def optimizebuy_keltner2(config):
feed = fxsignal.FxcmFeed('EUR/USD', datetime(2015, 10, 1),
datetime(2019, 5, 1), 'D1', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.OptimizeRunner(feed, data, 'trend_keltner2', 'sell')
runner.run()
def optimizesell_trend30(config):
feed = fxsignal.FxcmFeed('GBP/USD', datetime(2019, 1, 1),
datetime(2019, 9, 1), 'm30', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.OptimizeRunner(feed, data, 'trend30', 'sell')
runner.run()
def savecsv(config):
feed = fxsignal.FxcmFeed('GBP/USD', datetime(2018, 1, 1), datetime(2019, 5, 1), 'D1', config['fxcm'])
# data = pd.DataFrame()
data = feed.get_feed()
feed.close()
feed.save_csv(data)
logging.info("savecsv count(): {}".format(len(data.index)))
def optimizebuy_hma(config):
feed = fxsignal.FxcmFeed('EUR/USD', datetime(2015, 1, 1),
datetime(2019, 5, 1), 'D1', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.OptimizeRunner(feed, data, 'trend_jackvortex', 'buy')
runner.run()
def simplebuy_keltner2(config):
feed = fxsignal.FxcmFeed('EUR/USD', datetime(2015, 1, 1),
datetime(2019, 5, 1), 'D1', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.AlgoRunner(feed, data, 'trend_keltner2', 'buy')
runner.run()
def panda2bt(config):
feed = fxsignal.FxcmFeed('GBP/USD', datetime(2018, 1, 1), datetime(2019, 5, 1), 'D1', config['fxcm'])
# data = pd.DataFrame()
feed.read_csv()
d1 = feed.clean()
# print(d1.info())
data = fxsignal.FeedConverter.fxcm2bt(d1)
logging.info("panda2bt symbol(): {}".format(feed.symbol))
def simplebuy_keltner(config):
feed = fxsignal.FxcmFeed('EUR/USD', datetime(2015, 1, 1),
datetime(2019, 5, 1), 'D1', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.AlgoRunner(feed, data, 'trend_keltner', 'buy')
runner.run()
logging.info("simplebuy symbol: {} {}".format(feed.symbol,
runner.feed.symbol))
def optimizesell_hma(config, currency, start_dt):
feed = fxsignal.FxcmFeed(currency, start_dt, datetime(2019, 8, 1), 'D1',
config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.OptimizeRunner(feed,
data,
'trend_jackvortex',
'sell',
file_id=start_dt.strftime("%Y-%m-%d"))
runner.run()
def collect(config):
''' Collects data for whole period '''
start_date = datetime(2019, 1, 1)
end_date = datetime(2019, 9, 1)
for symbol in symbol_list:
feed = fxsignal.FxcmFeed(symbol, start_date, end_date, 'm30',
config['fxcm'])
feed.get_feed()
feed.close()
data = feed.clean()
feed.save_csv(data)
logging.info("{} count: {}".format(symbol, len(data.index)))
def simplesell_trend30(config):
feed = fxsignal.FxcmFeed('GBP/USD', datetime(2019, 1, 1),
datetime(2019, 9, 1), 'm30', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.AlgoRunner(feed,
data,
'trend30',
'sell',
plot=True,
verbose=True)
runner.run()
logging.info("simplesell symbol: {} {}".format(feed.symbol,
runner.feed.symbol))
def simplesell_hma(config):
feed = fxsignal.FxcmFeed('EUR/USD', datetime(2018, 4, 1),
datetime(2019, 5, 1), 'D1', config['fxcm'])
feed.read_csv()
data = fxsignal.FeedConverter.fxcm2bt(feed.clean())
runner = fxsignal.AlgoRunner(feed,
data,
'trend_jackvortex',
'sell',
plot=False,
verbose=False)
runner.run()
logging.info("simplebuy symbol: {} {}".format(feed.symbol,
runner.feed.symbol))
def collect_chunk(config):
''' Collects data by months (there is 10000 records limitation in fxcmpy API) '''
start_date = datetime(2010, 1, 1)
end_date = datetime(2019, 9, 1)
for symbol in symbol_list:
chunk_start_date = start_date
chunk_end_date = start_date + relativedelta(months=+1)
history = pd.DataFrame()
while (chunk_start_date < end_date):
feed = fxsignal.FxcmFeed(symbol, chunk_start_date, chunk_end_date,
'm30', config['fxcm'])
feed.get_feed()
feed.close()
data = feed.clean()
history = history.append(data, ignore_index=True)
chunk_start_date = chunk_end_date + relativedelta(seconds=+1)
chunk_end_date = chunk_end_date + relativedelta(months=+1)
feed.save_csv(history)
logging.info("{} count: {}".format(symbol, len(history.index)))
def getcsv(config):
feed = fxsignal.FxcmFeed('GBP/USD', datetime(2018, 1, 1), datetime(2019, 5, 1), 'D1', config['fxcm'])
# data = pd.DataFrame()
data = feed.read_csv()
logging.info("getcsv count(): {}".format(len(data.index)))