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Loop around train and test/evaluate for 30 diffent contiguous lookup date valies (1-30). Put results in file CSV.
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‎machine-learning/stock-prediction/evaluate.py‎

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import numpy as np
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import matplotlib.pyplot as plt
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from stock_prediction import create_model, load_data
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from parameters import *
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def plot_graph(test_df,MY_LOOKUP_STEP):
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"""
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This function plots true close price along with predicted close price
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with blue and red colors respectively
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"""
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plt.plot(test_df[f'true_adjclose_{MY_LOOKUP_STEP}'], c='k')
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plt.plot(test_df[f'adjclose_{MY_LOOKUP_STEP}'], c='bo')
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plt.xlabel("Days")
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plt.ylabel("Price")
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plt.legend(["Actual Price", "Predicted Price"])
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plt.show()
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def get_final_df(model, data, MY_LOOKUP_STEP):
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"""
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This function takes the `model` and `data` dict to
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construct a final dataframe that includes the features along
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with true and predicted prices of the testing dataset
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"""
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# if predicted future price is higher than the current,
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# then calculate the true future price minus the current price, to get the buy profit
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buy_profit = lambda current, pred_future, true_future: true_future - current if pred_future > current else 0
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# if the predicted future price is lower than the current price,
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# then subtract the true future price from the current price
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sell_profit = lambda current, pred_future, true_future: current - true_future if pred_future < current else 0
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X_test = data["X_test"]
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y_test = data["y_test"]
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# perform prediction and get prices
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y_pred = model.predict(X_test)
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if SCALE:
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y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
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y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
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test_df = data["test_df"]
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# add predicted future prices to the dataframe
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test_df[f"adjclose_{MY_LOOKUP_STEP}"] = y_pred
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# add true future prices to the dataframe
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test_df[f"true_adjclose_{MY_LOOKUP_STEP}"] = y_test
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# sort the dataframe by date
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test_df.sort_index(inplace=True)
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final_df = test_df
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# add the buy profit column
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final_df["buy_profit"] = list(map(buy_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{MY_LOOKUP_STEP}"],
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final_df[f"true_adjclose_{MY_LOOKUP_STEP}"])
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# since we don't have profit for last sequence, add 0's
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)
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# add the sell profit column
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final_df["sell_profit"] = list(map(sell_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{MY_LOOKUP_STEP}"],
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final_df[f"true_adjclose_{MY_LOOKUP_STEP}"])
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# since we don't have profit for last sequence, add 0's
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)
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return final_df
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def predict(model, data):
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# retrieve the last sequence from data
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last_sequence = data["last_sequence"][-N_STEPS:]
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# expand dimension
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last_sequence = np.expand_dims(last_sequence, axis=0)
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# get the prediction (scaled from 0 to 1)
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prediction = model.predict(last_sequence)
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# get the price (by inverting the scaling)
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if SCALE:
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predicted_price = data["column_scaler"]["adjclose"].inverse_transform(prediction)[0][0]
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else:
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predicted_price = prediction[0][0]
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return predicted_price
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predictions = []
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# predictions = np.array(float)
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# predictions.resize(30)
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for MY_LOOKUP_STEP in range(1, 30):
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# load the data
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data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE,
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shuffle=SHUFFLE, lookup_step=MY_LOOKUP_STEP, test_size=TEST_SIZE,
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feature_columns=FEATURE_COLUMNS)
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# construct the model
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model = create_model(N_STEPS, len(FEATURE_COLUMNS), loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,
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dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)
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my_model_name = f"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\
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{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{MY_LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
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if BIDIRECTIONAL:
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my_model_name += "-b"
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# load optimal model weights from results folder
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model_path = os.path.join("results", my_model_name) + ".h5"
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model.load_weights(model_path)
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# evaluate the model
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loss, mae = model.evaluate(data["X_test"], data["y_test"], verbose=0)
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# calculate the mean absolute error (inverse scaling)
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if SCALE:
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mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform([[mae]])[0][0]
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else:
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mean_absolute_error = mae
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# get the final dataframe for the testing set
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final_df = get_final_df(model, data, MY_LOOKUP_STEP)
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# predict the future price
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future_price = predict(model, data)
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# we calculate the accuracy by counting the number of positive profits
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accuracy_score = (len(final_df[final_df['sell_profit'] > 0]) + len(final_df[final_df['buy_profit'] > 0])) / len(final_df)
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# calculating total buy & sell profit
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total_buy_profit = final_df["buy_profit"].sum()
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total_sell_profit = final_df["sell_profit"].sum()
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# total profit by adding sell & buy together
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total_profit = total_buy_profit + total_sell_profit
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# dividing total profit by number of testing samples (number of trades)
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profit_per_trade = total_profit / len(final_df)
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# Append new prediction to array
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# predictions[MY_LOOKUP_STEP] = future_price
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predictions.append(future_price)
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# printing metrics
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print(f"Future price after {MY_LOOKUP_STEP} days is {future_price:.2f}$")
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print(f"{LOSS} loss:", loss)
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print("Mean Absolute Error:", mean_absolute_error)
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print("Accuracy score:", accuracy_score)
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print("Total buy profit:", total_buy_profit)
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print("Total sell profit:", total_sell_profit)
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print("Total profit:", total_profit)
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print("Profit per trade:", profit_per_trade)
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# plot true/pred prices graph
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plot_graph(final_df,MY_LOOKUP_STEP)
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print(final_df.tail(10))
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print(f"Future price after {MY_LOOKUP_STEP} days is {future_price:.2f}$")
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# save the final dataframe to csv-results folder
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csv_results_folder = "csv-results"
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if not os.path.isdir(csv_results_folder):
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os.mkdir(csv_results_folder)
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csv_filename = os.path.join(csv_results_folder, my_model_name + ".csv")
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final_df.to_csv(csv_filename)
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print(predictions)
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predictions = np.array(predictions)
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predictions.tofile(f"{csv_results_folder}\evaluate30_{date_now}_{ticker}.csv",",")

‎machine-learning/stock-prediction/parameters.py‎

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# Window size or the sequence length
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N_STEPS = 50
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# Lookup step, 1 is the next day
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LOOKUP_STEP = 15
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LOOKUP_STEP = 1 #15
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# whether to scale feature columns & output price as well
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SCALE = True
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SCALE = True #False #True
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scale_str = f"sc-{int(SCALE)}"
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# whether to shuffle the dataset
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SHUFFLE = True
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EPOCHS = 500
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# Amazon stock market
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ticker = "AMZN"
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# ticker = "AMZN"
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# Oxford Nanopore stock market
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ticker = "ONTTF"
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ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv")
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# model name to save, making it as unique as possible based on parameters
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model_name = f"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\

‎machine-learning/stock-prediction/train30.py‎

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from stock_prediction import create_model, load_data
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from tensorflow.keras.layers import LSTM
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from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
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import os
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import pandas as pd
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from parameters import *
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# create these folders if they does not exist
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if not os.path.isdir("results"):
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os.mkdir("results")
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if not os.path.isdir("logs"):
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os.mkdir("logs")
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if not os.path.isdir("data"):
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os.mkdir("data")
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for MY_LOOKUP_STEP in range(1,30):
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# load the data
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data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE,
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shuffle=SHUFFLE, lookup_step=MY_LOOKUP_STEP, test_size=TEST_SIZE,
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feature_columns=FEATURE_COLUMNS)
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# save the dataframe
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data["df"].to_csv(ticker_data_filename)
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# construct the model
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model = create_model(N_STEPS, len(FEATURE_COLUMNS), loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,
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dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)
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my_model_name = f"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\
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{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{MY_LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
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if BIDIRECTIONAL:
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my_model_name += "-b"
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# some tensorflow callbacks
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checkpointer = ModelCheckpoint(os.path.join("results", my_model_name + ".h5"), save_weights_only=True, save_best_only=True, verbose=1)
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tensorboard = TensorBoard(log_dir=os.path.join("logs", my_model_name))
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# train the model and save the weights whenever we see
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# a new optimal model using ModelCheckpoint
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history = model.fit(data["X_train"], data["y_train"],
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batch_size=BATCH_SIZE,
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epochs=EPOCHS,
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validation_data=(data["X_test"], data["y_test"]),
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callbacks=[checkpointer, tensorboard],
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verbose=1)

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