Thank you for joining the discussion @phamhung3589, and thanks much for sharing your further thoughts @t-hossein!
In addition to what has been said, I was also thinking that many of the feature classes (price data, technical indicators, statistics) shouldn’t only be calculated for price, but also for the returns themselves. Given that the target variable is typically log-returns in price prediction topics, it is probably important to calculate RSI, MACD, OBV, Bollinger, stochastic oscillator, ATR, ADX, CCI, and all kinds of MAs for log-returns in addition to those for price. This isn’t a very hard engineering step and might yield stronger signal.
Based on the many ideas that are floating around in this thread now, would it make sense to prioritise and/or split off tasks for quantitative testing?
If I summarise the above, I see the following initiatives:
- Time granularity (5m vs 24h)
- Including force and energy features (i.e. multi-timeframe
Δ[close-open]/Δt,[close-open]**2, difference from MA, multi-timeframe linear gradients) - Including external price drivers (e.g. spot XAUUSD, DXY, 10y yield, GLD ETF flows – obviously these are specific to gold and not always generalisable to other topics, except maybe BTC?)
- Including labelled time-of-day and real-valued time-of-week
- Performing feature reduction
- Modifying the training evaluation metric to match the [ZPTAE loss function]
- Add returns-focused feature set (all quantities you can calculate for price, but for log-returns)
(Losses in returns prediction topics - #8 by joel)
For ease of prioritisation, let’s do a poll on what we think are the high ROI things to test first (max 3 votes/person):
- Time granularity
- Force & energy features
- External price drivers
- Improved time-of-day & time-of-week
- Feature reduction
- ZPTAE evaluation metric
- Returns-focused feature set
Let’s make it run for 24h after this post so that we don’t slow down too much here. Great stuff everyone!