I'm talking about people who write their own software rather than working for a company that does it professionally.
I'd be interested to hear any tips or pointers on what strategies you've found that work.
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.
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.
If your algotrading profits depend on market being bull or bear, you're doing it wrong. Volatility matters, not the direction.
Mine work in either market. But I trade on the millisecond lines to avoid market bias. And yes volatility is key. As if confirmation of this approach I see Binance has just recently introduced a 1s chart (effectively 1000ms).
However most retail algo traders using indicators use larger time frames that are more susceptible to market trendiness.
And if you'd bought pets.com, or webvan, you'd be far behind. Survivor bias at work.
https://en.wikipedia.org/wiki/Registered_retirement_savings_...
So yes, I made a 3-5% profit over roughly 18 months, but it took a lot of time, stress, and in the end I ended up with less money.
Is your experience the same?
Success rate for newly formed hedge funds is probably something like 50% over a 5 year horizon. For solo traders, it's probably closer to 1%.
There is WAY too much stuff you need to be on top of, if you want to be successful, than a single person can manage. It's about as feasible as starting a one man (coal) mining operation, where you must dig the tunnel, extract the coal, process it, sell it, manage the business operation and retain your sanity. You need at least a handful of people to be effective, all with different specialties.
...not to mention that if you're successful as a solo trader, you could be making 10x more with proper financial backing. If your strategy is returning 25% annually on a personal 1 mil investment, there's a good chance it'll still be returning >20% on a 10 mil investment (once you get to 100 mil or into the billions, your returns start to diminish due to market impact). Find a good backer, they'll literally give you money and a support team for a piece of the profit.
I'm not doing any order book or HFT stuff. It's mostly mean reversion < 15 minute trades. I have a considerable amount of options trading experience and that's what my main strategy revolves around. I don't have a killer strategy that some of these algorithmic firms are capable of, but the current one is promising. I also have a fairly rudimentary form of backtesting, which is a Jupyter Notebook crunching data I've collected over the years. I use some ML python libraries as a bonus, but I haven't created any strong enough attributes yet to achieve a high amount of predictive accuracy in that sense. My strategies mostly revolve around simple statistics.
My stack is a Linux VM with MySQL, running a suite of python scripts that collect data in real time and make the automated trades through TD Ameritrade's API.
By the way, I wouldn't consider myself smart or gifted - my strength relies solely on my determination. If you were to ask me if it's worth it, I would say "mostly." At the very least I've come away with a much higher understanding of the equity/option markets, and I know much more about MySQL, Python and data mining. At this point though I'd take a job with an algorithmic firm in a heartbeat.
No, citadel securities just had a record year internalizing retail flow.
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.