Stock market predictions and financial analyses provided by this code are for informational purposes only and should not be considered as investment advice. The information, analyses, and predictions generated by this code are based on historical data, technical indicators, and machine learning models, which do not guarantee future performance or accuracy. Use of this code and any decision made based on its output is at your own risk. Trading and investing in the financial markets involves significant risk, and it's important to conduct thorough research, exercise caution, and consider seeking advice from a qualified financial advisor before making any investment decisions. The author(s) of this code are not responsible for any financial losses, gains, or decisions made based on the information provided by this code. Past performance is not indicative of future results, and predictions made by this code may not be accurate or reliable. This code is provided as-is without any warranty. Users are solely responsible for their own decisions and actions taken based on the information obtained from this code. Please perform your own due diligence and be aware of the risks involved in financial markets before making any investment decisions.
predictions_12030.mp4
The main idea behind this repo is to predict stock market up to several weeks in advance using daily trends. It combines differents stock from a one sector. By using transfer learning and fine tune to make final predictions for a given stock. End results are far better that predictions from a single stock.
usage: main.py [-h] [--tickers [TICKERS ...]] [--start_date START_DATE] [--end_date END_DATE] [--validation_days VALIDATION_DAYS] [--epochs EPOCHS] [--layers LAYERS]
[--input_time_steps INPUT_TIME_STEPS] [--output_time_steps OUTPUT_TIME_STEPS] [--batch_size BATCH_SIZE] [--lr LR] [--output_dir OUTPUT_DIR]python main.py --output_dir tech_stocks --tickers 'MSFT' 'AAPL' 'AMZN' 'GOOGL'tickers = ['MSFT', 'AAPL', 'AMZN', 'GOOGL']
dates (start, end, validation) 2018-11-08 2023-11-07 2022-11-07
epochs, lr, batch_size layers = 1000 1e-04 32 4
time steps (input, output) = 120 120
Directory 'tech_stocks/' created successfully.
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tech_stocks/MSFT.csv
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tech_stocks/AAPL.csv
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tech_stocks/AMZN.csv
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tech_stocks/GOOGL.csv
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tech_stocks/META.csvTrain all stocks as one combined model but training in sequentially..
new/AMZN.csv
train dataset <bound method DataFrame.info of| index | Date | Open | High | Low | Close | Adj Close | Volume |
|---|---|---|---|---|---|---|---|
| 0 | 2018-11-08 | 87.750000 | 89.199997 | 86.255501 | 87.745499 | 87.745499 | 130698000 |
| 1004 | 2022-11-04 | 91.489998 | 92.440002 | 88.040001 | 90.980003 | 90.980003 | 129101300 |
[1005 rows x 7 columns]>
test dataset <bound method DataFrame.info of
| index | Date | Open | High | Low | Close | Adj Close | Volume |
|---|---|---|---|---|---|---|---|
| 1005 | 2022-11-07 | 91.949997 | 92.099998 | 89.040001 | 90.529999 | 90.529999 | 77495700 |
| 1255 | 2023-11-06 | 138.759995 | 140.729996 | 138.360001 | 139.740005 | 139.740005 | 44928800 |
[251 rows x 7 columns]>
train/val samples (1016, 120, 5) (1016, 120) (11, 120, 5) (11, 120)
## Model summary, using same architecture for each stock
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 120, 5)] 0
LSTMLayer1 (Bidirectional) (None, 120, 240) 120960
LSTMLayer2 (Dropout) (None, 120, 240) 0
LSTMLayer3 (Bidirectional) (None, 120, 240) 346560
LSTMLayer4 (Dropout) (None, 120, 240) 0
LSTMLayer5 (Bidirectional) (None, 120, 240) 346560
LSTMLayer6 (Dropout) (None, 120, 240) 0
LSTMLayer7 (Bidirectional) (None, 240) 346560
LSTMLayer8 (Dropout) (None, 240) 0
dense (Dense) (None, 240) 57840
dist (DistributionLambda) ((None, 120), 0
(None, 120))
=================================================================
Total params: 1,218,480
Trainable params: 1,218,480
Non-trainable params: 0
_________________________________________________________________
## Training for a combined model using stocks data one by one
_________________________________________________________________
new/AMZN.csv
_________________________________________________________________
Epoch 1/1000
32/32 [==============================] - ETA: 0s - loss: 0.7739
Epoch 00001: val_loss improved from inf to 0.44102, saving model to tech_stocks/models/combined_model
32/32 [==============================] - 20s 408ms/step - loss: 0.7739 - val_loss: 0.4410
..........
Epoch 101/1000
32/32 [==============================] - ETA: 0s - loss: -1.5346
Epoch 00101: val_loss did not improve from -1.80982
32/32 [==============================] - 8s 255ms/step - loss: -1.5346 - val_loss: -1.6621
ticker AMZN 13.837315817674002 [min]
_________________________________________________________________
new/MSFT.csv
_________________________________________________________________
Epoch 1/1000
32/32 [==============================] - ETA: 0s - loss: 0.7634
Epoch 00001: val_loss improved from inf to 0.55485, saving model to tech_stocks/models/combined_model
32/32 [==============================] - 19s 377ms/step - loss: 0.7634 - val_loss: 0.5549
.........
Epoch 66/1000
32/32 [==============================] - ETA: 0s - loss: -1.7582
Epoch 00066: val_loss did not improve from -1.74426
32/32 [==============================] - 8s 247ms/step - loss: -1.7582 - val_loss: -1.6977
ticker MSFT 8.743007981777192 [min]
runs rest of the stocks....
## Now train each model again using combined model as a starting point.
_________________________________________________________________
Epoch 1/1000.<br>
32/32 [==============================] - ETA: 0s - loss: 0.7858
Epoch 00001: val_loss improved from inf to 0.61471, saving model to tech_stocks/models/AAPL_model
32/32 [==============================] - 21s 447ms/step - loss: 0.7858 - val_loss: 0.6147
...........
Epoch 126/1000
32/32 [==============================] - ETA: 0s - loss: -1.9969
Epoch 00126: val_loss did not improve from -1.90743
32/32 [==============================] - 8s 249ms/step - loss: -1.9969 - val_loss: -1.8522
tech_stocks/models/AAPL_model
...........................
and the rest of the stocks
new/models/MSFT_model
input shape (10, 120, 5)
predicting using model file new/models/MSFT_model
prediction file name new/MSFT_pred.npy
ticker MSFT 7.256359632809957 [min]
...........................
and the rest of the stocks
total time [min] = 67.62601785262426
