Algorithmic trading is becoming popular these days which more financial inclusion , growing understanding about systematic trading among retail investors , brokers giving their API to handle trading operations and ease of coding through languages like python.
Algorithmic trading is a process of using a computer programs to generate buy and sell orders in the markets according to well defined set of instructions. Once the market conditions match any predetermined criteria, trading algorithms sends a buy or sell order to the broker server.
An Algorithmic trading system is a set of fixed rules that provide buy and sell signals. Algorithmic Trading System makes trading faster, automated and scalable. Further, it also helps in reducing risk of human error, backtesting strategies before implementing in live market, building customized strategies and increasing the opportunity of correct order execution .
Before creating any Algo Trading system ,its important to have set or trading rules. I call this a RULE BASED TRADING SYSTEM. Such system has strictly defined rules to cover all aspects of a trade like entry criteria, position-sizing, maximum trade risk, trade management, and exit criteria
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| Pros | Cons |
|---|---|
| Eliminates human error and emotional factor. | Traders should be involved in the programming process to develop algorithms fitting their trading strategy. |
| Allows back testing in conjunction with historical data. | Loss of total control and ability to interfere when the deal could be rejected. |
| Low maintenance due to minimal investor intervention. | Algorithms may have short life spans. |
| Unique ability to generate revenue opportunities. | Total dependency on technologies. |
Steps to Create a Trading System
Step 1: Create a Trading System
A Trading systems recieves the data from various sources and store it for back testing. Further this keep records of buy and sell order, Profit and loss with each step and order, order management etc.
There are 2 ways to build a trading system :
1. Using an existing API to build a trading system around it.
2. Use existing platform such as back testing, backtrader, lean, etc. These platforms already have all the necessary tools to backtest, connect to exchanges, manage and execute orders, and evaluate performance. These platforms can be:
- Commercial platforms – these platforms are designed for retail investors. They have friendly interfaces and all the necessary features like reports, notifications, news feeds and helpful resources for education.
- Prop platforms – custom solutions created for global brokerages and designed to perfectly meet all the customer’s needs and requirements. Thus, a brokerage firm can get a custom-built platform, fully corresponds to the business objectives.
Step 2 : Define Time Frame and Trading Frequency
Trader should be clear about which time frame should be used for generating buy and sell signals. Many strategies use inputs from multi-time frame comparisons which form an inseparable components for successful trading algo strategy. Different time frequency requires different types of strategies. Some trader specialize in HFT so focus more no volume analysis, some in statistical arbitrage and pair trading, others work on intraday timeframe whicle rest can use momentum, trends and swings for developing trading strategy. Positional traders are more concerned about supply and demand zone and generally analyse chart in 15 min to 1 day frame.
Also equally important is to not over trade. More trades will lead to increase in brokerage charges to understand how frequently your strategy trades
Step 3: Develop and Visualize Your Trading Algorithm Strategy
Development of a strategy is the most important part of a successful trading algorithm. Its creation requires tons of research, mathematical thinking, and an in-depth understanding of financial markets and assets class. Each market has its own rules. Further each strategies has a life cycle. With more and more people deploying similar strategy on their trading system , the alpha generated by strategy reduces to almost zero after some period. Therefore quants need to come up with new version of strategy with optimised parameters or new strategy altogether. Its important that traders should choose strategy which meet his psychology. A deep drawdown or High Risk to Reward ratios with low hit ratios may not be suitable for beginners. Strategies used to by trades generally fall into following categories :-
1. Arbitrage – This strategy is profitable thanks to price margins. When a dual-listed stock is bought at a lower price on one market, it can be sold at a higher price on another market at the same time. This is known as risk-free price differentials or arbitrage. With the algorithm that identifies price differentials and places orders accordingly, one can make significant profits with no risk.
2. Trend-following strategies – To follow trends in moving averages is the most widespread strategy in algorithmic trading. This strategy also monitors the channel’s breakouts and price levels. This strategy is the simplest because it does not include predictions or price forecasts. The frequency of the desirable trend dictates this kind of trading. Therefore, this strategy can be easily implemented into the algorithm. You will have a programmed algorithm based on 50- and 200-day moving averages.
3. Index Fund Rebalancing – When it comes to index funds, there are certain time intervals when re-balancing occurs. The purpose of it is to bring index fund holdings to par with their benchmark indices. Traders capitalize on expected trades offering 20 to 80 basis point profits which depend on the quantity of stocks in certain index funds right before re-balancing happens.
4. Mathematical model/Quant-Based strategies – There are proven mathematical models like, for example, delta-neutral strategy. The Delta-neutral option consists of a variety of positions with either positive or negative deltas. This ratio is a comparison of the asset’s price changes and differences in the prices of its derivative.
5. Mean Reversion or Trading Range – This stock algorithm is based on the assumption that high or low prices are temporary and that assets revert to their average value periodically. Certain algorithms can adjust buying and selling to when prices go above or
6. Volume-weighted Average Price (VWAP) – Using VWAP strategy consists of breaking up a large order into smaller ones for releasing it to the market, using stock specific historical volume profiles. It is done, so the order is executed close to the volume-weighted average price (VWAP).
7. Time Weighted Average Price (TWAP) – TWAP strategy consists of breaking up a large order into smaller ones to release it to the market, using evenly divided time intervals between start and finish. It is done to minimize market impact by placing the order closer to the average price between the start and end times on the market.
8. Percentage of Volume (POV) – This algorithm sends partial orders that are adjusted to the defined participation ratio and considers the volume traded on the markets. The so-called “steps strategy” makes orders at a percentage of the market volumes defined by the user. The algorithm either increases or decreases the participation rate, depending on whether the stock price reaches the levels defined by a user.
9. Implementation Shortfall – Implementation shortfall is the sum of the execution cost and the opportunity cost, incurred in case of adverse market movement between the time of the trading decision and order execution. Using this strategy, the aim is to keep the implementation shortfall as low as possible.This strategy increases the targeted participation rate if the stock prices move to a trader’s advantage and decreases it if the stock prices move to a trader’s disadvantage. In other words, it decreases the possibility of a trader losing if price changes during the decision time.
Other Non-Usual Trading Algorithms
There are high-tech front-running algorithms at play as well. These algorithms detect other algorithms on the side used by a sell-side market maker. Thus, traders are encouraged to use algorithm strategies not to lose to those who already use algorithm strategies to identify large order opportunities.
Every trading strategy should be identified by set of parameters
- Trading system name
- Version number
- Type of strategy
- Indicators or patterns used, with settings
- Entry Rules
- Position Sizing or Risk Management Rules
- SL Rules
- Trailing SL Rules
- Re-entry rules
Step 4: Backtest the Trading Algorithm on Historical Data
The algorithmic is tested on historical data. By using multiple time series of different assest classes, perfromance of a trading algorhtic is computed which is then compared with baseline alroithmic to chek I any alpha is generated or not. However the check the statistical importances of such performace result, further statiscal analysis must be done to ensure that the result is statitcial ly significant.
Your back testing will give you some key pieces of information:
- Draw down
- Win rate
- Profit Factor
- Losing Rate
- Consecutive Losses
- Sharp Ratio
- Calmar ratio. etc.
Step 5: Connect Algorithm To a Live Trading Account
After the profitability of the trading algo is confirmed, it’s time to trade using a live demo account – also called paper trading. The real market conditions are different as here the robot’s buy and sell orders affect the market. Keep your eyes wide open until it’s verified that the trading algorithm program is working in live conditions.
That’s it.
FAQs
What is Algorithmic Trading?
Algorithmic trading is the use of computer algorithms to automatically place trades in financial markets. This process is based on mathematical models and predetermined sets of rules, and it allows traders to take advantage of market inefficiencies and execute trades faster and more efficiently than through traditional manual trading methods.
What are the benefits of algorithmic trading?
There are many benefits to algorithmic trading, including increased efficiency, speed, and accuracy in the execution of trades. Algorithmic trading also allows traders to implement and test multiple strategies simultaneously, reducing the time and effort required to manually execute trades. Additionally, algorithmic trading helps traders identify and capitalize on market inefficiencies and opportunities more quickly, leading to greater returns.
What are the risks of algorithmic trading?
Like any investment, algorithmic trading carries certain risks. Some of the risks associated with algorithmic trading include the possibility of incorrect data, technological malfunctions, and the potential for algorithmic biases to impact the outcome of trades. Additionally, algorithmic trading can lead to high-frequency trading, which can create increased volatility in financial markets.
What should I look for in an algorithmic trading platform?
When choosing an algorithmic trading platform, it is important to consider several key factors, including ease of use, security, reliability, and access to data and tools. You should also consider the platform’s fees, as well as its customer support and training resources. Additionally, it is important to choose a platform that is customizable, allowing you to adjust your algorithmic trading strategies as needed.
How do I start algorithmic trading?
Getting started with algorithmic trading requires a certain level of technical expertise, as well as a thorough understanding of financial markets and trading strategies. Before starting, it is important to conduct research, familiarize yourself with the basics of algorithmic trading, and choose a reputable and user-friendly algorithmic trading platform. Additionally, it is important to familiarize yourself with the regulatory landscape surrounding algorithmic trading and understand the potential risks involved.
In conclusion, algorithmic trading platforms are a useful tool for traders who want to automate their trades and take advantage of market inefficiencies. There are a variety of options available in the U.S. market, each with its own unique features and benefits. When choosing a platform, it is important to consider your trading style, experience level, and risk tolerance. Whether you’re a beginner or an experienced trader, you can find a platform that fits your needs. The platforms mentioned in this article are all great options to consider. By taking the time to evaluate these platforms and choose the right one for you, you can improve your trading results and achieve greater success in the market.
