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{ | |
"metadata": { | |
"name": "csv_conversion" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
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{ | |
"metadata": { | |
"name": "analysis_dbs" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
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{ |
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{ | |
"metadata": { | |
"name": "analyze_ratcliff" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
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def initialize(context): | |
fetch_csv('URL', | |
date_format='%d/%m/%y', | |
universe_func=my_universe) | |
def my_universe(context, fetcher_data): | |
my_stocks = set(fetcher_data['sid']) | |
# log the size of the universe for debugging | |
context.count = len(my_stocks) |
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import pandas as pd | |
from pandas.tseries.offsets import * | |
def rolling_window(data, resample='1BM', lookback=12 * BMonthEnd()): | |
dts = data.resample(resample).index | |
if lookback == 'aggregate': | |
for dt in dts: | |
yield data.loc[:dt] | |
else: |