mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-30 03:45:15 +08:00
Eddie Hebert
08bc42dc0c
Indexes to risk answers were pointing to a previous version. Also, provide the risk cumulative answers as a pd.Series, so that it is easier to compare to values produced by risk class.
293 lines
10 KiB
Python
293 lines
10 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import datetime
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import hashlib
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import os
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import numpy as np
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import pandas as pd
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import pytz
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import xlrd
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import requests
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def col_letter_to_index(col_letter):
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# Only supports single letter,
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# but answer key doesn't need multi-letter, yet.
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index = 0
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for i, char in enumerate(reversed(col_letter)):
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index += ((ord(char) - 65) + 1) * pow(26, i)
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return index
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DIR = os.path.dirname(os.path.realpath(__file__))
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ANSWER_KEY_CHECKSUMS_PATH = os.path.join(DIR, 'risk-answer-key-checksums')
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ANSWER_KEY_CHECKSUMS = open(ANSWER_KEY_CHECKSUMS_PATH, 'r').read().splitlines()
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ANSWER_KEY_FILENAME = 'risk-answer-key.xlsx'
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ANSWER_KEY_PATH = os.path.join(DIR, ANSWER_KEY_FILENAME)
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ANSWER_KEY_BUCKET_NAME = 'zipline-test_data'
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ANSWER_KEY_DL_TEMPLATE = """
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https://s3.amazonaws.com/zipline-test-data/risk/{md5}/risk-answer-key.xlsx
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""".strip()
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LATEST_ANSWER_KEY_URL = ANSWER_KEY_DL_TEMPLATE.format(
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md5=ANSWER_KEY_CHECKSUMS[-1])
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def answer_key_signature():
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with open(ANSWER_KEY_PATH, 'r') as f:
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md5 = hashlib.md5()
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while True:
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buf = f.read(1024)
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if not buf:
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break
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md5.update(buf)
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return md5.hexdigest()
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def ensure_latest_answer_key():
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"""
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Get the latest answer key from a publically available location.
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Logic for determining what and when to download is as such:
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- If there is no local spreadsheet file, then get the lastest answer key,
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as defined by the last row in the checksum file.
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- If there is a local spreadsheet file:
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-- If the spreadsheet's checksum is in the checksum file:
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--- If the spreadsheet's checksum does not match the latest, then grab the
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the latest checksum and replace the local checksum file.
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--- If the spreadsheet's checksum matches the latest, then skip download,
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and use the local spreadsheet as a cached copy.
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-- If the spreadsheet's checksum is not in the checksum file, then leave
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the local file alone, assuming that the local xls's md5 is not in the list
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due to local modifications during development.
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It is possible that md5's could collide, if that is ever case, we should
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then find an alternative naming scheme.
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The spreadsheet answer sheet is not kept in SCM, as every edit would
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increase the repo size by the file size, since it is treated as a binary.
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"""
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answer_key_dl_checksum = None
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local_answer_key_exists = os.path.exists(ANSWER_KEY_PATH)
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if local_answer_key_exists:
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local_hash = answer_key_signature()
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if local_hash in ANSWER_KEY_CHECKSUMS:
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# Assume previously downloaded version.
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# Check for latest.
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if local_hash != ANSWER_KEY_CHECKSUMS[-1]:
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# More recent checksum, download
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answer_key_dl_checksum = ANSWER_KEY_CHECKSUMS[-1]
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else:
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# Assume local copy that is being developed on
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answer_key_dl_checksum = None
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else:
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answer_key_dl_checksum = ANSWER_KEY_CHECKSUMS[-1]
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if answer_key_dl_checksum:
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res = requests.get(
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ANSWER_KEY_DL_TEMPLATE.format(md5=answer_key_dl_checksum))
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with open(ANSWER_KEY_PATH, 'w') as f:
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f.write(res.content)
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# Get latest answer key on load.
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ensure_latest_answer_key()
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class DataIndex(object):
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"""
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Coordinates for the spreadsheet, using the values as seen in the notebook.
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The python-excel libraries use 0 index, while the spreadsheet in a GUI
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uses a 1 index.
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"""
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def __init__(self, sheet_name, col, row_start, row_end,
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value_type='float'):
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self.sheet_name = sheet_name
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self.col = col
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self.row_start = row_start
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self.row_end = row_end
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self.value_type = value_type
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@property
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def col_index(self):
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return col_letter_to_index(self.col) - 1
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@property
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def row_start_index(self):
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return self.row_start - 1
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@property
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def row_end_index(self):
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return self.row_end - 1
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def __str__(self):
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return "'{sheet_name}'!{col}{row_start}:{col}{row_end}".format(
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sheet_name=self.sheet_name,
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col=self.col,
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row_start=self.row_start,
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row_end=self.row_end
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)
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class AnswerKey(object):
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INDEXES = {
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'RETURNS': DataIndex('Sim Period', 'D', 4, 255),
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'BENCHMARK': {
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'Dates': DataIndex('s_p', 'A', 4, 254, value_type='date'),
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'Returns': DataIndex('s_p', 'H', 4, 254)
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},
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# Below matches the inconsistent capitalization in spreadsheet
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'BENCHMARK_PERIOD_RETURNS': {
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'Monthly': DataIndex('s_p', 'P', 8, 19),
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'3-Month': DataIndex('s_p', 'Q', 10, 19),
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'6-month': DataIndex('s_p', 'R', 13, 19),
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'year': DataIndex('s_p', 'S', 19, 19),
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},
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'BENCHMARK_PERIOD_VOLATILITY': {
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'Monthly': DataIndex('s_p', 'T', 8, 19),
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'3-Month': DataIndex('s_p', 'U', 10, 19),
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'6-month': DataIndex('s_p', 'V', 13, 19),
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'year': DataIndex('s_p', 'W', 19, 19),
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},
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'ALGORITHM_PERIOD_RETURNS': {
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'Monthly': DataIndex('Sim Period', 'V', 23, 34),
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'3-Month': DataIndex('Sim Period', 'W', 25, 34),
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'6-month': DataIndex('Sim Period', 'X', 28, 34),
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'year': DataIndex('Sim Period', 'Y', 34, 34),
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},
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'ALGORITHM_PERIOD_VOLATILITY': {
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'Monthly': DataIndex('Sim Period', 'Z', 23, 34),
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'3-Month': DataIndex('Sim Period', 'AA', 25, 34),
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'6-month': DataIndex('Sim Period', 'AB', 28, 34),
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'year': DataIndex('Sim Period', 'AC', 34, 34),
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},
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'ALGORITHM_PERIOD_SHARPE': {
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'Monthly': DataIndex('Sim Period', 'AD', 23, 34),
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'3-Month': DataIndex('Sim Period', 'AE', 25, 34),
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'6-month': DataIndex('Sim Period', 'AF', 28, 34),
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'year': DataIndex('Sim Period', 'AG', 34, 34),
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},
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'ALGORITHM_PERIOD_BETA': {
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'Monthly': DataIndex('Sim Period', 'AH', 23, 34),
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'3-Month': DataIndex('Sim Period', 'AI', 25, 34),
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'6-month': DataIndex('Sim Period', 'AJ', 28, 34),
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'year': DataIndex('Sim Period', 'AK', 34, 34),
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},
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'ALGORITHM_PERIOD_ALPHA': {
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'Monthly': DataIndex('Sim Period', 'AL', 23, 34),
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'3-Month': DataIndex('Sim Period', 'AM', 25, 34),
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'6-month': DataIndex('Sim Period', 'AN', 28, 34),
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'year': DataIndex('Sim Period', 'AO', 34, 34),
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},
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'ALGORITHM_PERIOD_BENCHMARK_VARIANCE': {
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'Monthly': DataIndex('Sim Period', 'BB', 23, 34),
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'3-Month': DataIndex('Sim Period', 'BC', 25, 34),
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'6-month': DataIndex('Sim Period', 'BD', 28, 34),
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'year': DataIndex('Sim Period', 'BE', 34, 34),
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},
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'ALGORITHM_PERIOD_COVARIANCE': {
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'Monthly': DataIndex('Sim Period', 'AX', 23, 34),
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'3-Month': DataIndex('Sim Period', 'AY', 25, 34),
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'6-month': DataIndex('Sim Period', 'AZ', 28, 34),
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'year': DataIndex('Sim Period', 'BA', 34, 34),
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},
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'ALGORITHM_RETURN_VALUES': DataIndex(
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'Sim Cumulative', 'D', 4, 254),
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'ALGORITHM_CUMULATIVE_VOLATILITY': DataIndex(
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'Sim Cumulative', 'O', 4, 254),
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'ALGORITHM_CUMULATIVE_SHARPE': DataIndex(
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'Sim Cumulative', 'R', 4, 254)
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}
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def __init__(self):
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self.workbook = xlrd.open_workbook(ANSWER_KEY_PATH)
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self.sheets = {}
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self.sheets['Sim Period'] = self.workbook.sheet_by_name('Sim Period')
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self.sheets['Sim Cumulative'] = self.workbook.sheet_by_name(
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'Sim Cumulative')
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self.sheets['s_p'] = self.workbook.sheet_by_name('s_p')
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for name, index in self.INDEXES.items():
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if isinstance(index, dict):
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subvalues = {}
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for subkey, subindex in index.items():
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subvalues[subkey] = self.get_values(subindex)
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setattr(self, name, subvalues)
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else:
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setattr(self, name, self.get_values(index))
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def parse_date_value(self, value):
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return xlrd.xldate_as_tuple(value, 0)
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def parse_float_value(self, value):
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return value if value != '' else np.nan
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def get_raw_values(self, data_index):
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return self.sheets[data_index.sheet_name].col_values(
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data_index.col_index,
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data_index.row_start_index,
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data_index.row_end_index + 1)
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@property
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def value_type_to_value_func(self):
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return {
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'float': self.parse_float_value,
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'date': self.parse_date_value,
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}
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def get_values(self, data_index):
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value_parser = self.value_type_to_value_func[data_index.value_type]
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return map(value_parser, self.get_raw_values(data_index))
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ANSWER_KEY = AnswerKey()
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BENCHMARK_DATES = ANSWER_KEY.BENCHMARK['Dates']
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BENCHMARK_RETURNS = ANSWER_KEY.BENCHMARK['Returns']
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DATES = [datetime.datetime(*x, tzinfo=pytz.UTC) for x in BENCHMARK_DATES]
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BENCHMARK = pd.Series(dict(zip(DATES, BENCHMARK_RETURNS)))
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ALGORITHM_RETURNS = pd.Series(
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dict(zip(DATES, ANSWER_KEY.ALGORITHM_RETURN_VALUES)))
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RETURNS_DATA = pd.DataFrame({'Benchmark Returns': BENCHMARK,
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'Algorithm Returns': ALGORITHM_RETURNS})
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RISK_CUMULATIVE = pd.DataFrame({
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'volatility': pd.Series(dict(zip(
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DATES, ANSWER_KEY.ALGORITHM_CUMULATIVE_VOLATILITY))),
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'sharpe': pd.Series(dict(zip(
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DATES, ANSWER_KEY.ALGORITHM_CUMULATIVE_SHARPE)))})
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