Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Enhancements and Refactoring’s in run.py Script: String formatting Updates, and AI Feature Integration #35

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Enhancements and Refactoring’s in run.py Script: String formatting Updates, and AI Feature Integration #35

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4aca057
ai_data_pipeline.py
RahulVadisetty91 Aug 27, 2024
0dafe03
Merge pull request #1 from RahulVadisetty91/RahulVadisetty91-patch-1
RahulVadisetty91 Aug 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
116 changes: 116 additions & 0 deletions ai_data_pipeline.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,116 @@
from models.autoencoder import AutoEncoder
from models.nnmodel import NNModel
from models.rfmodel import RFModel
from data_processor.data_processing import DataProcessing
from data_processor.feature_engineering import AdvancedFeatureEngineering
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import StackingClassifier
import matplotlib.pyplot as plt
from data_processor.base_bars import BaseBars
import joblib

# Define constants
RAW_DATA_PATH = "sample_data/raw_data/price_vol.csv"
PROCESSED_DATA_PATH = "sample_data/processed_data/"
TICK_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/tick_bars.csv"
DOLLAR_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/dollar_bars.csv"
VOLUME_BARS_PATH = PROCESSED_DATA_PATH + "price_bars/volume_bars.csv"
AUTOENCODER_DATA_PATH = PROCESSED_DATA_PATH + "autoencoder_data/"
NN_DATA_PATH = PROCESSED_DATA_PATH + "nn_data/"
RF_DATA_PATH = PROCESSED_DATA_PATH + "rf_data/"
FULL_X_PATH = AUTOENCODER_DATA_PATH + "full_x.csv"
FULL_Y_PATH = AUTOENCODER_DATA_PATH + "full_y.csv"
TRAIN_X_PATH = RF_DATA_PATH + "train_x.csv"
TRAIN_Y_PATH = RF_DATA_PATH + "train_y.csv"
TEST_X_PATH = RF_DATA_PATH + "test_x.csv"
TEST_Y_PATH = RF_DATA_PATH + "test_y.csv"

print('Creating tick bars...')
base = BaseBars(RAW_DATA_PATH, TICK_BARS_PATH, "tick", 10)
base.batch_run()

print('Creating dollar bars...')
base = BaseBars(RAW_DATA_PATH, DOLLAR_BARS_PATH, "dollar", 20000)
base.batch_run()

print('Creating volume bars...')
base = BaseBars(RAW_DATA_PATH, VOLUME_BARS_PATH, "volume", 50)
base.batch_run()

print('Processing data...')
preprocess = DataProcessing(0.8)
df = preprocess.make_features(file_path=DOLLAR_BARS_PATH, window=20,
csv_path=AUTOENCODER_DATA_PATH, save_csv=True)
fulldata, y_values, train_x, train_y, test_x, test_y = preprocess.make_train_test(df_x=df, df_y=None, window=1,
csv_path=AUTOENCODER_DATA_PATH, save_csv=True)

print('Loading data...')
a_train_x = pd.read_csv(TRAIN_X_PATH, index_col=0)
a_train_y = pd.read_csv(TRAIN_Y_PATH, index_col=0)
a_test_x = pd.read_csv(TEST_X_PATH, index_col=0)
a_test_y = pd.read_csv(TEST_Y_PATH, index_col=0)
print(a_train_x.head())
print(a_train_x.shape)

print('Scaling data...')
scaler = MinMaxScaler(feature_range=(-1, 1))
x_train_a = scaler.fit_transform(a_train_x.iloc[:, 1:])
x_test_a = scaler.transform(a_test_x.iloc[:, 1:])

autoencoder = AutoEncoder(20, x_train_a.shape[1])
autoencoder.build_model(100, 50, 50, 100)

print('Training model...')
autoencoder.train_model(autoencoder.autoencoder, x_train_a, epochs=20, model_name='autoencoder')

print('Testing model...')
autoencoder.test_model(autoencoder.autoencoder, x_test_a)

print('Encoding data...')
a_full_data = pd.read_csv(FULL_X_PATH, index_col=0)
a_scaled_full = pd.DataFrame(scaler.transform(a_full_data.iloc[:, 1:]))
autoencoder.encode_data(a_scaled_full, csv_path=NN_DATA_PATH + 'full_x.csv')

print('Processing data...')
preprocess = DataProcessing(0.8)
df1 = pd.read_csv(NN_DATA_PATH + "full_x.csv", index_col=0)
df2 = pd.read_csv(FULL_Y_PATH, index_col=0)
fulldata, y_values, train_x, train_y, test_x, test_y = preprocess.make_train_test(df_x=df1, df_y=df2, window=1,
csv_path=RF_DATA_PATH, has_y=True, binary_y=True, save_csv=True)
y = pd.read_csv(RF_DATA_PATH + 'full_y.csv', index_col=0)
preprocess.check_labels(y)

print('Loading data...')
train_x = pd.read_csv(TRAIN_X_PATH, index_col=0)
train_y = pd.read_csv(TRAIN_Y_PATH, index_col=0)
test_x = pd.read_csv(TEST_X_PATH, index_col=0)
test_y = pd.read_csv(TEST_Y_PATH, index_col=0)
print(train_x.head())
print(train_y.shape)

print('Scaling data...')
scaler = MinMaxScaler(feature_range=(-1, 1))
x_train = scaler.fit_transform(train_x)
x_test = scaler.transform(test_x)

print('Training Random Forest model...')
rfmodel = RFModel(x_train.shape[1])
rfmodel.make_model(300, -1, verbose=1)
rfmodel.train_model(x_train, train_y)
rfmodel.test_model(x_test, test_y)

print('Training AutoEncoder-based Random Forest model...')
rfmodel = RFModel(x_train_a.shape[1])
rfmodel.make_model(300, -1, verbose=1)
rfmodel.train_model(x_train_a, train_y)
rfmodel.test_model(x_test_a, test_y)

# Save models
joblib.dump(rfmodel, 'rfmodel.pkl')
joblib.dump(autoencoder, 'autoencoder.pkl')

print('Models saved.')