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.
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:
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.
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:
Used in: stock, ETF, objects and currencies. This works especially well in the range-bound markets.
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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:
Example:
Used in: Equity, futures, cryptocurrency, foreign currency.
Risks:
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.
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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.
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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.
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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:
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.
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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:
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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:
"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.
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.
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.
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.
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.