def test_GenerateHistoricTradesForActiveStrategies(self):
self._createStrategy()
args = "-n unitTest".split()
generateHistoricTradesForActiveStrategies(args)
self.assertEqual(self._countRows("HistoricTrades"), 333)
def test_GenerateHistoricTradesForActiveStrategies(self):
self._createStrategy()
args = "-n unitTest".split()
generateHistoricTradesForActiveStrategies(args)
self.assertEqual(self._countRows("HistoricTrades"), 333)
from pyswing.AskHorse import askHorse
args = "-n asx".split()
askHorse(args)
from pyswing.AnalyseStrategies import analyseStrategies
args = "-n ftse -s v1.2 -r 0.4 -t 500".split
analyseStrategies(args)
# Run me to populate (emptying to begin with) the historic trades table using the strategies in active strategies (which must be put in there manually)...
from pyswing.GenerateHistoricTradesForActiveStrategies import generateHistoricTradesForActiveStrategies
args = "-n ftse".split()
generateHistoricTradesForActiveStrategies(args)
# Run me to chart the (distinct) results in active strategy
import pyswing.database
import sqlite3
from pandas.io.sql import read_sql_query
from pandas import expanding_sum
connection = sqlite3.connect(pyswing.database.pySwingDatabase)
query = ("select t.matchDate as Date, t.code as Code, t.type as Type, t.ExitValue as ExitValue from ( select distinct matchDate, Code, type, exitValue from historicTrades order by matchDate asc) t")
cbaEquityData = read_sql_query(query, connection, 'Date')
connection.close()
cbaEquityData['ExitValueAfterCosts'] = cbaEquityData['ExitValue'] - 0.2
exitValueDataFrame = cbaEquityData.ix[:,'ExitValueAfterCosts']
cbaEquityData["Sum"] = expanding_sum(exitValueDataFrame)
cbaEquityData.query("Date > '2005-01-01 00:00:00'").plot(y=['Sum'], title='v1.4')
connection.close()
cbaEquityData.query("Date > '2015-06-01 00:00:00'").plot(
y=['Close', 'SMA_200'], title='Testing')
from pyswing.AskHorse import askHorse
args = "-n asx".split()
askHorse(args)
from pyswing.AnalyseStrategies import analyseStrategies
args = "-n ftse -s v1.2 -r 0.4 -t 500".split
analyseStrategies(args)
# Run me to populate (emptying to begin with) the historic trades table using the strategies in active strategies (which must be put in there manually)...
from pyswing.GenerateHistoricTradesForActiveStrategies import generateHistoricTradesForActiveStrategies
args = "-n ftse".split()
generateHistoricTradesForActiveStrategies(args)
# Run me to chart the (distinct) results in active strategy
import pyswing.database
import sqlite3
from pandas.io.sql import read_sql_query
from pandas import expanding_sum
connection = sqlite3.connect(pyswing.database.pySwingDatabase)
query = (
"select t.matchDate as Date, t.code as Code, t.type as Type, t.ExitValue as ExitValue from ( select distinct matchDate, Code, type, exitValue from historicTrades order by matchDate asc) t"
)
cbaEquityData = read_sql_query(query, connection, 'Date')
connection.close()
cbaEquityData['ExitValueAfterCosts'] = cbaEquityData['ExitValue'] - 0.2
exitValueDataFrame = cbaEquityData.ix[:, 'ExitValueAfterCosts']
cbaEquityData["Sum"] = expanding_sum(exitValueDataFrame)
