PROPHET-OF-WALLSTREET

WORK IN PROGRESS BY: H. Pandit

Overview

PROPHET-OF-WALLSTREET is a Python-based stock market analysis toolkit designed to predict stock prices and detect anomalies in financial time-series data. It integrates traditional statistical forecasting models (such as Facebook’s Prophet), deep learning architectures (like LSTM neural networks), and anomaly detection algorithms (such as Isolation Forest) into a cohesive pipeline. The goal is to empower investors, analysts, and researchers with a transparent, data-driven toolset for financial forecasting and risk assessment.

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Core Features

• Time-Series Forecasting
  Utilizes Facebook’s Prophet model to forecast future stock prices based on seasonal trends. Prophet handles missing data, outliers, and changepoints effectively.

• Deep Learning via LSTM
  Applies Long Short-Term Memory (LSTM) neural networks to learn from historical stock price sequences and predict future trends.

• Anomaly Detection
  Employs the Isolation Forest algorithm to flag data points that deviate significantly from the historical trend, helping detect potential manipulations or unusual market events.

• Data Preprocessing
  Offers utilities for reading stock data, scaling numerical features, and generating training sequences for LSTM-based models.

• Visualization
  Provides functions to visualize stock price trends and mark anomalies directly on the price chart for easier analysis.

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Technical Stack

Category	Tools/Frameworks
Language	Python 3.x
Forecasting	Facebook Prophet
Deep Learning	LSTM (TensorFlow/Keras or PyTorch recommended)
ML/Anomaly Detection	Scikit-learn (Isolation Forest)
Data Handling	Pandas, NumPy
Visualization	Matplotlib

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Code Structure

preprocessing.py

Contains functions for:

• load_stock_data(csv_path): Reads CSV file and sorts by date.
• scale_data(df, feature_cols): Normalizes selected features using MinMaxScaler.
• create_lstm_sequences(data, sequence_length): Generates input-output pairs for LSTM training from normalized sequences.

isolation_forest.py

Provides anomaly detection functionality:

• detect_anomalies(df, contamination=0.01): Applies Isolation Forest to flag anomalies.
• plot_anomalies(df): Visualizes anomalies on a line chart of closing prices.

sample_stock_data.csv

A historical dataset containing:

• date, open, high, low, close, volume
  Spans one year of daily stock prices (approx. 261 rows).

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Algorithms Used

Prophet

An additive time series forecasting model that handles daily, weekly, and yearly seasonality. Designed for interpretability and ease of use by business users.

LSTM (Long Short-Term Memory)

A specialized form of recurrent neural network (RNN) capable of learning long-term dependencies. Ideal for sequential prediction tasks like stock price modeling.

Isolation Forest

An unsupervised learning algorithm for anomaly detection. Efficiently isolates outliers using an ensemble of randomly partitioned trees.

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Sample Usage

Load and Preprocess Data

from preprocessing import load_stock_data, scale_data, create_lstm_sequences

df = load_stock_data("sample_stock_data.csv")
scaled_values, scaler = scale_data(df, feature_cols=["close"])
X, y = create_lstm_sequences(scaled_values, sequence_length=50)

Apply Anomaly Detection

from isolation_forest import detect_anomalies, plot_anomalies

df_with_scores = detect_anomalies(df, contamination=0.01)
plot_anomalies(df_with_scores)

Build LSTM Model (example outline)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

model = Sequential([
    LSTM(64, input_shape=(50, 1)),
    Dense(1)
])

model.compile(loss='mse', optimizer='adam')
model.fit(X, y, epochs=10, batch_size=32)

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Visual Output

The tool produces:

• A time series plot of closing stock prices.
• Anomaly markers (in red) overlaid on the price trend.
• LSTM and Prophet forecasts (can be visualized separately as extensions).

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Installation & Setup

Requirements

• Python 3.8+
• Recommended packages:

pip install pandas numpy scikit-learn matplotlib tensorflow prophet

Running the Pipeline

1. Replace sample_stock_data.csv with your own stock data.
2. Call the preprocessing and modeling functions.
3. Visualize anomalies or predictions using provided functions or Jupyter notebooks.

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Potential Applications

• Retail Investors: To identify abnormal price behavior and inform buy/sell decisions.
• Quant Researchers: As a base framework for algorithmic strategy development.
• Financial Advisors: To generate automated forecasts and anomaly reports.
• Risk Analysts: For detecting early warning signals of market manipulation or volatility.

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Limitations & Extensions

• Not Financial Advice: This tool is for research and educational purposes.
• No Live Feed Integration: Requires CSV-based historical data input.
• Basic LSTM Model: Can be extended with additional features (e.g., technical indicators, macro data).
• No License Specified: Currently lacks an open-source license.

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License

GNU GENERAL PUBLIC LICENSE Version 2, June 1991

Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

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Authors & Credits

Originally developed by H PANDIT
GitHub: MykeHaunt/PROPHET-OF-WALLSTREET