesting to live implementation. We’ve got you covered by breaking down the intricacies to make your Algorithmic Options Trading Journey a meaningful one.

In our next instalment, we cover the importance of building and backtesting your trading strategies.

Options trading is not suitable for all investors due to its inherent high risk, which can potentially result in significant losses. Please read Characteristics and Risks of Standardized Options before investing in options.

All investments involve risk and the past performance of a security, or financial product does not guarantee future results or returns. There is no guarantee that any investment strategy will achieve its objectives. Please note that diversification does not assure a profit, or protect against loss. There is always the potential of losing money when you invest in securities, or other financial products. Investors should consider their investment objectives and risks carefully before investing.

The Paper Trading API is offered by AlpacaDB, Inc. and does not require real money or permit a user to conduct real transactions in the market. Providing use of the Paper Trading API is not an offer or solicitation to buy or sell securities, securities derivative or futures products of any kind, or any type of trading or investment advice, recommendation or strategy, given or in any manner endorsed by AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate and the information made available through the Paper Trading API is not an offer or solicitation of any kind in any jurisdiction where AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate (collectively, "Alpaca") is not authorized to do business.

Please note that the content is for informational purposes and is believed to be accurate as of posting date but may be subject to change. All screenshots are for illustrative purposes only.

Securities brokerage services are provided by Alpaca Securities LLC ("Alpaca Securities"), member FINRA/SIPC, a wholly-owned subsidiary of AlpacaDB, Inc. Technology and services are offered by AlpacaDB, Inc.

This is not an offer, solicitation of an offer, or advice to buy or sell securities or open a brokerage account in any jurisdiction where Alpaca Securities are not registered or licensed, as applicable.

Algorithmic Trading Basics