ccerv1’s gists

ccerv1 / load_real_prices_data.py

Created April 26, 2019 05:14

Ingests our three datasets for our price analysis

	import pandas as pd

	parent_url = 'https://raw.githubusercontent.com/numbers-coffee/blog/master/real_prices/'
	currency = pd.read_csv(parent_url + 'currency.csv')
	inflation = pd.read_csv(parent_url + 'inflation.csv')
	prices = pd.read_csv(parent_url + 'prices.csv')

ccerv1 / clean_dates.py

Created April 26, 2019 05:22

Use strptime to standardize our date formats

	from datetime import datetime

	date_format = '%Y-%m-%dT%H:%M:%S'
	currency['Date'] = currency['DateTime'].apply(lambda x: datetime.strptime(x, date_format))
	inflation['Date'] = inflation['DateTime'].apply(lambda x: datetime.strptime(x, date_format))
	prices['Date'] = prices['DATE'].apply(lambda x: datetime.strptime(x, '%m/%d/%y'))

ccerv1 / continuous_date_series.py

Created April 26, 2019 05:27

Create a DataFrame with a continuous series of dates

	from datetime import timedelta

	start_date = datetime(1999, 4, 1)
	end_date = datetime(2019, 3, 31)
	this_date = start_date
	date_range = []
	while this_date <= end_date:
	date_range.append(this_date)
	this_date += timedelta(1)

ccerv1 / merge_asof_real_prices.py

Created April 26, 2019 05:32

Merges are three datasets onto a continuous date series

	df = pd.merge_asof(continuous_dates, currency[['Date', 'Value']], on='Date', direction='nearest')
	df = pd.merge_asof(df, inflation[['Date', 'Value']], on='Date', direction='forward')
	df = pd.merge_asof(df, prices[['Date', 'CLOSE']], on='Date', direction='backward')

ccerv1 / format_real_price_df.py

Created April 26, 2019 05:34

Apply some formatting so our table looks nice

	df.set_index('Date', inplace=True)
	df.rename(columns={
	'Value_x': 'FX',
	'Value_y': 'Inflation',
	'CLOSE': 'C Market'
	}, inplace=True)

ccerv1 / index_to_date.py

Created August 15, 2019 13:38

Takes a date we want to index to and a series of values, and returns a new series that shows the ratio of historical values to the current (or indexed) value

	index_date = datetime(2019,3,31)

	def index_to_date(date, series):
	index_val = series.loc[date]
	indexed_series = series / index_val
	return indexed_series

	df['FX Index'] = index_to_date(index_date, df['FX'])
	df.plot(kind='line', y=['FX', 'FX Index'], secondary_y='FX', alpha=.5)

ccerv1 / index_annual_rate_to_date.py

Created August 15, 2019 13:43

Converts annualized rates to daily rates, and indexes them to a given date

	def index_annual_rate_to_date(date, series):
	daily_rate = (1 + series/100) ** (1/365)
	cumulative_rate = daily_rate.sort_index(ascending=False).cumprod()
	return index_to_date(date, cumulative_rate)

	df['Inflation Index'] = index_annual_rate_to_date(index_date, df['Inflation'])
	df.plot(kind='line', y=['Inflation', 'Inflation Index'], secondary_y='Inflation')

ccerv1 / real_c_prices.py

Created August 15, 2019 13:46

Convert nominal to real coffee prices

	df['C Market (Real)'] = df['C Market'] * df['FX Index'] * df['Inflation Index']
	df.rename(columns={'C Market': 'C Market (Nominal)'}, inplace=True)

ccerv1 / plot_real_nominal_prices.py

Created August 15, 2019 13:48

Plot real and nominal prices in the same graph

	import matplotlib.pyplot as plt

	fig, ax = plt.subplots(figsize=(20,8))
	df.plot(kind='line', y=['C Market (Real)', 'C Market (Nominal)'], ax=ax)

	current_price = df.iloc[-1]['C Market (Nominal)']
	ax.axhline(current_price, color='black')

ccerv1 / RPGF3_attestation_fetcher.py

Last active October 23, 2023 17:26

RPGF3 Attestations

	import json
	import os
	import requests


	EAS_SCHEMA = "0x76e98cce95f3ba992c2ee25cef25f756495147608a3da3aa2e5ca43109fe77cc"
	START_TIME = 0
	RAW_APPLICANT_JSON = "raw_applicant_data.json"
	CLEANED_APPLICANT_JSON = "cleaned_applicant_data.json"

Carl Cervone ccerv1