gen_syntheticdata.py

"""Copyright (C) 2024 James Sawyer
All rights reserved.

This script and the associated files are private
and confidential property. Unauthorized copying of
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"""

# -*- coding: utf-8 -*-
# pylint: disable=C0116, W0621, W1203, C0103, C0301, W1201, W0511, E0401, E1101, E0606
# C0116: Missing function or method docstring
# W0621: Redefining name %r from outer scope (line %s)
# W1203: Use % formatting in logging functions and pass the % parameters as arguments
# C0103: Constant name "%s" doesn't conform to UPPER_CASE naming style
# C0301: Line too long (%s/%s)
# W1201: Specify string format arguments as logging function parameters
# W0511: TODOs
# E1101: Module 'holidays' has no 'US' member (no-member) ... it does, so ignore this
# E0606: possibly-used-before-assignment, ignore this
# UP018: native-literals (UP018)

from datetime import datetime, timedelta

import numpy as np
import pandas as pd


def generate_realistic_stock_data(num_years=6):
    """Generate an hourly stock dataset for `num_years` (weekdays only).

    Returns a pandas DataFrame with columns:
    [Gmt time, Open, High, Low, Close, Volume]
    in a simulated 'realistic' pattern without requiring manual parameter tweaking.
    """
    # -----------------------
    # 1. Automatic Parameter Selection
    # -----------------------
    # For annual drift, let it range from -2% to +4% (commodities can be flat or negative)
    annual_drift = np.random.uniform(-0.10, 0.10)

    # For annual volatility, let it range from 30% to 60%
    annual_volatility = np.random.uniform(0.30, 0.60)

    initial_price = np.random.uniform(5000, 7000)

    # -----------------------
    # 2. Create date range (hourly, skipping weekends)
    # -----------------------
    # We'll start from a fixed date, say 1st Jan 2022
    start_date = datetime(2022, 1, 1)
    end_date = start_date + timedelta(days=365 * num_years)

    # Create an hourly range from start_date to end_date, but skip weekends
    current = start_date
    timestamps = []
    while current < end_date:
        # Monday=0, Sunday=6
        if current.weekday() < 5:  # 0-4 => Monday-Friday
            timestamps.append(current)
        current += timedelta(hours=1)

    # Number of data points
    n = len(timestamps)

    # -----------------------
    # 3. Prepare Arrays
    # -----------------------
    opens = np.zeros(n)
    highs = np.zeros(n)
    lows = np.zeros(n)
    closes = np.zeros(n)
    volumes = np.zeros(n)

    # The first open is initial_price
    opens[0] = initial_price
    closes[0] = initial_price
    highs[0] = initial_price
    lows[0] = initial_price

    # For hourly steps, delta_t in years is 1 / (365 * 24) approx
    delta_t = 1.0 / (365 * 24)

    # Pre-calculate for GBM
    mu_term = (annual_drift - 0.5 * annual_volatility**2) * delta_t
    sigma_term = annual_volatility * np.sqrt(delta_t)

    # Get random normal draws
    z = np.random.normal(0, 1, n)

    # -----------------------
    # 4. Generate Price Data
    # -----------------------
    for i in range(1, n):
        # The open price = previous close
        opens[i] = closes[i - 1]

        # GBM for close
        closes[i] = opens[i] * np.exp(mu_term + sigma_term * z[i])

        # High/Low around open-close range
        # Add random fluctuations
        price_range = abs(closes[i] - opens[i])
        # randomize a fluctuation between 0.001 and 0.01 of the price range
        rand_fluct = np.random.uniform(0.0001, 0.001) * np.random.lognormal(mean=0, sigma=0.75)
        high_fluct = np.random.uniform(0, price_range * rand_fluct)
        low_fluct = np.random.uniform(0, price_range * rand_fluct)

        highs[i] = max(opens[i], closes[i]) + high_fluct
        lows[i] = min(opens[i], closes[i]) - low_fluct

    # -----------------------
    # 5. Generate Volume Data
    # -----------------------
    # Very rough intraday pattern:
    # - Higher volume near "market open" (e.g., 9:00 - 10:00)
    # - Lower volume in the middle of the day
    # - Higher near "close" (e.g., 15:00 - 16:00)
    # Even though we're generating 24 hours, let's pretend "9-10" and "15-16" are "peak".

    for i, t in enumerate(timestamps):
        hour_of_day = t.hour
        # Basic pattern: peak hours (9-10, 15-16) get higher base volume
        if 9 <= hour_of_day < 10 or 15 <= hour_of_day < 16:
            base_volume = np.random.uniform(10000, 15000)
        else:
            base_volume = np.random.uniform(3000, 7000)

        # Random multiplier for extra variation
        multiplier = np.random.lognormal(mean=0, sigma=0.75)

        # Final volume
        volumes[i] = base_volume * multiplier

    # Convert volume to something smaller or bigger if you want, but let's keep it as is

    # -----------------------
    # 6. Build DataFrame
    # -----------------------
    df = pd.DataFrame(
        {
            "Gmt time": timestamps,
            "Open": opens,
            "High": highs,
            "Low": lows,
            "Close": closes,
            "Volume": volumes,
        }
    )

    return df


def save_to_csv(df, filename="simulated_stock_data.csv"):
    """Save the DataFrame to CSV in the desired format:
    dd.mm.yyyy HH:MM:SS.000,Open,High,Low,Close,Volume
    """
    df_to_save = df.copy()

    # Format timestamps
    df_to_save["Gmt time"] = df_to_save["Gmt time"].dt.strftime("%d.%m.%Y %H:%M:%S.000")

    # Round numeric columns for readability
    df_to_save["Open"] = df_to_save["Open"].round(4)
    df_to_save["High"] = df_to_save["High"].round(4)
    df_to_save["Low"] = df_to_save["Low"].round(4)
    df_to_save["Close"] = df_to_save["Close"].round(4)
    df_to_save["Volume"] = df_to_save["Volume"].round(4)

    df_to_save.to_csv(filename, index=False)


if __name__ == "__main__":
    # Example usage: generate 1 year of data (weekdays only)
    df_data = generate_realistic_stock_data(num_years=6)
    save_to_csv(df_data, "realistic_stock_data.csv")
    print("Simulation complete. Check 'realistic_stock_data.csv' for the output.")