def test_ExitValues(self):
exitValues = ExitValuesTrailingStop("WOR.AX", 0.03, 2)
exitValues.calculateExitValues()
dataPointBuyWin = exitValues._buyExitValueDataFrame.ix['2015-07-02 00:00:00']
self.assertAlmostEqual(dataPointBuyWin['ExitValue'], 1.581, 3)
dataPointBuyLose = exitValues._buyExitValueDataFrame.ix['2015-07-13 00:00:00']
self.assertAlmostEqual(dataPointBuyLose['ExitValue'], -1.741, 3)
dataPointSellWin = exitValues._sellExitValueDataFrame.ix['2015-07-03 00:00:00']
self.assertAlmostEqual(dataPointSellWin['ExitValue'], 6.000, 3)
dataPointSellLose = exitValues._sellExitValueDataFrame.ix['2015-07-14 00:00:00']
self.assertAlmostEqual(dataPointSellLose['ExitValue'], -4.832, 3)
strategies = getExitStrategies()
self.assertTrue(len(strategies) == 1)
def test_ExitValues(self):
exitValues = ExitValuesTrailingStop("WOR.AX", 0.03, 2)
exitValues.calculateExitValues()
dataPointBuyWin = exitValues._buyExitValueDataFrame.ix[
'2015-07-02 00:00:00']
self.assertAlmostEqual(dataPointBuyWin['ExitValue'], 1.581, 3)
dataPointBuyLose = exitValues._buyExitValueDataFrame.ix[
'2015-07-13 00:00:00']
self.assertAlmostEqual(dataPointBuyLose['ExitValue'], -1.741, 3)
dataPointSellWin = exitValues._sellExitValueDataFrame.ix[
'2015-07-03 00:00:00']
self.assertAlmostEqual(dataPointSellWin['ExitValue'], 6.000, 3)
dataPointSellLose = exitValues._sellExitValueDataFrame.ix[
'2015-07-14 00:00:00']
self.assertAlmostEqual(dataPointSellLose['ExitValue'], -4.832, 3)
strategies = getExitStrategies()
self.assertTrue(len(strategies) == 1)
def evaluateTwoRuleStrategies(argv):
"""
Evaluate Two-Rule Strategies.
:param argv: Command Line Parameters.
-n = Name
-m = Minimum Matches Per Day
-s = Strategy Name
Example:
python -m pyswing.EvaluateTwoRuleStrategies -n asx -m 0.1 -s v4.0
"""
Logger.log(logging.INFO, "Log Script Call", {"scope":__name__, "arguments":" ".join(argv)})
Logger.pushLogData("script", __name__)
marketName = ""
minimumMatchesPerDay = None
try:
shortOptions = "n:m:s:dh"
longOptions = ["marketName=", "matches=", "strategy=", "debug", "help"]
opts, __ = getopt.getopt(argv, shortOptions, longOptions)
except getopt.GetoptError as e:
Logger.log(logging.ERROR, "Error Reading Options", {"scope": __name__, "exception": str(e)})
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ("-d", "--debug"):
Logger().setLevel(logging.DEBUG)
elif opt in ("-h", "--help"):
print("?")
usage()
sys.exit()
elif opt in ("-n", "--marketName"):
marketName = arg
elif opt in ("-m", "--matches"):
minimumMatchesPerDay = arg
elif opt in ("-s", "--strategy"):
pyswing.constants.pySwingStrategy = arg
if marketName != "" and minimumMatchesPerDay and pyswing.constants.pySwingStrategy:
pyswing.database.initialiseDatabase(marketName)
Logger.log(logging.INFO, "Evaluate Two-Rule Strategies", {"scope":__name__, "market":marketName, "matches":minimumMatchesPerDay, "strategy":pyswing.constants.pySwingStrategy})
strategies = getTwoRuleStrategies(minimumMatchesPerDay)
exits = getExitStrategies()
inverseStrategies = set()
for rules in strategies:
if rules not in inverseStrategies:
for exit in exits:
buyStrategy = Strategy(rules[0], rules[1], exit, 'Buy')
buyStrategy.evaluateTwoRuleStrategy()
sellStrategy = Strategy(rules[0], rules[1], exit, 'Sell')
sellStrategy.evaluateTwoRuleStrategy()
inverseStrategies.add((rules[1], rules[0]))
TeamCity.setBuildResultText("Evaluated Two-Rule Strategies")
else:
Logger.log(logging.ERROR, "Missing Options", {"scope": __name__, "options": str(argv)})
usage()
sys.exit(2)
