“Risk comes from not knowing what you are doing.”
To create a trading strategy, all you need is to have a trading idea. Then, you need to write an algorithm describing your idea as well as the conditions for execution. Once the algorithm is complete, your strategy is ready. Still, you must ensure that your strategy is workable and you would not lose much if it goes haywire. Backtesting the strategy and paper-trading before going live is a good method to check for discrepancies in your strategy.
Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. The following are a few of the common strategies used in algo-trading:
1. Trend Following Strategies or Momentum Investing
These strategies are one of the most simple yet effective strategies. One of the easiest strategies to implement through algorithmic trading because these do not involve any predictions. Once a share starts following a trend, the algorithm assumes that the momentum will continue indefinitely until there are specific signals or indicators otherwise.
2. Arbitrage Opportunities
Arbitrage is simply the practice to take advantage of occasional small market price discrepancies that arise in the market price of a security that is traded on two different exchanges. For an arbitrage to occur, it must meet three conditions. First, the same assets should not trade at the same price on all markets. Second, two assets with the same cash flows should not trade at the same price. Lastly, an asset with a known price in the future should not trade today at the future price, discounted at the risk-free interest rate.
Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit. This can be replicated for stocks vs futures instruments when price differentials exist.
3. Mean Reversion Technique
Mean reversion techniques calculate the temporary average low and high of an asset. When the price of a stock lags behind the average, the stock is considered attractive, hoping that the price will increase. On the other hand, should the stock rise beyond the average, the stock is considered undesirable, as the code will be expecting the price to fall. Identifying and defining a price range on an algorithm for trades to be placed automatically when the price of the asset breaks out of its defined range.
4. Index Fund Rebalancing
All index funds have a defined period of rebalancing to bring their assets up to par with their benchmark indices. When an account is heavily invested into an index fund, this creates profitable opportunities for algorithmic traders, who capitalize on the expected trades of rebalancing. Such trades are executed algorithmically for timely executions at the best prices.
5. Delta-neutral Strategy
A portfolio strategy comprising of multiple positions with offsetting positive and negative deltas – a ratio comparing the change in the asset price to the corresponding change in the derivative price, such that overall delta of assets is zero
6. Weighted Average Price
This strategy involves breaking up a large order and releasing dynamically determined smaller chunks of the order using stock-specific volumes or deviated time slots between the start and end time. The aim is to execute the order closer to the average price, be it Volume-Weighted average or Time-weighted average.
These strategies are some of the most common strategies that are being used by traders around the globe. Knowing when to use the right strategy is more important than using any strategy whenever you like. Trading is more safe when played under a calculated risk setting.