In a previous article, the fundamentals of algorithmic trading and initial steps to get started were discussed. This article will build on that foundation by exploring the various strategies employed by algorithmic traders in their trading setups.
Learn more about the Basics of Algorithm Trading here
Different Algorithm Trading Strategies Implemented
To be effective, any algorithmic trading strategy must identify a profitable opportunity that enhances earnings or reduces costs. Here are some commonly used strategies in algorithmic trading:
Trend-Following Strategies
Trend-following strategies are among the most commonly used in algorithmic trading. These strategies focus on identifying trends using moving averages, channel breakouts, price levels, and other technical indicators. They are straightforward to implement because they do not require predictions or forecasts. Instead, trades are executed based on observable trends, making them relatively simple for algorithms to manage. A well-known example of a trend-following strategy involves using 50-day and 200-day moving averages.
Arbitrage Opportunities
Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another, capturing the price difference as a risk-free profit. This strategy can also be applied to discrepancies between stocks and futures instruments when such price differentials arise. By using an algorithm to detect these price differences and execute trades efficiently, traders can capitalize on these profitable opportunities.
Index Fund Rebalancing
Index funds periodically rebalance their holdings to align with their benchmark indices, creating opportunities for algorithmic traders. These traders can exploit anticipated trades, depending on the number of stocks in the index fund before rebalancing. Algorithmic trading systems execute these trades efficiently to secure optimal prices.
Mathematical Model-Based Strategies
Mathematical models, such as the delta-neutral trading strategy, facilitate trading by combining options with their underlying securities. Delta-neutral strategies involve creating a portfolio with offsetting positive and negative deltas—a measure of how the price of an asset changes in relation to its derivative—so that the overall delta of the portfolio totals zero.
Trading Range (Mean Reversion)
The Volume-Weighted Average Price (VWAP) strategy involves breaking a large order into smaller, dynamically determined parts and releasing these portions into the market based on historical volume profiles specific to the stock. The goal is to execute the order as close as possible to the VWAP, minimizing market impact.
Volume-Weighted Average Price (VWAP)
Volume-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock-specific historical volume profiles. The aim is to execute the order close to the volume-weighted average price (VWAP).
Time Weighted Average Price (TWAP)
The Time-Weighted Average Price (TWAP) strategy involves dividing a large order into smaller segments and releasing them into the market at evenly spaced intervals between the start and end times. The objective is to execute the order at a price close to the average price over the specified time period, thereby reducing market impact.
Percentage of Volume (POV)
This algorithm breaks down a trade order into partial orders, sending them according to a specified participation ratio based on the volume traded in the market. The “steps strategy” aspect of POV adjusts the participation rate by sending orders at a user-defined percentage of market volume and modifying this rate as the stock price reaches predetermined levels.
Implementation Shortfall
The implementation shortfall strategy seeks to minimize the total cost of executing an order by balancing the trade-off between immediate market execution costs and the opportunity cost of delayed execution. The strategy adjusts the participation rate dynamically: increasing it when the stock price moves favorably and decreasing it when the price moves unfavorably.
The Bottom Line
Algorithmic trading combines computer software and financial markets to automatically open and close trades based on programmed instructions. Traders can set specific criteria for when they want trades executed and can use computing power for high-frequency trading.
With numerous strategies available, algorithmic trading is widely used in today’s financial markets. To embark on this journey, you'll need the right computer hardware, programming expertise, and a deep understanding of financial markets.
Equally crucial is selecting a reliable broker like Orient Futures Singapore, who can offer the necessary infrastructure for successful trading. A trusted broker ensures you have access to essential tools, data feeds, and platform access to effectively deploy your algorithmic strategies.
Start Trading with Orient Futures Singapore
Being an Overseas Intermediary of Shanghai International Energy Exchange (INE), Dalian Commodity Exchange (DCE), and Zhengzhou Commodity Exchange (ZCE), when foreign clients participate in internationalised futures contracts in these Chinese markets with us, they have direct access to trading, clearing, and settlement. Our parent company, Shanghai Orient Futures, is the largest broker in terms of aggregated volume across the five regulated exchanges in China.
Orient Futures Singapore also currently holds memberships at the Singapore Exchange (SGX), Asia Pacific Exchange (APEX), and ICE Futures Singapore (ICE SG). Starting August 2023, corporate clients can also gain access to the B3 Exchange through us, opening additional trading avenues.
Expect streamlined processes and an easy-to-use interface designed for minimal latency, accompanied by our team's round-the-clock availability on trading days to provide assistance for all your trading needs.
