/
test_cache.py
1897 lines (1688 loc) · 62.4 KB
/
test_cache.py
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# Copyright 2018 QuantRocket LLC - All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# To run: python3 -m unittest discover -s tests/ -p test_*.py -t . -v
import os
import unittest
from unittest.mock import patch
import glob
import pickle
from pathlib import Path
import inspect
import pandas as pd
import numpy as np
from moonshot import Moonshot, MoonshotML
from moonshot.cache import TMP_DIR
from quantrocket.exceptions import ImproperlyConfigured
from sklearn.tree import DecisionTreeClassifier
class HistoricalPricesCacheTestCase(unittest.TestCase):
def test_10_complain_if_houston_not_set(self):
"""
Tests that a "HOUSTON_URL not set" error is raised if a backtest is
run without mock. This is a control for later tests.
"""
# clear cache dir if any pickles are hanging around
files = glob.glob("{0}/moonshot_*.pkl".format(TMP_DIR))
for file in files:
os.remove(file)
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
with self.assertRaises(ImproperlyConfigured) as cm:
BuyBelow10().backtest()
self.assertIn("HOUSTON_URL is not set", repr(cm.exception))
def test_20_load_history_from_mock(self):
"""
Runs a strategy using mock to fill the history cache.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
def mock_get_prices(*args, **kwargs):
dt_idx = pd.DatetimeIndex(["2018-05-01","2018-05-02","2018-05-03", "2018-05-04"])
fields = ["Close","Volume"]
idx = pd.MultiIndex.from_product([fields, dt_idx], names=["Field", "Date"])
prices = pd.DataFrame(
{
"FI12345": [
# Close
9,
11,
10.50,
9.99,
# Volume
5000,
16000,
8800,
9900
],
"FI23456": [
# Close
9.89,
11,
8.50,
10.50,
# Volume
15000,
14000,
28800,
17000
],
},
index=idx
)
return prices
def mock_download_master_file(f, *args, **kwargs):
master_fields = ["Timezone", "Symbol", "SecType", "Currency", "PriceMagnifier", "Multiplier"]
securities = pd.DataFrame(
{
"FI12345": [
"America/New_York",
"ABC",
"STK",
"USD",
None,
None
],
"FI23456": [
"America/New_York",
"DEF",
"STK",
"USD",
None,
None,
]
},
index=master_fields
)
securities.columns.name = "Sid"
securities.T.to_csv(f, index=True, header=True)
f.seek(0)
with patch("moonshot.strategies.base.get_prices", new=mock_get_prices):
with patch("moonshot.strategies.base.download_master_file", new=mock_download_master_file):
results = BuyBelow10().backtest(end_date="2018-05-04")
self.assertSetEqual(
set(results.index.get_level_values("Field")),
{'Commission',
'AbsExposure',
'Signal',
'Return',
'Slippage',
'NetExposure',
'TotalHoldings',
'Turnover',
'AbsWeight',
'Weight'}
)
# replace nan with "nan" to allow equality comparisons
results = results.round(7)
results = results.where(results.notnull(), "nan")
signals = results.loc["Signal"].reset_index()
signals.loc[:, "Date"] = signals.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
signals.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [1.0,
0.0,
0.0,
1.0],
"FI23456": [1.0,
0.0,
1.0,
0.0]}
)
weights = results.loc["Weight"].reset_index()
weights.loc[:, "Date"] = weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
abs_weights = results.loc["AbsWeight"].reset_index()
abs_weights.loc[:, "Date"] = abs_weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
net_positions = results.loc["NetExposure"].reset_index()
net_positions.loc[:, "Date"] = net_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
net_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
abs_positions = results.loc["AbsExposure"].reset_index()
abs_positions.loc[:, "Date"] = abs_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
turnover = results.loc["Turnover"].reset_index()
turnover.loc[:, "Date"] = turnover.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
turnover.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.5,
0.0],
"FI23456": ["nan",
0.5,
0.5,
1.0]}
)
commissions = results.loc["Commission"].reset_index()
commissions.loc[:, "Date"] = commissions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
commissions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
slippage = results.loc["Slippage"].reset_index()
slippage.loc[:, "Date"] = slippage.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
slippage.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
returns = results.loc["Return"]
returns = returns.reset_index()
returns.loc[:, "Date"] = returns.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
returns.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
-0.0227273, # (10.50 - 11)/11 * 0.5
-0.0],
"FI23456": [0.0,
0.0,
-0.1136364, # (8.50 - 11)/11 * 0.5
0.0]}
)
def test_30_load_history_from_cache(self):
"""
Re-Runs the strategy without using mock to show that the history
cache is used.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
results = BuyBelow10().backtest(end_date="2018-05-04")
self.assertSetEqual(
set(results.index.get_level_values("Field")),
{'Commission',
'AbsExposure',
'Signal',
'Return',
'Slippage',
'NetExposure',
'TotalHoldings',
'Turnover',
'AbsWeight',
'Weight'}
)
# replace nan with "nan" to allow equality comparisons
results = results.round(7)
results = results.where(results.notnull(), "nan")
signals = results.loc["Signal"].reset_index()
signals.loc[:, "Date"] = signals.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
signals.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [1.0,
0.0,
0.0,
1.0],
"FI23456": [1.0,
0.0,
1.0,
0.0]}
)
weights = results.loc["Weight"].reset_index()
weights.loc[:, "Date"] = weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
abs_weights = results.loc["AbsWeight"].reset_index()
abs_weights.loc[:, "Date"] = abs_weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
net_positions = results.loc["NetExposure"].reset_index()
net_positions.loc[:, "Date"] = net_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
net_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
abs_positions = results.loc["AbsExposure"].reset_index()
abs_positions.loc[:, "Date"] = abs_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
turnover = results.loc["Turnover"].reset_index()
turnover.loc[:, "Date"] = turnover.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
turnover.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.5,
0.0],
"FI23456": ["nan",
0.5,
0.5,
1.0]}
)
commissions = results.loc["Commission"].reset_index()
commissions.loc[:, "Date"] = commissions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
commissions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
slippage = results.loc["Slippage"].reset_index()
slippage.loc[:, "Date"] = slippage.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
slippage.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
returns = results.loc["Return"]
returns = returns.reset_index()
returns.loc[:, "Date"] = returns.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
returns.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
-0.0227273, # (10.50 - 11)/11 * 0.5
-0.0],
"FI23456": [0.0,
0.0,
-0.1136364, # (8.50 - 11)/11 * 0.5
0.0]}
)
def test_40_dont_use_cache_if_different_params(self):
"""
Re-runs the strategy without using mock and specifying different DB
parameters so as not to use the cache, which should trigger
ImproperlyConfigured.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
DB_FIELDS = ["Open"]
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
with self.assertRaises(ImproperlyConfigured) as cm:
BuyBelow10().backtest(end_date="2018-05-04")
self.assertIn("HOUSTON_URL is not set", repr(cm.exception))
def test_50_dont_use_cache_if_no_cache(self):
"""
Re-runs the strategy without using mock and with the same DB
parameters but with no_cache=True, which should not use the cache and
thus should trigger ImproperlyConfigured.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
with self.assertRaises(ImproperlyConfigured) as cm:
BuyBelow10().backtest(end_date="2018-05-04", no_cache=True)
self.assertIn("HOUSTON_URL is not set", repr(cm.exception))
def test_60_use_cache_if_end_date_and_db_modified(self):
"""
Tests that if an end date is specified, the cache is used, even
though we pretend that the db was modified after the file was cached.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
def mock_list_databases(**kwargs):
return {
"postgres": [],
"sqlite": [{'last_modified': "2015-01-01T13:45:00",
'name': 'quantrocket.history.my-db1.sqlite',
'path': '/var/lib/quantrocket/quantrocket.history.my-db1.sqlite',
'size_in_mb': 3.1},
# Database was recently modified (in future)
{'last_modified': (pd.Timestamp.now() + pd.Timedelta(seconds=60)).isoformat(),
'name': 'quantrocket.history.my-db2.sqlite',
'path': '/var/lib/quantrocket/quantrocket.history.my-db2.sqlite',
'size_in_mb': 2.1},
]}
with patch("moonshot.cache.list_databases", new=mock_list_databases):
results = BuyBelow10().backtest(end_date="2018-05-04")
self.assertSetEqual(
set(results.index.get_level_values("Field")),
{'Commission',
'AbsExposure',
'Signal',
'Return',
'Slippage',
'NetExposure',
'TotalHoldings',
'Turnover',
'AbsWeight',
'Weight'}
)
# replace nan with "nan" to allow equality comparisons
results = results.round(7)
results = results.where(results.notnull(), "nan")
signals = results.loc["Signal"].reset_index()
signals.loc[:, "Date"] = signals.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
signals.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [1.0,
0.0,
0.0,
1.0],
"FI23456": [1.0,
0.0,
1.0,
0.0]}
)
weights = results.loc["Weight"].reset_index()
weights.loc[:, "Date"] = weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
abs_weights = results.loc["AbsWeight"].reset_index()
abs_weights.loc[:, "Date"] = abs_weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
net_positions = results.loc["NetExposure"].reset_index()
net_positions.loc[:, "Date"] = net_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
net_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
abs_positions = results.loc["AbsExposure"].reset_index()
abs_positions.loc[:, "Date"] = abs_positions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
abs_positions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.0,
0.0],
"FI23456": ["nan",
0.5,
0.0,
1.0]}
)
turnover = results.loc["Turnover"].reset_index()
turnover.loc[:, "Date"] = turnover.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
turnover.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": ["nan",
0.5,
0.5,
0.0],
"FI23456": ["nan",
0.5,
0.5,
1.0]}
)
commissions = results.loc["Commission"].reset_index()
commissions.loc[:, "Date"] = commissions.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
commissions.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
slippage = results.loc["Slippage"].reset_index()
slippage.loc[:, "Date"] = slippage.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
slippage.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
0.0,
0.0],
"FI23456": [0.0,
0.0,
0.0,
0.0]}
)
returns = results.loc["Return"]
returns = returns.reset_index()
returns.loc[:, "Date"] = returns.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
returns.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
-0.0227273, # (10.50 - 11)/11 * 0.5
-0.0],
"FI23456": [0.0,
0.0,
-0.1136364, # (8.50 - 11)/11 * 0.5
0.0]}
)
def test_70_load_history_from_mock(self):
"""
Runs a strategy with no end date, using mock, to fill the history
cache. This test is setup for later tests.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
def mock_get_prices(*args, **kwargs):
dt_idx = pd.DatetimeIndex(["2018-05-01","2018-05-02","2018-05-03", "2018-05-04"])
fields = ["Close","Volume"]
idx = pd.MultiIndex.from_product([fields, dt_idx], names=["Field", "Date"])
prices = pd.DataFrame(
{
"FI12345": [
# Close
9,
11,
10.50,
9.99,
# Volume
5000,
16000,
8800,
9900
],
"FI23456": [
# Close
9.89,
11,
8.50,
10.50,
# Volume
15000,
14000,
28800,
17000
],
},
index=idx
)
return prices
def mock_download_master_file(f, *args, **kwargs):
master_fields = ["Timezone", "Symbol", "SecType", "Currency", "PriceMagnifier", "Multiplier"]
securities = pd.DataFrame(
{
"FI12345": [
"America/New_York",
"ABC",
"STK",
"USD",
None,
None
],
"FI23456": [
"America/New_York",
"DEF",
"STK",
"USD",
None,
None,
]
},
index=master_fields
)
securities.columns.name = "Sid"
securities.T.to_csv(f, index=True, header=True)
f.seek(0)
with patch("moonshot.strategies.base.get_prices", new=mock_get_prices):
with patch("moonshot.strategies.base.download_master_file", new=mock_download_master_file):
results = BuyBelow10().backtest()
self.assertSetEqual(
set(results.index.get_level_values("Field")),
{'Commission',
'AbsExposure',
'Signal',
'Return',
'Slippage',
'NetExposure',
'TotalHoldings',
'Turnover',
'AbsWeight',
'Weight'}
)
# replace nan with "nan" to allow equality comparisons
results = results.round(7)
results = results.where(results.notnull(), "nan")
signals = results.loc["Signal"].reset_index()
signals.loc[:, "Date"] = signals.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
signals.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [1.0,
0.0,
0.0,
1.0],
"FI23456": [1.0,
0.0,
1.0,
0.0]}
)
weights = results.loc["Weight"].reset_index()
weights.loc[:, "Date"] = weights.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
weights.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.5,
0.0,
0.0,
1.0],
"FI23456": [0.5,
0.0,
1.0,
0.0]}
)
returns = results.loc["Return"]
returns = returns.reset_index()
returns.loc[:, "Date"] = returns.Date.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
self.assertDictEqual(
returns.to_dict(orient="list"),
{'Date': [
'2018-05-01T00:00:00',
'2018-05-02T00:00:00',
'2018-05-03T00:00:00',
'2018-05-04T00:00:00'],
"FI12345": [0.0,
0.0,
-0.0227273, # (10.50 - 11)/11 * 0.5
-0.0],
"FI23456": [0.0,
0.0,
-0.1136364, # (8.50 - 11)/11 * 0.5
0.0]}
)
def test_80_load_history_from_cache_if_no_end_date_and_db_not_modified(self):
"""
Runs a strategy without using mock to show that the history cache is
used. Because no end_date is passed, list_databases is consulted, but
it reports that the db was not recently modified.
"""
class BuyBelow10(Moonshot):
"""
A basic test strategy that buys below 10.
"""
def prices_to_signals(self, prices):
signals = prices.loc["Close"] < 10
return signals.astype(int)
def mock_list_databases(**kwargs):
return {
"postgres": [],
"sqlite": [{'last_modified': "2015-01-01T13:45:00",
'name': 'quantrocket.history.my-db1.sqlite',
'path': '/var/lib/quantrocket/quantrocket.history.my-db1.sqlite',
'size_in_mb': 3.1},
{'last_modified': "2016-01-01T04:04:00",
'name': 'quantrocket.history.my-db2.sqlite',
'path': '/var/lib/quantrocket/quantrocket.history.my-db2.sqlite',
'size_in_mb': 2.1},
]}
with patch("moonshot.cache.list_databases", new=mock_list_databases):