digital_asset_sharpe.py

# helper functions
def to_df(response):
    "Turns the repsonse into a dataframe"
    # load the data into a DataFrame and rename the columns
    data = pd.DataFrame(response["data"]).rename({0: "date", 1: "volatility", 2: "sharpe"}, axis=1)
    # turn the date column into datetime type
    data["date"] = pd.to_datetime(data["date"])
    # set the date as the index
    data.set_index("date", inplace=True)
    return data

def get_sharpe(token):
    "Gets the Sharpe Ratio for the token"
    # the URL for the response
    url = f"https://web3api.io/api/v2/market/metrics/{token}/historical/sharpe"
    # get the response
    response = get_response(url, headers)
    try:
        return response
    except TypeError:
        print("No Response")

# the Sharpe Data querystring
querystring = {
    "startDate": start,
    "endDate": end,
    "timeFrame": "1d"
}

# the list of tokens we wish to get data for
tokens = ['btc', 'eth', 'link', 'xlm', 'bch']
# a dictionary to save our returned data
results = {}

# lets iterate over the tokens of interest
for token in tokens:
    # get the token name in lower case
    token = token.lower()
    # get the result
    token_sharpe = get_sharpe(token)
    # if we got a response
    if token_sharpe:
        # if the response has data
        if token_sharpe["data"]:
            # save the result as a dataframe
            results[token] = to_df(token_sharpe)