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Jamie
2021-05-17 12:01:21 -04:00
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13
gamestonk_terminal/prediction_techniques/FUNE-TUNING-NN-models.md
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@@ -35,8 +35,9 @@ To try "backtesting" to a certain data, you can specify the end date. This will
Decide whether you want to pre-process your share price data or not. I.e. do you want to use these values between 18 and 245, or perhaps normalize these between 0 and 1? The later may improve performance.
```
-p/--pp : pre-processing data. Default minmax.
Preprocess = "minmax"
```
This is defined in the config file.
5. **Select input, prediction days and jumps in training data**
@@ -54,13 +55,6 @@ Personally, I recommend to set this for a really short window. This is because t
-d/--days : prediciton days. Default 5.
```
The **jumps in the training data** corresponds to the number of days we want to jump between training loops.
Let's say we start training our Neural Network from 2021-01-04, with 10 inputs (days) and 5 prediction days. If jump is set to 1, it means that the after optimizing our model for input data from 2021-01-04 to 2021-01-15, and predicting from 2021-01-18 to 2021-01-22, we will optimize the model for input data from 2021-01-05 to 2021-01-18, and predicting from 2021-01-19 to 2021-01-25. On the other hand, if we set the jump to 5, it means that we are identifying a "weekly" frequency in the data, and therefore, our next model optimization would use input data from 2021-01-11 to 2021-01-22, and predicting from 2021-01-25 to 2021-01-29.
```
-j/--jumps : number of jumps in training data. Default 1.
```
6. **Select NN architecture.** TL;DR: LSTM > RNN > MLP
**MLP**: The neurons don't have any memory.
@@ -98,8 +92,9 @@ This model is set in [config_neural_network_models.py](/config_neural_network_mo
**Loss function**: Often chosen having the activation functions of the hidden layers in mind. See https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/, and https://www.tensorflow.org/api_docs/python/tf/keras/losses.
```
-l/--loss : loss function. Default mae.
Loss = "mae"
```
This is defined in the config file.
**Optimizer technique**:Adaptive Moment Estimation (_adam_) is usually the default choice for this type of problems. See https://www.tensorflow.org/api_docs/python/tf/keras/optimizers.

63
gamestonk_terminal/prediction_techniques/README.md
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@@ -43,19 +43,18 @@ Exponential Smoothing (based on trend+seasonality, see https://otexts.com/fpp2/t
* -t : trend component: N: None, A: Additive, Ad: Additive Damped. Default N.
* -s : seasonality component: N: None, A: Additive, M: Multiplicative. Default N.
* -p : seasonal periods. Default 5.
* -d : prediciton days. Default 5.
* -d : prediction days. Default 5.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
![ets_pltr](https://user-images.githubusercontent.com/25267873/110266847-97a6d280-7fb6-11eb-997e-0b598abc713b.png)
## knn <a name="knn"></a>
```
usage: knn [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-n N_NEIGHBORS]
usage: knn [-i N_INPUTS] [-d N_DAYS] [-n N_NEIGHBORS]
```
k-Nearest Neighbors:
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
* -d : prediction days. Default 5.
* -n : number of neighbors to use on the algorithm. Default 20.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
@@ -63,51 +62,47 @@ k-Nearest Neighbors:
## linear <a name="linear"></a>
```
usage: linear [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 1 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: linear [-i N_INPUTS] [-d N_DAYS]
usage: regression -p 1 [-i N_INPUTS] [-d N_DAYS]
```
Linear Regression (p=1):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
* -d : prediction days. Default 5.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
![linear](https://user-images.githubusercontent.com/25267873/108604948-d3cc1780-73a8-11eb-860f-49274a34038b.png)
## quadratic <a name="quadratic"></a>
```
usage: quadratic [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 2 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: quadratic [-i N_INPUTS] [-d N_DAYS]
usage: regression -p 2 [-i N_INPUTS] [-d N_DAYS]
```
Quadratic Regression (p=2):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
* -d : prediction days. Default 5.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
![quadratic](https://user-images.githubusercontent.com/25267873/108604935-cca50980-73a8-11eb-9af1-bba807203cc6.png)
## cubic <a name="cubic"></a>
```
usage: cubic [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 3 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: cubic [-i N_INPUTS] [-d N_DAYS]
usage: regression -p 3 [-i N_INPUTS] [-d N_DAYS]
```
Cubic Regression (p=3):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
* -d : prediction days. Default 5.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
![cubic](https://user-images.githubusercontent.com/25267873/108604941-d169bd80-73a8-11eb-9220-84a7013e1283.png)
## regression <a name="regression"></a>
```
usage: regression -p N_POLYNOMIAL [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p N_POLYNOMIAL [-i N_INPUTS] [-d N_DAYS]
```
Regression:
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
* -d : prediction days. Default 5.
* -p : polynomial associated with regression. Required.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
@@ -135,24 +130,21 @@ Auto-Regressive Integrated Moving Average:
usage: fbprophet [-d N_DAYS]
```
Facebook's Prophet:
* -d : prediciton days. Default 5.
* -d : prediction days. Default 5.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
![prophet](https://user-images.githubusercontent.com/25267873/108604938-cf9ffa00-73a8-11eb-973b-0affb343e2f6.png)
## mlp <a name="mlp"></a>
```
usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {minmax, normalization,standardization,none}]
[-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID] [--lr LEARNING_RATE] [--no_shuffle]
usage: mlp [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
[--lr LEARNING_RATE] [--no_shuffle]
```
MulitLayer Perceptron:
* -d : prediciton days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
* --epochs : number of training epochs. Default 200.
* -p : pre-processing data. Default minmax.
* -v : validation split. Default 0.1
* -l : loss function. Default mae.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
@@ -181,17 +173,14 @@ MultiLayer_Perceptron \
## rnn <a name="rnn"></a>
```
usage: rnn [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {normalization,standardization,none}]
usage: rnn [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-p {normalization,standardization,none}]
[-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID] [--lr LEARNING_RATE] [--no_shuffle]
```
Recurrent Neural Network:
* -d : prediciton days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
* --epochs : number of training epochs. Default 200.
* -p : pre-processing data. Default normalization.
* -v : validation split. Default 0.1
* -l : loss function. Default mae.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
@@ -227,17 +216,14 @@ Recurrent_Neural_Network \
## lstm <a name="lstm"></a>
```
usage: lstm [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {normalization,standardization,none}]
[-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID] [--lr LEARNING_RATE] [--no_shuffle]
usage: lstm [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
[--lr LEARNING_RATE] [--no_shuffle]
```
Long-Short Term Memory:
* -d : prediciton days. Default 5.
* -d : prediction days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
* --epochs : number of training epochs. Default 200.
* -p : pre-processing data. Default normalization.
* -v : validation split. Default 0.1
* -l : loss function. Default mae.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
@@ -270,17 +256,14 @@ Long_Short_Term_Memory \
## conv1d <a name="conv1d"></a>
```
usage: lstm [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {normalization,standardization,minmax,none}]
[-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID] [--lr LEARNING_RATE] [--no_shuffle]
usage: lstm [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
[--lr LEARNING_RATE] [--no_shuffle]
```
1D Convolutional Neural Net:
* -d : prediction days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
* --epochs : number of training epochs. Default 50.
* -p : pre-processing data. Default minmax.
* -v : validation split. Default 0.1
* -l : loss function. Default mae.
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).