Walk Forward Optimization with VectorBT and Alpaca

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In this video, we discuss Walk Forward Optimization. We use VectorBT + Alpaca to demonstrate.

[@parttimelarry]

If you like this video and want to support the channel: Buy Me a Drink: www.buymeacoffee.com/parttimelarry Also, I will be starting a spinoff channel on AI in music, art, and gaming in 2023. Subscribe at: youtube.com/@parttimeai Source Code: github.com/hackingthemarkets/walk-forward-optimization

[@lucidwavs4004]

You're the man! More VectorBT vids please.

[@prabhu_patil]

Thanks larry.. Glad to see you back with excellent content .. This really helps in analysing my stategy settings.

[@izem9652]

Man you're a life savior; I was struggling understanding well how to do walk-forward optimization with vektorBT (indeed, the notebook in the doc isn't well commented), it is much clearer now. Greetings from France !

[@ashkanhesaraki]

Please do more on VectorBT, specially optimization and TP/SL exits

[@somajuice666]

YES exactly what was needed Larry!

[@Userjdanon]

Cool! I have never heard of walk forward. But Ive implemented myself some kind of similar testing method. It's kind of similar to cross validation just for time series :) thanks for the video

[@J2897Tutorials]

Just a minor error. Probably worth mentioning though... future_data = vbt.AlpacaData.download('SPY', start='2021-06-01', timeframe='1d', limit=10000).get('Close') _APIError: your subscription does not permit querying data from the past 15 minutes_

[@juanpablorocha5609]

Great video brother!

[@carters6138]

🔥🔥🔥🔥🔥❤️❤️

[@kevinalexis9886]

WalkForwardOptimization implementation begins - 24:00

[@turnbull82]

train test split instead? Seems a bit squirrely

[@brianrowe1152]

Great but how do you write a real strategy?? Or use a real strategy function that takes a DF and returns ‘buy’. And then a real sell formula?? Is that a future video ?? Please :)

[@andrewlovesbry]

Do you have a guide for setting up VS Code?

[@petrzurek5713]

Hey Larry, wasn’t there another video of Vectorbt and Alpaca? Seems like it disappeared, can’t find it anymore …

[@guttimgify]

Super helpful content! Do you have other methodology of back testing to recommend? I have thought about changing the 70/30 to something like 60/40 or smaller or 60 to 20 with a skip in between. I'm trying to come up with other methods of back testing to see how well it will perform.

[@bufirolas]

Hello Larry! Again, thanks for another great, clearly explained video. I was wondering if you could think of a way to have a single, continuous, optimised performance result, instead of individual metrics for every single optimised chunk (20 in this case). I was trying to look for the trade logs in vbt and override it with a concatenated version of it but I cannot seem to find the method to access them anymore.

[@lorenzourera4223]

How can I do a walk-forward backtest without the optimization? I'd like to see how my strategy would have performed month on month if I had kept my parameters fixed.

[@brandonjohnson8880]

In thos case you don't need to do walk forward. If you fix your parameters you just need to run your backtest once over the whole period.

[@dservais1]

Strange I can only install version 0.14.4 : λ pip install -U vectorbt==0.23.2 ERROR: Could not find a version that satisfies the requirement vectorbt==0.23.2 (from versions: 0.10, 0.11, 0.12, 0.13, 0.13.1, 0.13.2, 0.13.3, 0.13.4, 0.13.5, 0.13.6, 0.13.7, 0.14, 0.14.1, 0.14.2, 0.14.3, 0.14.4) ERROR: No matching distribution found for vectorbt==0.23.2

[@parttimelarry]

Are you using Python 3.7+? When I install a package I use pip3 in a Python 3.8 virtual environment. Some systems have pip for Python 2.7, just checking.

[@dservais1]

@@parttimelarry I'm using 3.10.2 on windows 10.

[@emblazed2579]

​@@dservais1 I experienced the same issue as you. Vectorbt currently doesn't support python 3.10. Downgrading to python 3.9 resolved the issue, and vectorbt installed version 0.23.2.

[@dservais1]

@@emblazed2579 ​ ​ @Part Time Larry I had to downgrade from Python 3.10.2 to 3.8.10 to make it working. Thanks for the videos Larry, and thanks to Emblazed for the trick.

[@parttimelarry]

@@emblazed2579 Thanks for helping out here!

[@urgebharadwaj9081]

Hello, larry your videos are very informative and easy to understand!😊♥️. please make a video on metatrader 5 expert advisor...

[@TheDjarEl]

Trying the same code on 1H data for 100 days for BTC. Takes 14GB of RAM to run.... For only 2 parameters to optimise. I can do the same in a C/C++ loop will take like 50 MB of RAM... Vector BT is over engineered, really.

[@parttimelarry]

Thanks for the comments!

[@TheDjarEl]

@@parttimelarry but thanks for the video and explanations it is a very nice work you are doing ! :)

[@olegpolakow7286]

I just measured peak memory usage of modeling 100 days of hourly price data for the code above (which yields 252 rows × 15600 columns btw) - 369.6 MB. Also note that vectorbt supports multiple modes, one of them allows you to run everything in Numba, just like in C/C++. If you run into memory issues feel free to post a GitHub issue and I'll take a look :)

[@TheDjarEl]

@@olegpolakow7286 thanks 👍. I am running on Jupiter notebook on Ubuntu, maybe that takes much more memory.

[@parttimelarry]

@@olegpolakow7286 Thanks so much for the library and the notebooks and for replying here.

[@gokhanfidan4636]

hi larry. thanks for amazing works. you are great. also i didn't catch the last point of video. We found the best index of what ? i mean the point is to find those that give good results in real life among the data I tested. but you suddenly stopped. what did you find with "performance[performance.groupby('split_idx').idxmax()].index" ?? and what should i do next ? don't wory i am not going to take it as a financial advice :))

[@letsgobrandon2044]

I’m a follower and love your content and I appreciate your time. This video was difficult to stay with because you talk fast type fast and mouse is all over the screen. I came away with a near headache and frustration. To say the least.

[@parttimelarry]

Thanks for the feedback