| import pandas as pd |
| import numpy as np |
| import datetime |
|
|
|
|
| def create_mock_data(): |
| dates = pd.date_range(start="2024-10-01", end="2024-11-01", freq="D") |
| tickers = ["AAPL", "TSLA", "AMZN", "GOOG", "MSFT"] |
|
|
| rows = [] |
| for d in dates: |
| for t in tickers: |
| |
| entry = 10.0 + np.random.randn() |
| |
| rows.append( |
| { |
| "datetime": d, |
| "Ticker": t, |
| "premarket_change_from_perviousday_perc": 10.0 + np.random.randn(), |
| "premarket_close": entry, |
| "Shares Float": 2e6, |
| "Market Capitalization": 50e6, |
| "marketsession_1min": entry * (1 - 0.01 * np.random.randn()), |
| "marketsession_3min": entry * (1 - 0.02 * np.random.randn()), |
| "marketsession_5min": entry * (1 - 0.03 * np.random.randn()), |
| "marketsession_10min": entry * (1 - 0.04 * np.random.randn()), |
| "marketsession_15min": entry * (1 - 0.05 * np.random.randn()), |
| "marketsession_30min": entry * (1 - 0.06 * np.random.randn()), |
| "marketsession_60min": entry * (1 - 0.07 * np.random.randn()), |
| "marketsession_120min": entry * (1 - 0.08 * np.random.randn()), |
| "marketsession_high": entry * 1.1, |
| "marketsession_close": entry * 0.9, |
| } |
| ) |
|
|
| df = pd.DataFrame(rows) |
| filename = "marketsession_post_polygon_2020-01-01_2026-01-01.parquet_with_premarketvolume900K_marketcap1B.parquet" |
| df.to_parquet(filename) |
| print(f"Mock data created: {filename}") |
|
|
|
|
| if __name__ == "__main__": |
| create_mock_data() |
|
|
