def get_accuracy(model, data):
y_test = data["y_test"]
X_test = data["X_test"]
y_pred = model.predict(X_test)
y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
y_pred = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_pred[LOOKUP_STEP:]))
y_test = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_test[LOOKUP_STEP:]))
return accuracy_score(y_test, y_pred)
def plot_graph(model, data):
y_test = data["y_test"]
X_test = data["X_test"]
y_pred = model.predict(X_test)
y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
plt.plot(y_test[-200:], c='b')
plt.plot(y_pred[-200:], c='r')
plt.xlabel("Days")
plt.ylabel("Price")
plt.legend(["Actual Price", "Predicted Price"])
plt.show()
def predict(model, data):
# retrieve the last sequence from data
last_sequence = data["last_sequence"][-N_STEPS:]
# expand dimension
last_sequence = np.expand_dims(last_sequence, axis=0)
# get the prediction (scaled from 0 to 1)
prediction = model.predict(last_sequence)
# get the price (by inverting the scaling)
if SCALE:
predicted_price = data["column_scaler"]["adjclose"].inverse_transform(prediction)[0][0]
else:
predicted_price = prediction[0][0]
return predicted_price
def predict(model, data, classification=False):
# retrieve the last sequence from data
last_sequence = data["last_sequence"][:N_STEPS]
# retrieve the column scalers
column_scaler = data["column_scaler"]
# reshape the last sequence
last_sequence = last_sequence.reshape((last_sequence.shape[1], last_sequence.shape[0]))
# expand dimension
last_sequence = np.expand_dims(last_sequence, axis=0)
# get the prediction (scaled from 0 to 1)
prediction = model.predict(last_sequence)
# get the price (by inverting the scaling)
predicted_price = column_scaler["adjclose"].inverse_transform(prediction)[0][0]
return predicted_price
def plot_graph(model, data, name):
y_test = data["y_test"]
X_test = data["X_test"]
y_pred = model.predict(X_test)
y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0)))
y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred))
plt.close()
plt.plot(y_test[-200:], c='b')
plt.plot(y_pred[-200:], c='r')
plt.title(name)
plt.xlabel("Days")
plt.ylabel("Price")
plt.legend(["Actual Price", "Predicted Price"])
plt.savefig('/root/PycharmProjects/stock-advisor/images/results.png')
plt.show()
def get_final_df(model, data):
"""
This function takes the `model` and `data` dict to
construct a final dataframe that includes the features along
with true and predicted prices of the testing dataset
"""
# if predicted future price is higher than the current,
# then calculate the true future price minus the current price, to get the buy profit
buy_profit = lambda current, true_future, pred_future: true_future - current if pred_future > current else 0
# if the predicted future price is lower than the current price,
# then subtract the true future price from the current price
sell_profit = lambda current, true_future, pred_future: current - true_future if pred_future < current else 0
X_test = data["X_test"]
y_test = data["y_test"]
# perform prediction and get prices
y_pred = model.predict(X_test)
if SCALE:
y_test = np.squeeze(
data["column_scaler"]["adjclose"].inverse_transform(
np.expand_dims(y_test, axis=0)))
y_pred = np.squeeze(
data["column_scaler"]["adjclose"].inverse_transform(y_pred))
test_df = data["test_df"]
# add predicted future prices to the dataframe
test_df[f"adjclose_{LOOKUP_STEP}"] = y_pred
# add true future prices to the dataframe
test_df[f"true_adjclose_{LOOKUP_STEP}"] = y_test
# sort the dataframe by date
test_df.sort_index(inplace=True)
final_df = test_df
# add the buy profit column
final_df["buy_profit"] = list(
map(buy_profit, final_df["adjclose"],
final_df[f"adjclose_{LOOKUP_STEP}"],
final_df[f"true_adjclose_{LOOKUP_STEP}"])
# since we don't have profit for last sequence, add 0's
)
# add the sell profit column
final_df["sell_profit"] = list(
map(sell_profit, final_df["adjclose"],
final_df[f"adjclose_{LOOKUP_STEP}"],
final_df[f"true_adjclose_{LOOKUP_STEP}"])
# since we don't have profit for last sequence, add 0's
)
return final_df
def predict_gap(model, data, df2, indexVals):
predicted_prices = []
for X in range(LOOKUP_STEP):
# retrieve the last sequence from data
last_sequence = data["last_sequence"][-N_STEPS-X:]
last_sequence = last_sequence[:N_STEPS]
# expand dimension
last_sequence = np.expand_dims(last_sequence, axis=0)
# get the prediction (scaled from 0 to 1)
prediction = model.predict(last_sequence)
# get the price (by inverting the scaling)
if SCALE:
predicted_price = data["column_scaler"]["adjclose"].inverse_transform(prediction)[0][0]
else:
predicted_price = prediction[0][0]
df2.loc[indexVals[-X],'forecast'] = predicted_price
return df2
