L
Thanks a lot. My previous stuff was working but it was breaking while running optimization with multiple cpu because of the way it was designed. This psql example has shown me what to do.
Here is the new version for the benefits of others. Happy Friday!
Lamp'
import datetime as dt
from backtrader import TimeFrame
from backtrader.feed import DataBase
from backtrader import date2num
class SQLiteData(DataBase):
params = (
('database', None),
('symbol', 'XBTUSD'),
('tick_value', 0.01),
('timeframe ', TimeFrame.Minutes),
('compression', 1),
('fromdate', dt.datetime(1900, 1, 1)),
('todate', dt.datetime.max),
# parameterized column indices for ease of overwriting/re-implementing
('datetime', 0),
('open', 1),
('high', 2),
('low', 3),
('close', 4),
('volume', 5),
('openinterest', -1),
)
def __init__(self):
self._timeframe = self.p.timeframe
self._compression = self.p.compression
self._dataname = '{0}-{1:.2f}'.format(self.p.symbol, self.p.tick_value)
def start(self):
engine = self._connect_db()
sql_query = "SELECT `date`,`open`,`high`,`low`,`close`,`volume` FROM `historical_data` WHERE `symbol` = '" + self.p.symbol + "' AND `date` between '" + self.p.fromdate.strftime(
"%Y-%m-%d %H:%M:%S") + "' and '" + self.p.todate.strftime("%Y-%m-%d %H:%M:%S") + "' ORDER BY `date` ASC"
result = engine.execute(sql_query)
self.price_rows = result.fetchall()
result.close()
self.price_i = 0
super(SQLiteData, self).start()
def _load(self):
if self.price_i >= len(self.price_rows):
return False
row = self.price_rows[self.price_i]
self.price_i += 1
for datafield in self.getlinealiases():
if datafield == 'datetime':
self.lines.datetime[0] = date2num(dt.datetime.strptime(row[self.p.datetime],'%Y-%m-%d %H:%M:%S'))
elif datafield == 'volume':
self.lines.volume[0] = row[self.p.volume]
else:
# get the column index
colidx = getattr(self.params, datafield)
if colidx < 0:
# column not present -- skip
continue
# get the line to be set
line = getattr(self.lines, datafield)
line[0] = float(row[colidx])
return True
def _connect_db(self):
from sqlalchemy import create_engine
url = 'sqlite:///{0}'.format(self.p.database)
engine = create_engine(url, echo=False)
return engine
def preload(self):
super(SQLiteData, self).preload()
# preloaded - no need to keep the price_rows as data is stored in the cerebro's Lines
self.price_rows = None
