Download Market Data

Overview

The download function is a utility provided by the Investing Algorithm Framework that allows you to retrieve historical market data for specific symbols (assets), including OHLCV data (Open, High, Low, Close, Volume), ticker data, and more. This function is particularly useful for:

Backtesting trading strategies
Performing exploratory data analysis
Preprocessing input data for machine learning models

Function Signature

def download(
    symbol: str,
    market=None,
    date=None,
    time_frame: str = None,
    data_type: str = "ohlcv",
    start_date: str = None,
    end_date: str = None,
    window_size: int = 200,
    pandas: bool = True,
    save: bool = True,
    storage_path: str = None,
) -> Union[pandas.DataFrame, polars.DataFrame]

Parameter	Type	Description
`symbol`	`str`	The symbol (e.g., `"BTC/USDT"`) for which data is downloaded.
`market`	`str`	(Optional) The market to download data from (e.g., `"binance"`).
`date`	`str`	(Optional) Specific date to retrieve data for.
`time_frame`	`str`	The time frame for data (e.g., `"1d"`, `"1h"`).
`data_type`	`str`	Type of data to retrieve: `"ohlcv"` (default) or `"ticker"`.
`start_date`	`str`	(Optional) Start of the date range to retrieve data.
`end_date`	`str`	(Optional) End of the date range.
`window_size`	`int`	Number of records to retrieve (default: 200).
`pandas`	`bool`	If `True`, returns a `pandas.DataFrame`; otherwise returns a `polars.DataFrame`.
`save`	`bool`	If `True`, saves the downloaded data to disk.
`storage_path`	`str`	(Optional) Path to store the data when save=True. Also used to load from disk if data already exists.

Returns

The function returns a DataFrame (pandas or polars) containing the requested market data, ready for analysis, visualization, or model training.

How It Works with storage_path

When a storage_path is provided, the function first checks if the requested data already exists at the specified path. If the file is found and readable, the function loads it directly from disk, avoiding unnecessary network calls. If not, it downloads the data and saves it to the path (if save=True).

This makes repeated experiments much faster and reduces API usage and rate-limiting issues.

Why It's Useful?

This function streamlines the process of acquiring market data by:

Automatically selecting the correct data provider
Supporting multiple formats (pandas or polars)
Handling flexible input dates and ranges
Offering a simple way to persist downloaded data to disk

Example Use Cases

📈 Backtesting a Strategy

df = download("BTC/USDT", market="binance", time_frame="1d", start_date="2021-01-01", end_date="2022-01-01")

Use the returned df to simulate your strategy’s performance over historical data.

🧠 Preparing Data for Machine Learning

df = download("ETH/USDT", market="binance", time_frame="1h", window_size=500, pandas=True)
features = df[["close", "volume"]]

This enables quick preparation of time-series datasets for supervised learning tasks.

💾 Saving Data for Offline Analysis

download("SOL/USDT", market="binance", time_frame="1d", save=True, storage_path="./data/")

Overview​

Function Signature​

Returns​

How It Works with storage_path​

Why It's Useful?​

Example Use Cases​

🧠 Preparing Data for Machine Learning​

💾 Saving Data for Offline Analysis​