mirror of
https://github.com/wassname/catalyst.git
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ENH: Add hdf5 and csv source.
This creates a data source for csv and hdf5 files, a generator to create a sample csv, and a pytables generator to go from a list of dated gzipped csv's in a directory to a pytables data source. This does not add a unittest yet which we should write for the future.
This commit is contained in:
Michael Schatzow
committed by
twiecki
twiecki
parent
07e25ae018
commit
59bcd097d5
@@ -0,0 +1,172 @@
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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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"""
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leverage work of briancappello and quantopian team
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(especcially twiecki, eddie, and fawce)
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michaelws
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"""
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import pandas as pd
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from zipline.gens.utils import hash_args
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from zipline.sources.data_source import DataSource
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import datetime
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import csv
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import numpy as np
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import dateutil.parser
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def gen_ts(date, time):
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return pd.Timestamp(datetime.datetime.combine(date, time))
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class DatasourceCSVohlc(DataSource):
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""" expects dictReader for a csv file
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with the following columns in the header
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dt, sid, open, high, low, close, volume
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dt expected in ISO format and order does not matter"""
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def __init__(self, data, **kwargs):
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isinstance(data, csv.DictReader)
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self.data = data
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# Unpack config dictionary with default values.
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self.tz_in = kwargs.get('tz_in', "US/Eastern")
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self.start = pd.Timestamp(np.datetime64(kwargs.get('start')))
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self.start = self.start.tz_localize('utc')
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self.end = pd.Timestamp(np.datetime64(kwargs.get('end')))
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self.end = self.end.tz_localize('utc')
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start_time_str = kwargs.get("start_time", "9:30")
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end_time_str = kwargs.get("end_time", "16:00")
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self.sid_filter = kwargs.get('sid_filter', None)
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self.source_id = kwargs.get("source_id", None)
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self.sids = kwargs.get('sidsF', None)
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self.start_time = dateutil.parser.parse(start_time_str).time()
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self.end_time = dateutil.parser.parse(end_time_str).time()
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self._raw_data = None
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self.arg_string = hash_args(data, **kwargs)
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@property
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def instance_hash(self):
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return self.arg_string
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def raw_data_gen(self):
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previous_ts = None
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cols = np.array(["open", "high", "low"])
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for row in self.data:
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dt64 = pd.Timestamp(np.datetime64(row["dt"]))
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ts = pd.Timestamp(dt64).tz_localize(self.tz_in).tz_convert('utc')
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if ts < self.start or ts > self.end:
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continue
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if previous_ts is None or ts.date() != previous_ts.date():
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start_ts = datetime.date(ts.date(), self.start_time)
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end_ts = gen_ts(ts.date(), self.end_time)
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volumes = {}
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price_volumes = {}
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sid = row["sid"]
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if self.sid_filter and sid in self.sid_filter:
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continue
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elif self.sids is None or sid in self.sids:
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if sid not in volumes:
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volumes[sid] = 0
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price_volumes[sid] = 0
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if ts < start_ts or ts > end_ts:
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continue
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event = {"sid": sid, "type": "TRADE", "symbol": sid}
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event["dt"] = ts
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event["price"] = float(row["close"])
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event["close"] = event["price"]
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event["volume"] = int(row["volume"])
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volumes[sid] += float(event["volume"])
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price_volumes[sid] += event["price"] * event["volume"]
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event["vwap"] = price_volumes[sid] / volumes[sid]
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for field in cols:
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event[field] = float(row[field])
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yield event
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previous_ts = ts
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@property
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def raw_data(self):
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if not self._raw_data:
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self._raw_data = self.raw_data_gen()
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return self._raw_data
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@property
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def mapping(self):
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return {
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'sid': (lambda x: x, 'sid'),
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'dt': (lambda x: x, 'dt'),
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'open': (float, 'open'),
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'high': (float, 'high'),
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'low': (float, 'low'),
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'close': (float, 'close'),
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'price': (float, 'price'),
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'volume': (int, 'volume'),
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'vwap': (lambda x: x, 'vwap')
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}
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class DataSourceCSVSignal(DataSource):
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""" expects dictReader for a csv file in form with header
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dt, sid, signal
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dt expected in ISO format"""
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def __init__(self, data, **kwargs):
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assert isinstance(data, csv.DictReader)
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self.data = data
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self.source_id = kwargs.get("source_id", None)
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# Unpack config dictionary with default values.
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self.start = kwargs.get('start')
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self.end = kwargs.get('end')
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self.sids = kwargs.get('sids', None)
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self.sid_filter = kwargs.get('sid_filter', None)
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self.arg_string = hash_args(data, **kwargs)
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self._raw_data = None
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@property
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def instance_hash(self):
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return self.arg_string
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def raw_data_gen(self):
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previous_ts = None
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for row in self.data:
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dt64 = pd.Timestamp(np.datetime64(row["dt"]))
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ts = pd.Timestamp(dt64).tz_localize(self.tz_in).tz_convert('utc')
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if ts < self.start or ts > self.end:
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continue
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if previous_ts is None or ts.date() != previous_ts.date():
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start_ts = gen_ts(ts.date(), self.start_time)
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end_ts = gen_ts(ts.date(), self.end_time)
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volumes = {}
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price_volumes = {}
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sid = row["sid"]
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if self.sid_filter and sid in self.sid_filter:
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continue
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elif self.sids is None or sid in self.sids:
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if sid not in volumes:
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volumes[sid] = 0
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price_volumes[sid] = 0
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if ts < start_ts or ts > end_ts:
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continue
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event = {"sid": sid, "type": "CUSTOM", "dt": ts,
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"signal": row["signal"]}
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yield event
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previous_ts = ts
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@property
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def raw_data(self):
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if not self._raw_data:
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self._raw_data = self.raw_data_gen()
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return self._raw_data
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@property
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def mapping(self):
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return {
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'sid': (lambda x: x, 'symbol'),
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'dt': (lambda x: x, 'dt'),
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'signal': (lambda x: x, 'signal'),
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}
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@@ -0,0 +1,220 @@
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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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"""
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leverage work of briancappello and quantopian team
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(especially twiecki, eddie, and fawce)
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"""
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import pandas as pd
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from zipline.gens.utils import hash_args
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from zipline.sources.data_source import DataSource
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import datetime
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import numpy as np
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import dateutil.parser
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import tables
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def _iterate_ohlc(date_node, sid_filter, sids, start_ts, end_ts):
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last_stamp = None
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last_ts = None
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volumes = {}
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price_volumes = {}
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cols = np.array(["open", "high", "low", "close"])
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for row in date_node.iterrows():
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sid = row["sid"]
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if sid_filter and sid in sid_filter:
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continue
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elif sids is None or sid in sids:
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if sid not in volumes:
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volumes[sid] = 0
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price_volumes[sid] = 0
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if last_stamp and row["dt"] == last_stamp:
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ts = last_ts
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else:
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ts = pd.Timestamp(np.datetime64(row["dt"], "s"), tz='utc')
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last_ts = ts
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last_stamp = row["dt"]
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if (start_ts > ts) or (ts > end_ts):
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continue
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event = {"sid": sid, "type": "TRADE", "symbol": sid}
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event["dt"] = ts
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event["price"] = row["close"]
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event["volume"] = row["volume"]
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volumes[sid] += event["volume"]
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price_volumes[sid] += event["price"] * event["volume"]
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event["vwap"] = price_volumes[sid] / volumes[sid]
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last_ts = ts
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for field in cols:
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event[field] = row[field]
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yield event
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def _iterate_signal(date_node, sids, sid_filter, start_ts, end_ts):
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last_stamp = None
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last_ts = None
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volumes = {}
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price_volumes = {}
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for row in date_node.iterrows():
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sid = row["sid"]
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if sid_filter and sid in sid_filter:
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continue
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elif sids is None or sid in sids:
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if sid not in volumes:
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volumes[sid] = 0
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price_volumes[sid] = 0
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if last_stamp and row["dt"] == last_stamp:
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ts = last_ts
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else:
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ts = pd.Timestamp(np.datetime64(row["dt"], "s"), tz='utc')
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last_ts = ts
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last_stamp = row["dt"]
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if (start_ts > ts) or (ts > end_ts):
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continue
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event = {"sid": sid, "type": "CUSTOM",
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"signal": row["signal"]}
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yield event
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class DataSourceTablesOHLC(DataSource):
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"""
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Yields all events in event_list that match the given sid_filter.
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If no event_list is specified, generates an internal stream of events
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to filter. Returns all events if filter is None.
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Configuration options:
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sids : list of values representing simulated internal sids
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start : start date
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tz_in : timezzone of table
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filter : filter to remove the sids
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start_time: what time trading should start
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end_time: what time trading should end
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"""
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def __init__(self, data, **kwargs):
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assert isinstance(data, tables.file.File)
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self.data = data
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# Unpack config dictionary with default values.
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if 'symbols' in kwargs:
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self.sids = kwargs.get('symbols')
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else:
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self.sids = None
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self.tz_in = kwargs.get('tz_in', "US/Eastern")
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self.source_id = kwargs.get("source_id", None)
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self.sid_filter = kwargs.get("filter", None)
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self.start = pd.Timestamp(np.datetime64(kwargs.get('start')))
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self.start = self.start.tz_localize('utc')
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self.end = pd.Timestamp(np.datetime64(kwargs.get('end')))
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self.end = self.end.tz_localize('utc')
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start_time_str = kwargs.get("start_time", "9:30")
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end_time_str = kwargs.get("end_time", "16:00")
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self.start_time = dateutil.parser.parse(start_time_str).time()
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self.end_time = dateutil.parser.parse(end_time_str).time()
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self._raw_data = None
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self.arg_string = hash_args(data, **kwargs)
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self.root_node = "/" + kwargs.get('root', "TD") + "/"
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@property
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def instance_hash(self):
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return self.arg_string
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def raw_data_gen(self):
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for date_node in self.data.walkNodes(self.root_node):
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if isinstance(date_node, tables.group.Group):
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continue
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date = dateutil.parser.parse(date_node.name.split("_")[1])
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dt64 = np.datetime64(date)
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table_dt = pd.Timestamp(dt64).tz_localize("utc")
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if table_dt < self.start or table_dt > self.end:
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continue
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start_ts = pd.Timestamp(datetime.datetime.combine(table_dt.date(),
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self.start_time),
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tz=self.tz_in)
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start_ts = start_ts.tz_convert("utc")
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end_ts = pd.Timestamp(datetime.datetime.combine(table_dt.date(),
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self.end_time),
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tz=self.tz_in)
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end_ts = end_ts.tz_convert("utc")
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for item in _iterate_ohlc(date_node, self.sids, self.sid_filter,
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start_ts, end_ts):
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yield item
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@property
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def raw_data(self):
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if not self._raw_data:
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self._raw_data = self.raw_data_gen()
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return self._raw_data
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@property
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def mapping(self):
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return {
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'sid': (lambda x: x, 'sid'),
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'dt': (lambda x: x, 'dt'),
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'open': (lambda x: x, 'open'),
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'high': (lambda x: x, 'high'),
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'low': (lambda x: x, 'low'),
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'close': (lambda x: x, 'close'),
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'price': (lambda x: x, 'price'),
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'volume': (lambda x: x, 'volume'),
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'vwap': (lambda x: x, 'vwap')
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}
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class DataSourceTablesSignal(DataSource):
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def __init__(self, data, **kwargs):
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assert isinstance(data, tables.file.File)
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self.h5file = data
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self.sids = kwargs.get('sids', None)
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self.start = kwargs.get('start')
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self.end = kwargs.get('end')
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self.source_id = kwargs.get("source_id", None)
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self.arg_string = hash_args(data, **kwargs)
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self._raw_data = None
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self.root_node = +"/" + kwargs.get('root', "signal") + "/"
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@property
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def instance_hash(self):
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return self.arg_string
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def raw_data_gen(self):
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for date_node in self.data.walkNodes(self.root_node):
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if isinstance(date_node, tables.group.Group):
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continue
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date = dateutil.parser.parse(date_node.name.split("_")[1])
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dt64 = np.datetime64(date)
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table_dt = pd.Timestamp(dt64).tz_localize("utc")
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if table_dt < self.start or table_dt > self.end:
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continue
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start_ts = pd.Timestamp(datetime.datetime.combine(table_dt.date(),
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self.start_time),
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tz=self.tz_in)
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start_ts = start_ts.tz_convert("utc")
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end_ts = pd.Timestamp(datetime.datetime.combine(table_dt.date(),
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self.end_time),
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tz=self.tz_in)
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end_ts = end_ts.tz_convert("utc")
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table = self.data.getNode(date_node)
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for row in _iterate_signal(table, self.sids, self.sid_filter,
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start_ts, end_ts):
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yield row
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@property
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def raw_data(self):
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if not self._raw_data:
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self._raw_data = self.raw_data_gen()
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return self._raw_data
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@property
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def mapping(self):
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return {
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'sid': (lambda x: x, 'symbol'),
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'dt': (lambda x: x, 'dt'),
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'signal': (lambda x: x, 'signal'),
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}
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@@ -0,0 +1,194 @@
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#!/usr/bin/env python
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import sys
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import getopt
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import traceback
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import numpy as np
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import pandas as pd
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import datetime
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import logging
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import tables
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import gzip
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import glob
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import os
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import random
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import csv
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import time
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FORMAT = "%(asctime)-15s -8s %(message)s"
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logging.basicConfig(format=FORMAT, level=logging.INFO)
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class Usage(Exception):
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def __init__(self, msg):
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self.msg = msg
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OHLCTableDescription = {'sid': tables.StringCol(14, pos=2),
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'dt': tables.Int64Col(pos=1),
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'open': tables.Float64Col(dflt=np.NaN, pos=3),
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'high': tables.Float64Col(dflt=np.NaN, pos=4),
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'low': tables.Float64Col(dflt=np.NaN, pos=5),
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'close': tables.Float64Col(dflt=np.NaN, pos=6),
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"volume": tables.Int64Col(dflt=0, pos=7)}
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def process_line(line):
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dt = np.datetime64(line["dt"]).astype(np.int64)
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sid = line["sid"]
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open_p = float(line["open"])
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high_p = float(line["high"])
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low_p = float(line["low"])
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close_p = float(line["close"])
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volume = int(line["volume"])
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return (dt, sid, open_p, high_p, low_p, close_p, volume)
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def parse_csv(csv_reader):
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previous_date = None
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data = []
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dtype = [('dt', 'int64'), ('sid', '|S14'), ('open', float),
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('high', float), ('low', float), ('close', float),
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('volume', int)]
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for line in csv_reader:
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row = process_line(line)
|
||||
current_date = line["dt"][:10].replace("-", "")
|
||||
if previous_date and previous_date != current_date:
|
||||
rows = np.array(data, dtype=dtype).view(np.recarray)
|
||||
yield current_date, rows
|
||||
data = []
|
||||
data.append(row)
|
||||
previous_date = current_date
|
||||
|
||||
|
||||
def merge_all_files_into_pytables(file_dir, file_out):
|
||||
"""
|
||||
process each file into pytables
|
||||
"""
|
||||
start = None
|
||||
start = datetime.datetime.now()
|
||||
out_h5 = tables.openFile(file_out,
|
||||
mode="w",
|
||||
title="bars",
|
||||
filters=tables.Filters(complevel=9,
|
||||
complib='zlib'))
|
||||
table = None
|
||||
for file_in in glob.glob(file_dir + "/*.gz"):
|
||||
gzip_file = gzip.open(file_in)
|
||||
expected_header = ["dt", "sid", "open", "high", "low", "close",
|
||||
"volume"]
|
||||
csv_reader = csv.DictReader(gzip_file)
|
||||
header = csv_reader.fieldnames
|
||||
if header != expected_header:
|
||||
logging.warn("expected header %s\n" % (expected_header))
|
||||
logging.warn("header_found %s" % (header))
|
||||
return
|
||||
|
||||
for current_date, rows in parse_csv(csv_reader):
|
||||
table = out_h5.createTable("/TD", "date_" + current_date,
|
||||
OHLCTableDescription,
|
||||
expectedrows=len(rows),
|
||||
createparents=True)
|
||||
table.append(rows)
|
||||
table.flush()
|
||||
if not table is None:
|
||||
table.flush()
|
||||
end = datetime.datetime.now()
|
||||
diff = (end - start).seconds
|
||||
logging.debug("finished it took %d." % (diff))
|
||||
|
||||
|
||||
def create_fake_csv(file_in):
|
||||
fields = ["dt", "sid", "open", "high", "low", "close", "volume"]
|
||||
gzip_file = gzip.open(file_in, "w")
|
||||
dict_writer = csv.DictWriter(gzip_file, fieldnames=fields)
|
||||
current_dt = datetime.date.today() - datetime.timedelta(days=2)
|
||||
current_dt = pd.Timestamp(current_dt).replace(hour=9)
|
||||
current_dt = current_dt.replace(minute=30)
|
||||
end_time = pd.Timestamp(datetime.date.today())
|
||||
end_time = end_time.replace(hour=16)
|
||||
last_price = 10.0
|
||||
while current_dt < end_time:
|
||||
row = {}
|
||||
row["dt"] = current_dt
|
||||
row["sid"] = "test"
|
||||
last_price += random.randint(-20, 100) / 10000.0
|
||||
row["close"] = last_price
|
||||
row["open"] = last_price - 0.01
|
||||
row["low"] = last_price - 0.02
|
||||
row["high"] = last_price + 0.02
|
||||
row["volume"] = random.randint(10, 1000) * 10
|
||||
dict_writer.writerow(row)
|
||||
current_dt += datetime.timedelta(minutes=1)
|
||||
if current_dt.hour > 16:
|
||||
current_dt += datetime.timedelta(days=1)
|
||||
current_dt = current_dt.replace(hour=9)
|
||||
current_dt = current_dt.replace(minute=30)
|
||||
gzip_file.close()
|
||||
|
||||
|
||||
def main(argv=None):
|
||||
"""
|
||||
This script cleans minute bars into pytables file
|
||||
data_source_tables_gen.py
|
||||
[--tz_in] sets time zone of data only reasonably fast way to use
|
||||
time.tzset()
|
||||
[--dir_in] iterates through directory provided of csv files in gzip form
|
||||
in form:
|
||||
dt, sid, open, high, low, close, volume
|
||||
2012-01-01T12:30:30,1234HT,1, 2,3,4.0
|
||||
[--fake_csv] creates a fake sample csv to iterate through
|
||||
[--file_out] determines output file
|
||||
"""
|
||||
if argv is None:
|
||||
argv = sys.argv
|
||||
try:
|
||||
dir_in = None
|
||||
file_out = "./all.h5"
|
||||
fake_csv = None
|
||||
try:
|
||||
opts, args = getopt.getopt(argv[1:], "hdft",
|
||||
["help",
|
||||
"dir_in=",
|
||||
"debug",
|
||||
"tz_in=",
|
||||
"fake_csv=",
|
||||
"file_out="])
|
||||
except getopt.error, msg:
|
||||
raise Usage(msg)
|
||||
for opt, value in opts:
|
||||
if opt in ("--help", "-h"):
|
||||
print main.__doc__
|
||||
if opt in ("-d", "--debug"):
|
||||
logging.basicConfig(format=FORMAT,
|
||||
level=logging.DEBUG)
|
||||
if opt in ("-d", "--dir_in"):
|
||||
dir_in = value
|
||||
if opt in ("-o", "--file_out"):
|
||||
file_out = value
|
||||
if opt in ("--fake_csv"):
|
||||
fake_csv = value
|
||||
if opt in ("--tz_in"):
|
||||
os.environ['TZ'] = value
|
||||
time.tzset()
|
||||
try:
|
||||
if dir_in:
|
||||
merge_all_files_into_pytables(dir_in, file_out)
|
||||
if fake_csv:
|
||||
create_fake_csv(fake_csv)
|
||||
except Exception:
|
||||
error = "An unhandled error occured in the"
|
||||
error += "data_source_tables_gen.py script."
|
||||
error += "\n\nTraceback:\n"
|
||||
error += '-' * 70 + "\n"
|
||||
error += "".join(traceback.format_tb(sys.exc_info()[2]))
|
||||
error += repr(sys.exc_info()[1]) + "\n"
|
||||
error += str(sys.exc_info()[1]) + "\n"
|
||||
error += '-' * 70 + "\n"
|
||||
print error
|
||||
except Usage, err:
|
||||
print >>sys.stderr, err.msg
|
||||
print >>sys.stderr, "for help use --help"
|
||||
return 2
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user