def fetch_data(currenceyAr, exchange, directory, period):
dataObj = GetData(exchange, directory)
EWCEWAIGE = pd.read_csv('EWCEWAIGE.csv').set_index('date')
currenceyPairs = []
currenceyTsLengths = []
for currencey in currenceyAr:
if currencey == 'ewc' or currencey == 'ewa' or currencey == 'ige':
currenceyPairs.append(EWCEWAIGE[currencey].values)
currenceyTsLengths.append(len(EWCEWAIGE[currencey].values))
else:
ts = dataObj.fetch(currencey)
ts = dataObj.periodFormatter(currencey, period)
ts.index = pd.to_datetime(ts.index).dropna()
currenceyTsLengths.append(len(ts.Close.values))
currenceyPairs.append(ts.Close.values)
maxLen = 20000
for count, curr in enumerate(currenceyPairs):
if min(currenceyTsLengths) < 10000:
currenceyPairs[count] = curr[-min(currenceyTsLengths):]
else:
currenceyPairs[count] = curr[-maxLen:]
A = []
for curr in currenceyPairs:
A.append(curr)
A = np.transpose(np.array(A))
return A
def __init__(self, exchange, currencey, period, directory, livePreScreen=False):
self.exchange=exchange
self.currencey = currencey
self.period = period
self.directory = directory
dataObj = GetData(self.exchange, self.directory)
self.ts = dataObj.fetch(self.currencey)
self.ts = dataObj.periodFormatter(self.currencey, period)
self.ts.index = pd.to_datetime(self.ts.index).dropna()
dataObj = GetData(self.exchange, self.directory)
self.tsA = dataObj.fetch('XMR/USDT')
self.tsA = dataObj.periodFormatter('XMR/USDT', period)
self.tsA.index = pd.to_datetime(self.tsA.index).dropna()
self.tsB = dataObj.fetch('ETH/USDT')
self.tsB = dataObj.periodFormatter('ETH/USDT', period)
self.tsB.index = pd.to_datetime(self.tsB.index).dropna()
self.USDCAD = pd.read_csv('CADUSD.csv').astype(float).dropna()
self.EWCEWAIGE = pd.read_csv('EWCEWAIGE.csv').set_index('date')
def data():
#read in config info from json file
data_config = json.load(open('input_config.json', 'r'))
previous_data_name = data_config["data_and_directories"][
"previous_data_name"]
main_src_directory = data_config["data_and_directories"][
"main_src_directory"]
data_directory = data_config["data_and_directories"][
"main_data_directory"]
period = data_config["data_information"]["period"]
assetType = data_config["data_information"]["asset_type"]
pip = data_config["data_information"]["pip"]
#we want to make it s.t. if the 'newdata' param is False, then there must be an accompanying file directory
if data_config["data_and_directories"][
"newData"] == False and previous_data_name == False:
print(
'\nyou need to input a directory for the chosen file dummboy')
sys.exit(2)
#creating a very specific directory
temp_dir = ''
for currency in data_config["data_information"]["currencies"].split(
','):
temp_dir = temp_dir + '_' + tickDir(currency)
#get data for all specified tickers. NOTE as is, this will only create a df as long as the shortest time series
if data_config["data_and_directories"]["newData"] == False:
full_df = dePickler(data_directory + previous_data_name)
temp_dir = 'period_' + str(period) + '_pulledAt_' + str(
time.time())[:10] + '_' + temp_dir + '_' + str(
full_df.index.values[-1]).replace('.', '').replace(
':', '').replace(':', '') + '_to_' + str(
full_df.index.values[0]).replace('.', '').replace(
':', '').replace(':', '') + '.pickle'
else:
dataObj = GetData(
exchange=data_config["data_information"]["exchange"],
directory=data_directory,
period=data_config["data_information"]["period"],
asset_type=data_config["data_information"]["asset_type"],
newdata=data_config["data_and_directories"]["newData"])
full_df = None
#for each currency, fetch the time series and create a full dataframe
for currency in data_config["data_information"][
"currencies"].split(','):
data = dataObj.fetch(currency)
#make the df include seperate names for open high low and close
for label in ['Close', 'Open', 'High', 'Low', 'Volume']:
if full_df is None:
full_df = pd.DataFrame(data[label])
full_df.columns = [currency + '_' + label]
else:
full_df[currency + '_' + label] = data[label]
temp_dir = 'period_' + str(period) + '_pulledAt_' + str(
time.time())[:10] + '_' + temp_dir + '_' + str(
full_df.index.values[-1]).replace('.', '').replace(
':', '').replace(':', '') + '_to_' + str(
full_df.index.values[0]).replace('.', '').replace(
':', '').replace(':', '') + '.pickle'
#create features for all of the data and save
print('Creating Features')
mlObj = MLstrategy(data=full_df,
currencies=data_config["data_information"]
["currencies"].split(','),
model_directory=main_src_directory + 'models/',
pip=pip)
#if we're not collecting new data, then no need to create new data. Otherwise, create new features
if data_config["data_and_directories"]["newData"] == False:
df = full_df
else:
df = mlObj.preprocessIndicators(
forecastLen=data_config["data_information"]["forecastLen"])
pickler(data_directory + 'INDICATORS_ADDED_' + temp_dir, df)
print(full_df)
return full_df
def main():
data = GetData('poloniex', newdata=True)
tickers = [
'LTC/USDT'
] #,'BTC/USDT'] #tickers = data.tickers()
## tickers = ['OMG/BTC']#,'ETH/BTC','XMR/BTC','ZEC/BTC','BTC/USDT']
periods = ['5m', '15m', '30m', '1h'] #periods = ['5m','15m','30m','1h']
maximum_parameters = []
proportion_test = 0.1
graph_results = True
data.fetch('BTC/USDT') # use if list length error'ETH/USDT','XRP/USDT',
type_coin = 'USDT'
for tick in tickers:
if tick[-len(type_coin):] == type_coin:
data.fetch(tick)
temp_results = []
for numsimul in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
for period in periods:
'''formats the bitcoin and current coin data to the right period'''
tick_data = data.periodFormatter(tick, period)
startDate = tick_data.index[0]
btc_data = data.periodFormatter('BTC/USDT', period,
startDate)
'''formats the raw data to the proportion of data chosen'''
startDate = tick_data.index[int(
(1 - proportion_test) * len(tick_data))]
endDate = tick_data.index[-1]
btc_prices = btc_data.loc[startDate:endDate]['Close']
tick_prices = tick_data.loc[startDate:endDate]['Close']
if len(tick_prices) != len(btc_prices):
tick_prices.drop(tick_prices.index[0], inplace=True)
if len(tick_prices) == len(
btc_prices) or tick[-len(type_coin):] == type_coin:
strategy = Strategy(len(tick_prices), numsimul)
for count, price in enumerate(tick_prices.values):
if type_coin == 'BTC':
strategy.tick(price, btc_prices.values[count])
elif type_coin == 'USDT':
strategy.tick(price)
strategy.macd(26, 12, 9, count, len(tick_prices))
temp_results.append([
tick, period, strategy.profit, strategy.balance,
tick_prices, strategy.numtrades,
min(strategy.balanceList), strategy
])
else:
print('length error')
break
optimumParam = None
for result in temp_results:
if optimumParam == None:
optimumParam = result
optimum = result
elif result[3] > optimumParam[3]:
optimumParam = result
else:
pass
print(optimumParam[0], optimumParam[1],
'\nProfit on one coin per trade:', optimumParam[2],
'\nBalance on', optimumParam[-1].USDpertrade,
'USD per trade:', optimumParam[3],
'\nNumber of simeltaneuos trades:',
optimumParam[-1].numSimul, '\nNumber of trades:',
optimumParam[-1].numtrades)
maximum_parameters.append(optimumParam)
for param in maximum_parameters:
plot = Graphics(param[4],
bal=param[-1].balanceList,
buylist=param[-1].buylist,
selllist=param[-1].selllist,
MACD=param[-1].MACD,
signal=param[-1].signal,
EMAfast=param[-1].EMAfast,
EMAslow=param[-1].EMAslow)
plot.MACD_plot(26)
def main():
data = GetData('poloniex', newdata=False)
tickers = ['OMG/BTC'] #tickers = data.tickers()
periods = ['30m'] #periods = ['5m','15m','30m','1h']
maximum_parameters = []
proportion_test = 0.1
graph_results = True
#data.fetch('BTC/USDT') # use if list length error
for tick in tickers:
if tick[-3:] == 'BTC':
data.fetch(tick)
temp_results = []
for period in periods:
'''formats the bitcoin and current coin data to the right period'''
tick_data = data.periodFormatter(tick, period)
startDate = tick_data.index[0]
btc_data = data.periodFormatter('BTC/USDT', period, startDate)
prices = [close for close in tick_data['Close']]
'''formats the raw data to the proportion of data chosen'''
startDate = tick_data.index[int(
(1 - proportion_test) * len(prices))]
endDate = tick_data.index[-1]
btc_prices = btc_data.loc[startDate:endDate]['Close']
tick_prices = tick_data.loc[startDate:endDate]['Close']
for MAlong in [10, 15, 30]:
for x in [2, 3, 4]:
MAshort = int(MAlong / x)
if len(tick_prices) == len(btc_prices):
strategy = Strategy(len(tick_prices))
for count, price in enumerate(tick_prices.values):
strategy.tick(price, btc_prices.values[count])
strategy.movingaverage(MAlong, MAshort, count)
profit, balance = strategy.returnParam()
temp_results.append([
tick, period, tick_prices, strategy, profit,
balance, MAlong, MAshort
])
else:
print('length error')
break
optimumParam = None
for result in temp_results:
if optimumParam == None:
optimumParam = result
optimum = result
elif result[5] > optimumParam[5]:
optimumParam = result
else:
pass
print(optimumParam[0], optimumParam[1], optimumParam[4],
optimumParam[5], optimumParam[6], optimumParam[7])
maximum_parameters.append(optimumParam)
for param in maximum_parameters:
plot = Graphics(param[2], param[3].MAlong, param[3].MAshort,
param[3].buylist, param[3].selllist,
param[3].balanceList)
plot.MA_plot()