Converting List to DataFrame for Position Management in Python

As a cryptocurrency trader, having accurate and organized data is crucial to making informed decisions. In this article, we will explore how to convert a price list from the Binance Futures API into a pandas DataFrame that can be used for position management.

Prerequisites:

• Install the binance_f library using pip: pip install binance_f

• Set up your Binance API credentials

• Import the required libraries and set your API key

Code:

from binance_f import RequestClient, OrderBook





Set up API credentials and client instance
api_key = 'your_api_key'
api_secret = 'your_api_secret'
request_client = RequestClient(api_key=api_key, api_secret=api_secret)
def convert_to_df(prices):
"""
Convert a list of prices to a pandas DataFrame.
Parameters:
prices(list): List of prices to convert
Returns:
pd.DataFrame: DataFrame converted
"""
order_book = request_client.get_orderbook('BTCUSDT')

Create a dictionary to store price and volume data
data = {
'price': [],
'volume': []
}
for entry in order_book.entries:
if entry.price > entry.volume:
data['price'].append(entry.price)
data['volume'].append(entry.volume)
df = pd.DataFrame(data)
return df

Usage example
prices = [100.0, 120.0, 110.0, 130.0, 115.0] 
example of prices for BTC-USD
df = convert_to_df(prices)
print(df)

Explanation:

• First we import the necessary libraries and set up our API credentials.

• Create a RequestClient instance using our API key and secret.

• The convert_to_df() function takes a list of prices as input and uses the Binance Futures API to retrieve an order book entry for each price.

• For each entry, we append the price and volume data to a dictionary (data).

• Create a pandas DataFrame from the dictionary and return it.

• In the example usage section, we demonstrate how to use convert_to_df() with a list of prices.

Tips and Variations:

• You can modify the convert_to_df() function to accommodate different types of price data (e.g. candlestick charts).

• If you need to process additional data (e.g. trend analysis), you may consider using a more advanced library like pandas-datareader.

• To optimize performance, consider caching your API requests or using a queue-based approach to handle high-volume data.

By following this article and adapting it to your specific needs, you can efficiently convert your price list into a pandas DataFrame for position management in Python.

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