Error using Santiment sanpy library for cryptocurrency data analysis

Question

I am using it simply to gather crypto market data, compute alpha, beta and R-squared with statsmodels, and then create a crypto = input("Cryptocurrency: ") function with a while loop that allows me to ask the user for specific crypto and output its respective statistics, followed by showing the input again.

With the following code, I receive the error: ValueError: If using all scalar values, you must pass an index

import san import numpy as np import matplotlib.pyplot as plt import pandas as pd import datetime import statsmodels.api as sm from statsmodels import regression cryptos = ["bitcoin", "ethereum", "ripple", "bitcoin-cash", "tether", "bitcoin-sv", "litecoin", "binance-coin", "eos", "chainlink", "monero", "bitcoin-gold"] def get_and_process_data(c): raw_data = san.get("daily_closing_price_usd/" + c, from_date="2014-12-31", to_date="2019-12-31", interval="1d") # "query/slug" return raw_data.pct_change()[1:] df = pd.DataFrame({c: get_and_process_data(c) for c in cryptos}) df['MKT Return'] = df.mean(axis=1) # avg market return #print(df) # show dataframe with all data def model(x, y): # Calculate r-squared X = sm.add_constant(x) # artificially add intercept to x, as advised in the docs model = sm.OLS(y,X).fit() rsquared = model.rsquared # Fit linear regression and calculate alpha and beta X = sm.add_constant(x) model = regression.linear_model.OLS(y,X).fit() alpha = model.params[0] beta = model.params[1] return rsquared, alpha, beta results = pd.DataFrame({c: model(df[df[c].notnull()]['MKT Return'], df[df[c].notnull()][c]) for c in cryptos}).transpose() results.columns = ['rsquared', 'alpha', 'beta'] print(results)

The error is in the following line:

df = pd.DataFrame({c: get_and_process_data(c) for c in cryptos})

I tried solving the issue by changing it to:

df = {c: get_and_process_data(c) for c in cryptos} df['MKT Return'] = df.mean(axis=1) # avg market return print(df) # show dataframe with all data

But with that, it gave me a different error: AttributeError: 'dict' object has no attribute 'mean'.

The goal is to create a single DataFrame with the DateTime column, columns for the cryptos and their pct.change data, an additional column for MKT Return with the daily mean from all cryptos' pct.change. Then, use all this data to calculate each crypto's statistics and finally create the input function mentioned at the beginning.

I hope I made myself clear and that someone is able to help me with this matter.

Answer 1

This is a great start, but I think that you are getting confused with the return from san. If you look at the following:-

import san import pandas as pd # List of data we are interested in cryptos = ["bitcoin", "ethereum", "ripple", "bitcoin-cash", "tether", "bitcoin-sv", "litecoin", "binance-coin", "eos", "chainlink", "monero", "bitcoin-gold"] # function to get the data from san into a dataframe and turn in into # a daily percentage change def get_and_process_data(c): raw_data = san.get("daily_closing_price_usd/" + c, from_date="2014-12-31", to_date="2019-12-31", interval="1d") # "query/slug" return raw_data.pct_change()[1:] # now set up an empty dataframe to get all the data put into df = pd.DataFrame() # cycle through your list for c in cryptos: # get the data as percentage changes dftemp = get_and_process_data(c) # then add it to the output dataframe df df[c] = dftemp['value'] # have a look at what you have print(df)

And from that point on you know you have some good data and you can play with it as you go forward.

If I could suggest that you just get one currency and get the regressions working with that one then move forward to cycling through all of them.