Algorithmic Trading: A Practitioner’s Guide

It works, but do not expect huge returns.

I have worked on multiple strategies. - Strategies using technical indicators do work, but you have to reasonable. If you find these giving higher than expected returns, or too many consecutive wins - take the money. Stop live trading and continue dummy trading - eventually there is a point where you can start live trading again. The thresholds will be determined from backtests.

- Statistical strategies work for swing trading. However, these are very difficult to figure out. Need a lot of data (for backtesting). These work consistently over a longer period and might look loss making over a small period.

- Scalping within 5-10 minutes works pretty consistently, signals based on options data.

- Understand and realise the law of large numbers, and use that to your advantage.

- Give importance to understanding the concept of Time. There is a whole lot of weird and scammy pseudo-science around it. Therefore do not blindly rely on one theory.

Algorithmic trading here means more about how to "execute trades" thus reduce the "execution cost" of a trade, rather than using quantitative method to gain advantage (alpha). They are usually employed by firms executing large trades, no mather whether the trade comes from a traditional trading form, or a quantitative trading firm. There are definitely overlaps, especially for HFTs regarding market microstructure, but in HTFs case, the market microstructure is built-in the HFT strategy itself, while for algorithmic trading can be decoupled as execution from the actual trading strategy that produces the majority of the "alpha"s.

I started off doing it on my own back in 2008 with nothing more than an Interactive Brokers account. Then I went all in and started a company to do it professionally around 2012.

As far as strategies go, I've said it here before, but all my strategies are quite simple and straight forward. The difficulty is almost always the execution. Almost all of my strategies are arbitrage or market making, and some of them trade off of events, like earnings, interest rate announcements, the weekly petroleum status report, and things you'd find on an economic calendar.

None of my strategies are speculative, that is I have no idea what company will perform well over a long period of time and in fact in many cases I don't even know what the company I'm trading even does. I also don't make use of any so called technical indicators, like relative strength, or fibonacci this/fibonacci that.

For the vast majority of my activity, my algos only enter into a trade if it's guaranteed to make money.

To give a vague idea of what the process looks like, every strategy starts from the premise that the market is perfectly efficient and there is no opportunity to make money. We then construct a model of what a perfectly efficient market should look like. We then backtest this model to determine whether the market is actually perfectly efficient. In most cases it will turn out to be either very close to efficient, or inefficient but in a way that cannot be profited from after taking into account fees, latency, and other factors. But sometimes you find areas where the model predicts certain behavior and the backtesting shows that the real world doesn't follow that model. In that case we then proceed to investigate very carefully what is going on. Did we make a faulty assumption? Is our model not seeing the whole picture so that there are factors we did not take into account? This happens A LOT and in fact we are very skeptical when our backtesting deviates from our model. But if after scrutiny we find no reason to doubt out model, then go from backtesting to running it in a simulator, and then from simulator to running it live on a very small scale, and then over time increasing the scale.

Our backtesting and simulating is incredibly sophisticated and precise. Almost all publicly available backtesters just look at trade activity or very low resolution data, and never accounts for things like market impact. Our backtester takes into account individual orders and simulates the entire order book, taking into account where our order would be placed within the order book. We also simulate potential market impact as well, which requires us to run simulations in parallel with different potential market impacts so we can see what the worst case scenarios are and there's a host of other factors a good simulator and backtester will take into account.

I do similar stuff and confirm most of the above. It matches my experiences.

The only other thing I'd say is that if you're in a bull market you may have easy wins. Then after -- when the market turns against you -- these will evaporate.

So long term you have to ask yourself if you are REALLY beating the market. If you'd bought Amazon or Microsoft instead, over 20 years you'd probably be far ahead.

Maybe, maybe not.

Maybe Apple will come up wih the next revolution in human-machine interface. Who knows?