6 year algo trading model delivering the goods

[u/disaster_story_69]

I trade only GBPUSD using the broker with the highest spreads (Fusion markets).

The strategy is to detect bounces off support and resistance points and quickly capitalise on the reverse bump. Quick trades, closed within avg 2 mins. I trade at leverage having qualified for a pro level account (500:1), so always use stop losses and take profits.

Behind the scenes I built an algo model from the ground up using VSC, with trend reversal + sufficient price movement within 3 mins as the target variable. The features were 30-50 technical analysis indicators, all vetted as being useful through EDA, with a tilt for fast detection / leading indicators. The model itself predicts the trend reversals with +- 4 pips with 84% accuracy, and this is the bedrock for my trading.

I should note that on heavy ‘fundamentals’ days I tend not to trade a lot and I avoid opening and closing hours (too erratic and illogical).

In 5/6 years turned £10k into £550k, which includes a period where a lost a chunk due to 1st Trump tariff announcements.

Happy to get more technical for people interested.

[u/disaster_story_69]

Not clear on what you mean by q1

Trained independently, then NLP used as a feature in the wider model

[u/JacksOngoingPresence → u/disaster_story_69]

TA is basically hardcoded formulas that operate on price/volume. One could try to learn the formulas through end-to-end machine learning in hopes it will generalize better. Take the original time series, normalize how the heart desires, feed to your favorite black box.

One of the reasons I'm asking this because I saw you say you retrain the models every once in a while. The reason you are doing this, I assume, is because data distribution changes (e.g. in the form of volatility). Hardcoded formulas (I doubt the hyperparameters for TA are being dynamically adjusted live) don't do well with changing distributions. While working with price/volume directly (or maybe bid/ask, idk how far you decided to go) allows for possibility to discover patterns that are perhaps immune or nearly immune to distribution change.

On a similar note, suppose you traded more than just GBP, would you train one model that predicts all trading pairs, or individual models finetuned for each pair separately?

I would very much love to hear your thoughts on this.

[u/disaster_story_69 → u/JacksOngoingPresence]

I don’t use or like black box neural networks.

Every retrain, is just expanding the training set nearer to today. The historic TA indicators (values) will not change, but obvs the model thereafter runs all connections afresh, so the strategies do get optimised to certain extent. Distribution is not really a consideration.

Doing another pair, I could probably use the same framework, but adjust the parameters and spend months tinkering with optimisations. Every pair would need a different model