A recurrent neural network for predicting stock market performance
A Recurrent Neural Network For Predicting Stock Prices
Below is the structure of AlphaNetV2
input: (batch_size, history time steps, features) stride = 5 input -> expand features -> BN -> LSTM -> BN -> Dense(linear)
Below is the structure of AlphaNetV3
input: (batch_size, history time steps, features) stride = 5 +-> expand features -> BN -> GRU -> BN -+ input --| stride = 10 |- concat -> Dense(linear) +-> expand features -> BN -> GRU -> BN -+
Either clone this repository or just use pypi:
pip install alphanet.
The pypi project is here: alphanet.
Step 0: import alphanet
from alphanet import AlphaNetV3, load_model from alphanet.data import TrainValData, TimeSeriesData from alphanet.metrics import UpDownAccuracy
Step 1: build data
# read data df = pd.read_csv("some_data.csv") # compute label (future return) df_future_return = here_you_compute_it_by_your_self df = df_future_return.merge(df, how="inner", left_on=["date", "security_code"], right_on=["date", "security_code"]) # create an empty list stock_data_list =  # put each stock into the list using TimeSeriesData() class security_codes = df["security_code"].unique() for code in security_codes: table_part = df.loc[df["security_code"] == code, :] stock_data_list.append(TimeSeriesData(dates=table_part["date"].values, # date column data=table_part.iloc[:, 3:].values, # data columns labels=table_part["future_10_cum_return"].values)) # label column # put stock list into TrainValData() class, specify dataset lengths train_val_data = TrainValData(time_series_list=stock_data_list, train_length=1200, # 1200 trading days for training validate_length=150, # 150 trading days for validation history_length=30, # each input contains 30 days of history sample_step=2, # jump to days forward for each sampling train_val_gap=10 # leave a 10-day gap between training and validation
Step 2: get datasets from desired period
# get one training period that start from 20110131 train, val, dates_info = train_val_data.get(20110131, order="by_date") print(dates_info)
Step 3: compile the model and start training
# get an AlphaNetV3 instance model = AlphaNetV3(l2=0.001, dropout=0.0) # you may use UpDownAccuracy() here to evaluate performance model.compile(metrics=[tf.keras.metrics.RootMeanSquaredError(), UpDownAccuracy()] # train model.fit(train.batch(500).cache(), validation_data=val.batch(500).cache(), epochs=100)
Step 4: save and load
# save model by save method model.save("path_to_your_model") # or just save weights model.save_weights("path_to_your_weights")
# load entire model using load_model() from alphanet module model = load_model("path_to_your_model") # only load weights by first creating a model instance model = AlphaNetV3(l2=0.001, dropout=0.0) model.load_weights("path_to_your_weights")
alphanet.load_model(filename) recognizes custom
If you do not use
you can also use
For detailed documentation, go to alphanet documentation.
For implementation details, go to alphanet source folder.
One Little Caveat
The model expands features quadratically. So, if you have 5 features, it will be expanded to more than 50 features (for AlphaNetV3), and if you have 10 features, it will be expanded to more than 200 features. Therefore, do not put too many features inside.
One More Note
alphanet.datamodule is completely independent from
and can be a useful tool for training any timeseries neural network.
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