Long story short: I created an option backtester (MesoSim) and started analyzing public domain trades.
I have a couple of listed in this blog: https://blog.deltaray.io/tags/strategies/
If you are interested I'd suggest taking a look at the Weekend Effect, it's relatively easy to understand.
Best luck to you getting back to trading!
"Automated" trading, even if there is an edge (and unless you are a crook, there is not) will sink you with transaction costs
You should know this.
[0] FinGPT: Open-Source Financial Large Language Models https://github.com/AI4Finance-Foundation/FinGPT
[1] Zipline is a Pythonic algorithmic trading library https://github.com/quantopian/zipline
If you want equities, I think you can get that from Databento for low cost (won't need the CME cert for their stuff to get historical from here).
Then just write the code. The basic structure is not that much in a modern language (use Java, Rust). To trade, you can use Sequencer architecture as basic https://sissoftwarefactory.com/blog/an-introduction-to-the-s... (article describes so maybe jog your memory, but has only old code link so useless if not new to you, just sequence your messages) ChatGPT will help build this quick.
IMHO if you want to do this, crypto markets are ideal. Fees are high, but low upfront costs. Can eke out small wins with low capacity strategy.
Should be advised, though. Everyone who wants to get into this does this stuff and then finds that if you want to do big, you need to apply large force on long lever. So everyone and their uncle had "a few profitable strategies as a side bet". Means nothing at small scale. Only for fun.
Affordable data, especially tick level L3 data at scale is difficult to get. Having enough capital to make trading worth well (pricing in losses etc.) is also difficult. Direct market access etc. all cost money. Use interactive brokers if you need a compromise between fully professional and amateur. Spend more time on your financial models, and less time building beautiful frameworks. The engineering of quant finance is the least interesting part. Focus on the market microstructure and financial models. If your model is bad, you lose money regardless of how great your infra is.
It's difficult to find a good off the shelf backtester. This ties into your model too. At its core, a backtester is just a for loop. Computation of variables like slippage and integration of monte Carlo Sims is where the secret sauce lies. Those are all proprietary.
Best of luck in seeking alpha
There's a guy here explaining how to make trading bots in Python https://youtu.be/sUQmuL95_oY He has free code available on github to try out. He also explains how to backtest. There are a couple of popular packages like backtesting.py out there.
ccxt is a handy python package if you want to trade crypto. There are various free tutorials for it.
I also recommend playing around with Tradingview. It draws graphs and you can make your own indicators with their language Pinescript and even trade off that although Python probably gives you more flexibility.
Udemy courses https://www.udemy.com/course/algorithmic-trading-strategies-... and https://www.udemy.com/course/complete-algorthmic-forex-tradi... I paid about £14 each - they keep chopping and changing with the discounts
The details vary a bit depending what you want to trade - crypto, stocks fx or whatever.
One tip - chatgpt4 is surprisingly helpful and can almost code a whole bot for you if you know what you want. One slight pain for me has been finding brokers with the what I want. I'm the wrong nationality or they don't have futures or there isn't enough liquidity or they are sketchy or such like.
Hop on FinTwit or CryptoTwit.
Derivatives (options/futures) are your best bang:buck. But crypto is where the real speculation is.
Read up on a practical trading book (hypothesis building, backtesting without bias, and risk management via Kelly etc.) and then start testing strategies.
Backtesting you can build yourself with any broker that has an API, but not all are made equal. It’s dead simple.
Look into [redacted]
I ended up in twitter/reddit rabbit holes until I found a somewhat dodgy guy on twitter (@TerribleQuant - account is now deleted).
The person compiled a guide with study resources, courses, YT videos, podcasts, textbooks and everything else you can think of in 21 pages.
If you look for: BBM PUBLISHING INC “Roadmap” Resource Guide 3rd Edition you might be able to find a copy.
Its a platform where you just focus on implementing strategy. Was fun but I realised it would be more work than just getting a better job.
I'm sure there are others as well.
for quant: https://community.quantopian.com/home https://pyquantnews.com https://www.quantscience.io
for backtesting - zipline reloaded - is event based. It is very slow for optimization.
vectorbt - vector optimization - optimize the parameters. optimize - entry and exit parameters, number of ticks. useful for single-asset or spread strategies (i.e. refiner crack spread).
I’d say this: It is very hard to beat the market consistently. It is even harder to statistically prove and convince yourself that our new strategy actually now beats the market. There are a lot of gotchas and caveats to watch out for when backtesting.
I spent most of my time with time series techniques as this was most fun to me. My current stack is ccxt, binance, polygon.io and self made backtesting in python.
I think it can be a fun project to make an engine, and try to make models, together with some form of ingestion pipeline and finally solve and calibrate the models. Try some simple products, and see how close you can get to real world prices.
Making pricing/risk engines are in my opinion somewhat close to game engines even through that the domain is so different.