Top 7 Algorithmic Trading Strategies with Examples and Risks

At the core, algorithmic trading removes emotional bias from trade decisions and replaces it with data-driven logic. Whether you are an intraday trader, a scalper who likes to take quick trades or a long-term or positional trader, who likes to keep the position open for weeks to months, the underlying principle remains the same: let the code do the heavy lifting.

What Are Algorithmic Trading Strategies

Algorithmic trading strategies are predetermined rules that automate the process of buying and selling financial assets. These strategies make use of mathematical models, statistical analysis and programming logic for trade decisions, thereby eliminating the need for constant human intervention.

Some of the important features of algorithmic trading are:

• Speed ​​and precision: Algorithms can perform orders in milliseconds, much faster than any human trader.
• Scalability: When a strategy proves effective, it can be applied to different asset classes and even across various markets.
• Backtesting: Before going live, algorithms can be tested on historical data to evaluate their efficiency.
• Rules-based logic: Strategies are often based on indicators, price action, or statistical models, which can be created in an objective manner with ease. 
• Efficiency: Algorithmic trading not only reduces the monitoring and screen time, but it also leads to better efficiency of logic execution. 

Algorithmic trading is used in equity, futures, options, forex and cryptocurrency, and its popularity has increased due to the increasing access to coding languages such as Python. 

Moreover, brokers now offer low-cost APIs that can assist in creating complex trading strategies, competing with institutional systems in speed and logic.

Core Types of Algorithmic Trading Strategies

1. Mean Reversion

The mean reversion strategy is based on the idea that prices can revert to their historical average over time. These strategies identify assets that have deviated considerably from their average, and then there is an expectation that the asset will return to the mean. This is usually done using calculations on multiple indicators such as the moving average, Bollinger bands, and RSI. Mathematical models such as the z-score are also used to predict reversions. 

Example:

• A stock trades 10% over the 20-day moving average. The algorithm sells stock (or shorts), expecting it to fall back to the average.
• Alternatively, if the value falls much below the average, the algorithm will buy.

Used in: stock, ETF, objects and currencies. This works especially well in the range-bound markets.

Risks:

• This strategy is ineffective during strong trends.
• It is important to carefully calibrate the “means” and threshold from where the reversal is expected.

2. Arbitrage

The purpose of the arbitrage strategy is to make the most from temporary price deviations between the respective markets or assets.

The algorithm consistently scans several markets or instruments and executes long and short trades at the same time when it detects profitable mispricings. There are many types of strategies which are popular:

• Statistical arbitrage: This strategy uses mathematical models to identify mispricing in correlated assets.
• Cross-exchange arbitrage: Usually common in cryptocurrency where traders can buy on exchange A and sell together on exchange B of the same underlying virtual asset.
• Index arbitrage: This strategy utilises the value difference between an index and its underlying components.
• Merger arbitrage: Done when companies are going to merge. There could be good opportunities if a company is undergoing a merger or acquisition. 

Example:

• If Reliance shares are trading at ₹2,400 on the NSE and at ₹2,410 on the BSE, the algorithm would buy on the NSE and sell on the BSE simultaneously, thereby making a profit of ₹10. 

Used in: Equity, futures, cryptocurrency, foreign currency.

Risks:

• There are very small profit margins, and the cost of transactions can eliminate the profits.
• Market efficiency and competition reduce opportunities.
• It is important that the setup has low latency and fast execution, otherwise it could lead to substantial slippages.

3. Index Fund Rebalancing

This strategy works by taking advantage of the predictable nature of index rebalancing phenomena, such as changes in the composition or weightage of stocks in a benchmark index such as the Nifty 50 or the S&P 500.

The index fund, which aims to reflect any benchmarks, should buy or sell shares when the index itself undergoes periodic changes, which can be quarterly or yearly. This trading is usually executed close to the rebalancing date, which often leads to temporary value pressure on affected shares.

So in this strategy, the algorithms must predict the flow of funds that the passive fund will be putting into a particular stock.

And finally, the algorithm tries to take advantage of this step by going into positions before the fund or institutions take the trade. This is usually a low-risk strategy because it is based on the predictable, rule-based events.

Example:

• A stock is about to be added to the Nifty 50.
• The algorithm buys it quickly and assumes that the index funds must buy a large amount, which will make the price jump.

Risks:

• This is a fairly common strategy. Many Traders can try to do front running.
• Exact time and data access are required to take advantage.

4. Trend Following

The trend following strategies are aimed at riding the trend of the market by identifying and acting along with the movement of the underlying. These algorithms are tracking moving averages or directional indicators to determine when a trend has occurred and been confirmed. 

When a trend is identified, the algorithm enters the trade in the same direction and often remains until the trend shows signs of reversal or weakness.

Some popular indicators used for trend continuation are the Moving Average Crossovers (e.g., 50 EMA crossing 200 EMA), MACD (Moving Average Convergence Divergence), ADX (Average Directional Index), Donchian Channels and breakouts of key support/resistance levels The strategy works well in trend markets and can capture large price profits over time.

Example:

• If a stock breaks a strong resistance with high volume and the 50-day moving average is also going up, the algorithm will enter a long position and use the 50-day moving average as the trailing stop loss.

Risks:

• The strategy does not perform well in sideways markets or in choppy markets.
• There can be many whipsaws or false breakouts, which usually lead to low accuracy of these strategies.

5. Market Timing

Market timing strategy tries to predict future market direction and enter or close positions accordingly. They are usually done by using macroeconomic indicators, sentiment analysis or technical indicators. In this strategy, the algorithms use historical data and future models to determine when to enter the market and when to stay out of the market (or go short)

The purpose is to avoid taking too many trades and only participate under favourable conditions.

Some of the indicators used in these strategies are moving average or trend filter (for example, if the price> 200 DMA), economic indicators such as interest rates, inflation and GDP trends, volatility-based model (for example, Vix threshold) and news-based sentiment analysis using NLP models.

Example:

• If macro indicators suggest economic recession and technical indicators show weakness, the algorithm can reduce equity contact or switch to a defensive asset class such as bonds.
• This helps to reduce downside risk and helps in improving the risk-adjusted returns for traders.

Risks:

• The market timing is very difficult.
• Models can fail in unexpected or black swan events.

6. VWAP & TWAP Strategies

The purpose of these strategies is to reduce the impact cost of trading and achieve better trade prices by performing orders that fit the average market prices over time. These are of two types:

• VWAP (Volume Weighted Average Price): VWAP strategies break large orders into small pieces and trade them throughout the day to match the average price weighted by the volume traded. This kind of strategy is ideal for institutional investors who want to buy or sell large amounts of stocks without affecting the price of the underlying. 
• TWAP (Time Weighted Average Price): TWAP shares the orders equally, while ignoring the volume traded. It is useful in low-volume assets where the price may be uncertain. 

For example, if we want to buy 10,000 shares in 1 hour. In the case of VWAP, it can try to front-load more during high-volume periods. On the other hand, in the case of TWAP, the strategy will keep orders of the same size every 5 minutes.

The major reason for using these strategies is to reduce slippage and execution costs. Also, big institutions use this so that they don’t signal large trades to the market. 

Risks:

• Market conditions can differ significantly from VWAP/TWAP.
• The strategy can miss optimal prices if strictly following time/volume logic

7. Mathematical/ML-Based Models

These strategies use quantitative models combining statistics, machine learning or game theory, which try to benefit from imperfections of the market.

The way these algorithms work is that they analyse a large selection of inputs such as historical value data, volatility, correlation, order book dynamics and even alternative data (social media, satellite image, etc.). The trades are then carried out based on the output of complex models.

Since these strategies require access to huge datasets and strong mathematical capabilities, they are usually deployed by hedge funds, quant trading funds and HFT traders. The models that are used can be classified as:

• Factor model: eg Fama-French 3-factor model
• Machine learning models: Regression, Classification, Random Forest, Neural Network
• Bayesian Model, Kalman Filters and Markov Chains

Example:

• A neural network model learns from many years of stock data and predicts the possibility of a stock closing higher tomorrow. If the probability is more than 70%, the algorithm takes the buy order.
• Derivative pricing and risk management are an integral part of this strategy.

Risks:

• The strategy has high complexity. Also, if there is an error, it’s very tough to debug or explain the logic. It can also be a black box model.
• Since machine learning models are being used, there is a chance of making an overfit strategy, which might lead to poor real-world performance. 
• Strong infrastructure and clean data are required.

Essential Elements for a Successful Algorithmic Trading Strategy

It is a common misconception that creating a successful algorithmic trading strategy is just about coding the buy and sell signals. An algorithm has higher chances of being profitable if all the following elements are incorporated in the algorithm development:

1. Risk Management

"Risk comes from not knowing what you're doing." – Warren Buffett. It is of utmost importance to have risk management ingrained into the strategy. Even if there is a highly probable strategy, it can go bust if correct risk management is not followed and if the downside and black swan events are ignored.

Each trade must have a stop loss and take profit levels. The position sizing is important, and it should be based on the capital deployed, the current volatility and the risk appetite of the trader. Traders can also set maximum drawdown limits to pause or halt the strategy. Furthermore, traders can reduce risk by diversifying across strategies and instruments traded. 

2. Backtested Strategies

The beauty of algorithmic trading is that all the strategies can be rigorously backtested on historical data to evaluate performance before deploying them in live markets. Backtesting means that we get the expected profit that would have been made had we run the strategy for the last few years. There are some best practices that should be followed while backtesting a strategy.

• The backtest should be robust and avoid overfitting and look-ahead bias
• Use of “out-of-sample” data can help in checking how the backtest performs for non-trained data
• The backtest should be done over a considerable amount of time, and it should encompass different market regimes such as bearish, bullish, sideways and volatile markets.
• The backtest should include factors like slippage, transaction costs and impact cost.

Once the backtest is done, traders must analyse the backtest results by checking the CAGR, Sharpe/Sortino ratio, Calmar ratio, drawdown, accuracy and risk-to-reward ratios. 

3. Continuous Monitoring and Adaptation

Algorithmic trading does not mean deploy and forget. It is important to monitor the algorithms from time to time because the markets evolve, and so should the algorithms. Traders should monitor performance drift and check if the live performance is deviating from expectations. 

Traders should also adapt to macro and structural changes such as new regulations, volatility regimes, or sector rotations to get the most out of their algorithms. 

4. Reduced Transactional Costs

One of the major considerations often overlooked is the transaction costs of trading. Even the best alpha-generating strategy can become unprofitable when trading costs eat into margins. 

The costs of trading include the brokerage fees, exchange transaction charges, taxes, slippage (the gap between expected and actual execution price) and possible impact costs. To reduce transactional costs, algorithms can be fine-tuned to use VWAP/TWAP-based execution.

Also, the algorithmic  trades can be a LIMIT order with chase functionality in case the orders are skipped. This helps in reducing the slippages considerably.