Close price prediction
RMSE - Unacceptable Error: The RMSE (root mean square error) of the model is 0.06.
R2 - Unacceptable Model: The R-squared statistic is unable to explain the variation in the test dataset. The model exhibits low reliability.
Simple strategy: based on prediction and real price
· +1 if predicted price > real price\ · -1 if predicted price <= real price
This is a strategy that simply uses the predicted price and compares it with the real price, and there is always a position.
Number of trades: 2. Here are the first:
More complex strategy:
Based on mean reversion after exceeding "s" std upwards or downwards (when s=2 it's the Bollinger Bands)
Note: It would be interesting to run a stationarity test (not to be confused with seasonality) for mean reversion (we have the codes, it won't take more than a couple of hours to update it). This way, the assumption of mean reversion would have more scientific support and increase the level of causality and theorization.
Theory: The underlying theory, the basis of causality, is that the spread between the predicted and actual price exhibits mean reversion. This seems clear, although it needs to be tested. The question is whether this mean reversion of the spread is reflected in real price reversals. In that case, we are detecting tops and bottoms through LSTM.
This is a mean reversion strategy based on the spread between predicted and actual prices, which demonstrates mean reversion behavior. The question is whether this mean reversion in the spread is reflected in price reversals. In that case, we are using LSTM to detect price tops and bottoms.
Number of trades: 39. Here are the first ones: