class EMAStrategy(Strategy):
indicators = {'emaL': EMA([100]), 'emaS': EMA([20])}
def onEnter(self, update):
iv = self.indicatorValues()
emaS = self.indicators['emaS']
s = iv['emaS']
l = iv['emaL']
if emaS.crossed(l):
if s > l:
return self.openLongPositionMarket({
'mtsCreate': update['mts'],
'price': update['price'],
'amount': 1,
})
else:
return self.openShortPositionMarket({
'mtsCreate': update['mts'],
'price': update['price'],
'amount': 1,
})
def onUpdateShort(self, update):
iv = self.indicatorValues()
s = iv['emaS']
l = iv['emaL']
if s < l:
return self.closePositionMarket({
'mtsCreate': update['mts'],
'price': update['price']
})
def onUpdateLong(self, update):
iv = self.indicatorValues()
s = iv['emaS']
l = iv['emaL']
if s > l:
return self.closePositionMarket({
'mtsCreate': update['mts'],
'price': update['price']
})
import sys
import asyncio
sys.path.append('../')
from hfstrategy import Strategy
from bfxhfindicators import EMA
# Initialise strategy
strategy = Strategy(
symbol='tBTCUSD',
indicators={
'emaL': EMA([100]),
'emaS': EMA([20])
},
exchange_type=Strategy.ExchangeType.EXCHANGE,
logLevel='INFO'
)
@strategy.on_enter
async def enter(update):
emaS = strategy.get_indicators()['emaS']
emaL = strategy.get_indicators()['emaL']
if emaS.crossed(emaL.v()):
if emaS.v() > emaL.v():
await strategy.open_long_position_market(mtsCreate=update.mts, amount=1)
else:
await strategy.open_short_position_market(mtsCreate=update.mts, amount=1)
@strategy.on_update_short
async def update_short(update, position):
if (position.amount == 0):
import sys
import asyncio
sys.path.append('../')
import time
from hfstrategy import Strategy
from bfxhfindicators import EMA
import nest_asyncio
nest_asyncio.apply()
# Initialise strategy
strategy = Strategy(
symbol='tBTCUSD',
indicators={
# see https://github.com/bitfinexcom/bfx-hf-indicators-py for more info
'emaL': EMA(100),
'emaS': EMA(20)
},
exchange_type=Strategy.ExchangeType.EXCHANGE,
logLevel='INFO')
@strategy.on_enter
async def enter(update):
emaS = strategy.get_indicators()['emaS']
emaL = strategy.get_indicators()['emaL']
if emaS.crossed(emaL.v()):
if emaS.v() > emaL.v():
await strategy.open_long_position_market(mtsCreate=update.mts,
amount=1)
else:
await strategy.open_short_position_market(mtsCreate=update.mts,
def run_extract_candles():
# create results folder if it doesn't exist
if not os.path.exists('results/'):
os.makedirs('results/')
# start with blank files
open('results/below_55.txt', 'w').close()
open('results/above_55_below_200.txt', 'w').close()
open('results/above_200.txt', 'w').close()
# load symbols information
print('Getting list of BTC trade pairs...')
resp = requests.get(BASE_URL + '/api/v1/ticker/allBookTickers')
#print(resp.content)
tickers_list = json.loads(resp.content)
for ticker in tickers_list:
if str(ticker['symbol'])[-3:] == 'BTC':
symbols.append(ticker['symbol'])
# get 4h candles for symbols
print('Loading candle data for symbols...')
for sym in symbols:
#print('BINANCE:' + sym)
Thread(target=load_candles, args=(sym,)).start()
while len(candles) < len(symbols):
print('%s/%s loaded' %(len(candles), len(symbols)), end='\r', flush=True)
time.sleep(0.1)
# calculate EMAs for each symbol
print('Calculating EMAs...')
for sym in candles:
for period in EMA_PERIODS:
iEMA = EMA(period)
lst_candles = candles[sym][:]
for c in lst_candles:
iEMA.add(c['close'])
if sym not in ema_values:
ema_values[sym] = {}
ema_values[sym][period] = iEMA.v()
# save filtered EMA results in txt files
print('Saving filtered EMA results to txt files...')
for sym in ema_values:
ema_55 = ema_values[sym][55]
ema_200 = ema_values[sym][200]
price = prices[sym]
entry = ''
if price < ema_200:
# save symbols trading below EMA (50)
f = open('results/below_55.txt', 'a')
entry = '%s: $%s\n' %(sym, round(price,3))
f.write(entry)
elif price > ema_55 and price < ema_200:
# save symbols trading above EMA(200)
f = open('results/above_55_below_200.txt', 'a')
entry = '%s: $%s\n' %(sym, round(price,3))
f.write(entry)
elif price > ema_200:
# save symbols trading above EMA(50) but below EMA(200)
f = open('results/above_200.txt', 'a')
entry = '%s: $%s\n' %(sym, round(price,3))
f.write(entry)
f.close()
del f # cleanup
print('All done! Results saved in results folder.')
if str(ticker['symbol'])[-4:] == 'USDT':
symbols.append(ticker['symbol'])
# get 4h candles for symbols
print('Loading candle data for symbols...')
for sym in symbols:
Thread(target=load_candles, args=(sym,)).start()
while len(candles) < len(symbols):
print('%s/%s loaded' %(len(candles), len(symbols)), end='\r', flush=True)
time.sleep(0.1)
# calculate EMAs for each symbol
print('Calculating EMAs...')
for sym in candles:
for period in EMA_PERIODS:
iEMA = EMA([period])
lst_candles = candles[sym][:]
for c in lst_candles:
iEMA.add(c['close'])
if sym not in ema_values:
ema_values[sym] = {}
ema_values[sym][period] = iEMA.v()
# save filtered EMA results in txt files
print('Saving filtered EMA results to txt files...')
for sym in ema_values:
ema_50 = ema_values[sym][50]
ema_200 = ema_values[sym][200]
price = prices[sym]
entry = ''
if price < ema_50:
# import candles for each symbol
Thread(target=show_progress).start() # show progress
for sym in symbols:
Thread(target=import_candles, args=(sym,)).start()
# wait until all candles are loaded
while go_on == False:
time.sleep(1)
print('\nAll candles loaded.')
# calculate EMA values
print('Calculating EMA values and scanning for crosses...', end='', flush=True)
for sym in symbols:
for period in ema_periods:
iEMA = EMA([period]) # define EMA object
for candle in candles[sym]:
iEMA.add(candle['close']) # add all close prices
lst_ema = []
lst_ema.append(iEMA.v()) # add current EMA value
for i in range(historic_window):
# add historic EMA values
lst_ema.append(iEMA.prev(i+1))
if sym not in ema_values: # add symbol key to dictionary
ema_values[sym] = {}
ema_values[sym][period] = lst_ema # save EMA values
# identify EMA crosses
ema_results = {
'cross-downs': [],
'cross-ups': []
def single_run(ema_l, ema_s, quick_profit_target_percentage,
profit_target_percentage, stop_loss_percentage):
# Initialise strategy
strategy = Strategy(
symbol='tBTCUSD',
indicators={
# see https://github.com/bitfinexcom/bfx-hf-indicators-py for more info
'emaL': EMA(ema_l),
'emaS': EMA(ema_s)
},
exchange_type=Strategy.ExchangeType.EXCHANGE,
logLevel='INFO')
async def enter_long(update):
amount = 1000 / update.price
await strategy.open_long_position_market(mtsCreate=update.mts,
amount=amount)
# set profit target to 5% above entry
profit_target = update.price + (update.price *
profit_target_percentage)
# set a tight stop los of %2 below entry
stop_loss = update.price - (update.price * stop_loss_percentage)
# update positions with new targets
await strategy.set_position_target(profit_target)
await strategy.set_position_stop(stop_loss)
async def enter_short(update):
amount = 1000 / update.price
await strategy.open_short_position_market(mtsCreate=update.mts,
amount=amount)
# same as above, take full proft at 5%
profit_target = update.price - (update.price *
profit_target_percentage)
# set stop loss to %2 below entry
stop_loss = update.price + (update.price * stop_loss_percentage)
await strategy.set_position_target(profit_target)
await strategy.set_position_stop(stop_loss)
@strategy.on_enter
async def enter(update):
# We are going to use the ema cross for entrance
emaS = strategy.get_indicators()['emaS']
emaL = strategy.get_indicators()['emaL']
# enter market if ema crosses
if emaS.crossed(emaL.v()):
if emaS.v() > emaL.v():
await enter_long(update)
else:
await enter_short(update)
@strategy.on_update_short
async def update_short(update, position):
emaS = strategy.get_indicators()['emaS']
emaL = strategy.get_indicators()['emaL']
# if emas cross then just exit the position
if emaS.v() > emaL.v():
return await strategy.close_position_market(mtsCreate=update.mts)
## if we are up by 2% then take 50% profit and set stop loss to
## entry price
# get entry of initial order
entry = position.get_entry_order().price
half_position = abs(position.amount) / 2
if half_position < 0.1:
return
if update.price < entry - (position.price *
quick_profit_target_percentage):
print("Reached profit target, take 2%")
await strategy.update_position_market(mtsCreate=update.mts,
amount=half_position,
tag="Hit profit target")
# set our stop loss to be our original entry price
# here we will set our stop exit type to be a limit order.
# This will mean we will only be charged maker fees and since we are in profit
# we dont need to exit the position instantly with a market order
await strategy.set_position_stop(
entry, exit_type=Position.ExitType.MARKET)
@strategy.on_update_long
async def update_long(update, position):
emaS = strategy.get_indicators()['emaS']
emaL = strategy.get_indicators()['emaL']
# Market is going to change direction so exit position
if emaS.v() < emaL.v():
return await strategy.close_position_market(mtsCreate=update.mts)
# Same as above, take profit at 2% and set stop to entry
# get entry of initial order
entry = position.get_entry_order().price
half_position = abs(position.amount) / 2
if half_position < 0.1:
return
if update.price > entry + (position.price *
quick_profit_target_percentage):
print("Reached profit target, take 2%")
await strategy.update_position_market(mtsCreate=update.mts,
amount=-half_position,
tag="Hit mid profit target")
# set our stop loss to be our original entry price
await strategy.set_position_stop(
entry, exit_type=Position.ExitType.MARKET)
from hfstrategy import Executor
os.mkdir("current_run")
Executor(strategy, timeframe='1hr',
show_chart=False).offline(file='btc_candle_data.json')
# Executor(strategy, timeframe='1m').backtest_live()
# import time
# now = int(round(time.time() * 1000))
# then = now - (1000 * 60 * 60 * 24 * 15) # 15 days ago
# Executor(strategy, timeframe='30m').with_data_server(then, now)
with open('current_run/results.json') as json_file:
total_profit_loss = json.load(json_file)['total_profit_loss']
shutil.rmtree("current_run")
return total_profit_loss