u/Public_Law_9996

Hi everyone,

sharing a 24-hour forward projection (starting 16:30 UTC).

The current model output is still leaning bullish overall.

This is a continuation of yesterday’s structure, although the previous forecast did not fully play out as expected — something the model accounts for in its ongoing calibration.

For context, the approach is based on decomposing price action into dominant cyclical components and then recombining them into a single forward projection.

At the moment, the structure is composed of:

• Higher-frequency cycles (2h) → contributing mainly to intraday noise and short-term volatility
• Lower-frequency cycles (8.3h, 10.6h, 13.4h) → driving the broader directional structure

In the current synthesis, the mid/low-frequency components are approaching a local peak region around midnight, which is reflected in the projected curve.

The attached chart shows the aggregated 24-hour cycle output.

Feedback or critique is welcome — especially regarding interpretation or robustness.

Hi everyone,

I’ve been thinking about the applicability of Markowitz’s Modern Portfolio Theory (MPT) in the context of day trading. As far as I understand, MPT is primarily used for long-term portfolio allocation and diversification.

My question is: have you ever encountered practical use cases where Markowitz optimization is applied to intraday trading strategies?

Can concepts like efficient frontier or risk-return optimization be meaningfully adapted to short-term or even ultra-short-term trading, or does the theory break down at that time scale?

Curious to hear your thoughts and any real-world experiences.

Hi Traders, 

I’ve been working on a quantitative forecasting model for crypto (BTC, ETH), and I’m planning to bring it live soon. Before that, I’d like to get some feedback from the community. 

The core idea: 

This model applies wavelet decomposition to price data and uses that structure to generate forward price predictions. 

Instead of just predicting direction, it reconstructs an expected price path across a full horizon — from ~60 minutes up to 24 hours ahead. Each update produces a forward curve rather than a single point estimate. 

In other words: 

• It captures multi-scale structure in the data (via wavelets) 
• Projects that structure forward into the near future

 

Starting today, I will try to publish a new 24-hour forecast every morning, Central European time. 

The first one is below 

https://preview.redd.it/1t3e2u1m4pvg1.png?width=1850&format=png&auto=webp&s=7d4222fc171711411b7c37ec420489a4ad616401

Please also check the backtest results for BTC. 

The backtest was conducted in two modes: 

Mode 1: No fees (raw signal evaluation) 

Mode 2: With fees (0.05%), but only signals with strength > 1.013 are executed 

Evaluation metrics: 

• Average directional accuracy across the full forecast horizon 
• Pointwise cumulative directional accuracy (for each forecast step) 
• Returns from simple long/short strategies

 

Strategy setup: 

• Long strategy initialized with $100 
• Short strategy with leverage 0.001 BTC

 

Results 

All timestamps are in UTC. 

The results of Mode 1 (no fees): 

https://preview.redd.it/o9zcbfac4pvg1.png?width=1080&format=png&auto=webp&s=0130bd766f6ae7f24af97cdb8800dd3d270127eb

• Average directional accuracy approaches 0.518 toward the end of the backtest 
• Very positive return expectations.

 

The results of Mode 2 (with fees + filtering): 

https://preview.redd.it/q9b4xeac4pvg1.png?width=1080&format=png&auto=webp&s=8a0d7872c908990edddb714ed6433b735a4bddd3

• Average directional accuracy 0.513 
• Pointwise cumulative accuracy: 
• Higher on shorter horizons 
• Lower on longer horizons

 

However: 

Even though longer horizons degrade in directional accuracy, the corresponding strategy returns are mostly not negative. This suggests the model still captures meaningful price movements, even when direction is harder to predict. 

https://preview.redd.it/i9l3ydac4pvg1.png?width=1080&format=png&auto=webp&s=8c053b44675305cfd6cc238787e792a4f9e313f6

• Return expectations are clearly significantly lower compared to Mode 1, but it’s still fair to say that the strategy survives the fees.

 

Conclusion: 

The model captures a small but statistically consistent edge. As expected in a near-random market, performance depends heavily on execution and filtering - but the signal itself appears to contain usable structure. 

 

Would this type of model be useful to you? 

Any feedback (technical or practical) would be greatly appreciated. 

 

Disclaimer: 

This content is for informational purposes only and does not constitute financial advice. Trading involves risk, and past performance does not guarantee future results.