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Current Task:
- Display Live Streaming Data from IG and aggregate to historical data
- make qtplot accept jupyter data
Current Bug:
streaming data saved some very weird residual? i.e.
,time,Open,High,Low,Close
0,2022-01-04 10:56:00,1.1296300000000001,1.12964,1.12952,1.1295250000000001
1,2022-01-04 10:57:00,1.1295350000000002,1.12962,1.12947,1.12948
2,2022-01-04 10:58:00,1.12949,1.12962,1.12946,1.12948
3,2022-01-04 10:59:00,1.12949,1.1295549999999999,1.1294650000000002,1.1294650000000002
4,2022-01-04 11:00:00,1.129475,1.129505,1.129235,1.1292900000000001
Requirements
install conda environment from the environment.yml file via
conda env create -f environment.yml
or
conda env create -f environment.yml -p /home/user/anaconda3/envs/env_name
environment.yml is created via
conda env export | grep -v "^prefix: " > environment.yml
How to visualize:
Data used in this repository are obtained here
Run newqt.py, in MainWindow class change between Test1plot (minutes) and Test2plot (hourly)
Predictions: 1 step Predition of test data for Dema_9-rolling_Standard Deviation_100 on Hourly data
details inside 1stepprediction.ipynb
Preliminary 4 step prediction on scaled D1EMA100 on Hourly Data, 300 epochs
details inside d1multistep notebook
D1 means price[hour = i]-price[hour = (i-1)]
Discussion
The network can still improve further by adjusting learning rate, but the data source seems to be too noisy at small window scale. Might consider smoothing it for prediction showcase. Or jump straight to reinforcement learning of entry leaving signals.
Also suspecting that the loss of 1+n step is probably decreasing the learning speed of 1 step. s