How to build winning algorithm trading system
What is an algo trading system?
By coding a trading system into an algo, we can backtest a trading system through available historical data and real-time data to see if it is a viable trading strategy in reduced time.
Most manual traders have been trading an ineffective trading system over a long period of time without realizing it and without the ability to correct nor re-fined it.
An algo follows a defined set of instructions based on timing, price, quantity or any mathematical model to make trading systematic by ruling out the impact of human emotions or inconsistency due to deviations on trading activities.
Whats the goal of backtesting?
The goal of backtesting is to provide data evidence that the strategy identified or trading hypothesis is profitable in pips expectancy when applied to both historical and out-of-sample data.
When a trading system is now an algo, we can explode an edge based on quantitative data and segment all possible testing boundary into user-parameter for hypothesis testing as well as through system optimization by using what we called step-testing to effectively find the possibilities that weren’t uncovered through hypothesis testing.
The backtesting approach
Backtest is not only to find profitable and usable trading settings, and that’s one of the key reason why most manual traders or algo traders failed at when using the automated trading system.
The main objective of back-testing is to truly identify the strength and weaknesses of the trading system when pairing with different currency pairs as every pair has different characteristics and behavior especially in the ever-changing market conditions attributed by macroeconomic fundamentals. From there, we are able to understand how currency pairs react at different timing, volatility and which parameters should the currency pair be trading at to achieve optimal and realistic performance over a long period of times and a big number of trades.
For most, the algo trading system should work but because the operator is searching for a holy grail, it will eventually head for failure due to unrealistic testing and skidded equity curve on the back-test. And importantly, if a trading system has low pips expectancy or scalping trades. The backtest will not be reliable as the real market conditions, slippage and real-tick data movements are not really accounted for and an FX Broker and its technology bridges play an important role for any scalping system to really work.
How to use mined relevant data using an indicator algo
An indicator-algo can also pull relevant data that can be tracked and downloaded in the shortest possible time and then re-assemble them for data science and analysis as compared to the manual trading journal that will be very slow as the machine can quickly collect and my data more accurately and precise compared to a human. With that, we are able to understand the market conditions through the quantitative and statistical model approach to exploit the highest probability of any occurrences which can greatly improve the efficiency of an algo trading system.
What does a trading signal filter do?
A trading signal filter is a logical restriction that enables appropriate filter and should improve the profitability of the Algo with a realistic approach without cutting too many of its trades while back-testing the results against its original results, the benefit of this filter allows us to test if our hypothesis and data tracking is relevant and can be turn off if it does not prove otherwise.
Dynamic AI Trading Manager to replace human’s intervention
A dynamic trading manager that can be created as an AI with machine learning capabilities and further tracking abilities that encompasses risk management, capital allocation, leverage control, execution mechanism in terms of speed, latency, and slippage and other technical or technological factor that can be detected and responded in real-time. The dynamic trading manager or AI is able to make scenario-based decisions by making decisions based on the best possible come through statistical confidence.
Lastly, the system can always intervene and supervise by the trader himself, which gives an extra discretionary overlay.
The article is shared by Xeconds Strategy – A result-driven trading lab at the intersection of trading + algorithm