def backtest_exit(self, current_position, data):
try:
self.condictions_state_backtest = {
'buy': {
'ema': False,
},
'sell': {
'ema': False,
}
}
if len(data.date) > max(self.ema_1_period, self.ema_2_period):
data['ema_1'] = ema(list(data.bidclose), self.ema_1_period)
data['ema_2'] = ema(list(data.bidclose), self.ema_2_period)
if current_position == 'buy':
if ((data.ema_1.iloc[-1] > data.ema_2.iloc[-1])):
self.condictions_state_backtest['sell']['ema'] = True
self.condictions_state_backtest['buy']['ema'] = False
return 'exit'
elif current_position == 'sell':
if ((data.ema_1.iloc[-1] < data.ema_2.iloc[-1])):
self.condictions_state_backtest['buy']['ema'] = True
self.condictions_state_backtest['sell']['ema'] = False
return 'exit'
else:
self.condictions_state_backtest['buy']['ema'] = False
self.condictions_state_backtest['sell']['ema'] = False
return None
else:
return None
except:
return None
def ma(data: list, data_properties: dict, ma_type: int = 0) -> dict:
"""
Strategy for moving averages.
:param data: list[numeric]
:param data_properties: dict
:param ma_type: Moving Average Type
SMA = 0 (Default case)
EMA = 1
:return: dict
Result from strategy_builder
"""
close = data_parser.get_close(data)
if ma_type == 1:
ma50 = indicators.ema(close, period=50)
ma200 = indicators.ema(close, period=200)
else:
ma50 = indicators.sma(close, period=50)
ma200 = indicators.sma(close, period=200)
buy = Condition(data1=ma50, data2=ma200, operation=Operation.CROSSOVER)
sell = Condition(data1=ma50, data2=ma200, operation=Operation.CROSSUNDER)
chart_1 = ChartElement(data=ma50, label="ma50", chart_type=ChartType.LINE, plot=ChartAxis.SAME_AXIS)
chart_2 = ChartElement(data=ma200, label="ma200", chart_type=ChartType.LINE, plot=ChartAxis.SAME_AXIS)
charts = [chart_1, chart_2]
result = strategy_builder(data_properties=data_properties, data_list=data, strategy=BUY, buy=buy, sell=sell,
charts=charts)
return result
def _candle_indicators():
# with dpg.collapsing_header(label='indicators'):
indicators.rlines()
indicators.sma()
indicators.ema()
indicators.rsi()
indicators.bbands()
def setup(self, client=None):
tod = datetime.today()
fromdt = (tod - timedelta(days=120)).date()
todt = (tod - timedelta(days=1)).date()
nifty = client.get_instrument_by_symbol('nse_index', N50_SYMBOL)
ohlc_arr = self._get_ohlc(client, nifty, fromdt, todt)
import csv
with open('nifty_ohlc_sample.csv', 'w') as cfile:
fields = ['timestamp', 'open', 'high', 'low', 'close']
writer = csv.DictWriter(cfile, fieldnames=fields)
writer.writeheader()
for ohlc in ohlc_arr:
row = {
'timestamp':
ts_to_datetime(ohlc['timestamp']).strftime('%d-%m-%Y'),
'open':
ohlc['open'],
'high':
ohlc['high'],
'low':
ohlc['low'],
'close':
ohlc['close']
}
writer.writerow(row)
for i, ohlc in enumerate(ohlc_arr):
if i <= SLOW_EMA + 1:
continue
ema_3 = []
ema_5 = []
ema_3 = (ema(ohlc_arr[i - FAST_EMA - 2:i - 1],
n=FAST_EMA,
seed=None),
ema(ohlc_arr[i - FAST_EMA - 1:i], n=FAST_EMA, seed=None))
ema_5 = (ema(ohlc_arr[i - SLOW_EMA - 2:i - 1:],
n=SLOW_EMA,
seed=None),
ema(ohlc_arr[i - SLOW_EMA - 1:i], n=SLOW_EMA, seed=None))
cross = self._check_crossover(ema_3, ema_5)
if cross is not None:
d = ts_to_datetime(ohlc['timestamp']).date()
self.logger.debug('Crossover on %s. Direction = %s' %
(d.strftime('%d-%m-%Y'), cross))
self.logger.debug('3 EMAs = %.2f | %.2f' %
(ema_3[0], ema_3[1]))
self.logger.debug('5 EMAs = %.2f | %.2f' %
(ema_5[0], ema_5[1]))
self.logger.debug('-------------------------------\n')
def backtest_entry(self, data):
try:
self.condictions_state_backtest = {
'buy': {
'ema': False,
},
'sell': {
'ema': False,
}
}
if len(data.date) > max(self.ema_1_period, self.ema_2_period):
data['ema_1'] = ema(list(data.bidclose), self.ema_1_period)
data['ema_2'] = ema(list(data.bidclose), self.ema_2_period)
if ((data.ema_1.iloc[-1] > data.ema_2.iloc[-1])):
self.condictions_state_backtest['sell']['ema'] = True
self.condictions_state_backtest['buy']['ema'] = False
elif ((data.ema_1.iloc[-1] < data.ema_2.iloc[-1])):
self.condictions_state_backtest['buy']['ema'] = True
self.condictions_state_backtest['sell']['ema'] = False
else:
self.condictions_state_backtest['buy']['ema'] = False
self.condictions_state_backtest['sell']['ema'] = False
self.required_score
collected_score_buy = 0
collected_score_sell = 0
for k, v in self.conditions_weights.items():
if self.condictions_state_backtest['buy'][k] == True:
collected_score_buy += v
elif self.condictions_state_backtest['sell'][k] == True:
collected_score_sell += v
if collected_score_buy >= self.required_score:
return 'buy'
elif collected_score_sell >= self.required_score:
return 'sell'
else:
return None
else:
return None
except Exception as e:
print(e, 11111111111111)
return None
def short_close(data_open, data_close):
current_price = 0.0
ema_thirty_close = ind.ema(data_close, 30)
ema_close = ind.ema(data_close, 9)
ema_open = ind.ema(data_open, 9)
boolean = False
logging.info('9Ema: {} - 30Ema: {}'.format(ema_close, ema_thirty_close))
if (ema_close > ema_thirty_close) and (ema_open < ema_thirty_close):
boolean = True
logging.info('Short close: {}'.format(boolean))
return boolean
def testPolicy(symbol='JPM',
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31)):
bb_indicator = bb(symbol=[symbol], sd=sd, ed=ed)
sma_indicator = sma(symbol=[symbol], sd=sd, ed=ed)
ema_indicator = ema(symbol=[symbol], sd=sd, ed=ed)
#create new strategy df
strategy = pd.DataFrame(index=bb_indicator.index)
#thresholds and buy/sell/hold flags
strategy['sma'] = [
-1 if x > 1.02 else 1 if x < 0.98 else 0 for x in sma_indicator
]
strategy['bb'] = [
-1 if x > 1.5 else 1 if x < -.5 else 0 for x in bb_indicator
]
strategy['ema'] = [
-1 if x > 1.02 else 1 if x < .98 else 0 for x in ema_indicator
]
strategy['holding'] = [
0 if x == 0 else 1000 if x > 0 else -1000 for x in strategy.sum(axis=1)
]
# print strategy
strategy[symbol] = 0
strategy[symbol].values[
1:] = strategy['holding'].values[1:] - strategy['holding'].values[:-1]
df_trades = strategy[[symbol]]
return df_trades
# if __name__ == "__main__":
# df_trades = testPolicy()
# print df_trades
def short_open(data_open, data_close):
current_price = 0.0
alma_close = ind.alma(data_close, 300)
ema_close = ind.ema(data_close, 9)
ema_open = ind.ema(data_open, 9)
boolean = False
logging.info('9Ema Close: {} - 9Ema Open: {} - 300Alma: {}'.format(
ema_close, ema_open, alma_close))
if (ema_close < alma_close) and (ema_open > alma_close):
boolean = True
logging.info('Short open: {}'.format(boolean))
return boolean
def add_ema_to_dataframe(df, period=21):
ema_array = pd.DataFrame(df['Close']).to_numpy()
ema_data = ema(ema_array, period)
padding = [None for _ in range(period)]
ema_data = np.insert(ema_data, 0, padding)
name = f'ema{period}'
df[name] = pd.Series(ema_data)
return df
def get_discretized_indicators(sd, ed, symbol):
prices, _ = get_df_prices(sd, ed, symbol)
# EMA 2 States: Price <= EMA: 0, Price > EMA: 1
ema_20 = indicators.ema(sd, ed, symbol, window_size=20)
ema_30 = indicators.ema(sd, ed, symbol, window_size=30)
ema_50 = indicators.ema(sd, ed, symbol, window_size=50)
ema_20 = (prices > ema_20) * 1
ema_30 = (prices > ema_30) * 1
ema_50 = (prices > ema_50) * 1
# MACD 2 States: MACD <= Signal: 0, MACD > Signal: 1
macd_raw, macd_signal = indicators.macd(sd, ed, symbol)
macd = (macd_raw > macd_signal) * 1
# TSI 2 States: TSI <= 0: 0, TSI > 0: 1
tsi = indicators.tsi(sd, ed, symbol)
tsi = (tsi > 0) * 1
return ema_20, ema_30, ema_50, macd, tsi
def backtest_exit(self, current_position, data):
try:
self.condictions_state_backtest = {
'buy': {
'linear_regression_channel': False,
'r_percent': False,
'cmf': False
},
'sell': {
'linear_regression_channel': False,
'r_percent': False,
'cmf': False
}
}
if len(data.date) > exit * 2:
data = data.iloc[(len(data.date) -
(2 * max(self.lrc_period_1, self.
lrc_period_2, self.lrc_period_3))):]
linear_regression_exit, upper_line_exit, lower_line_exit, slope_exit, channel_width_exit = linear_regression_channel(
data, self.lrc_period_exit, self.lrc_std_exit)
data['cmf'] = cmf(list(data.bidclose), list(data.bidhigh),
list(data.bidlow), list(data.tickqty),
self.cmf_period)
data['cmf_ma'] = ema(list(data.cmf), self.cmf_ma_period)
data['r_percent'] = r_percent(data, self.r_percent_period)
if current_position == 'buy':
if ((data.bidclose.iloc[-1] > upper_line_exit[-1])):
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = True
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = False
return 'exit'
elif current_position == 'sell':
if ((data.bidclose.iloc[-1] < lower_line_exit[-1])):
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = True
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = False
return 'exit'
else:
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = False
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = False
return None
else:
return None
except:
return None
def check_entry(self):
try:
self.get_data()
self.data['ema_1'] = ema(list(self.data.bidclose),
self.ema_1_period)
self.data['ema_2'] = ema(list(self.data.bidclose),
self.ema_2_period)
if ((self.data.ema_1.iloc[-1] > self.data.ema_2.iloc[-1])):
self.condictions_state['sell']['ema'] = True
self.condictions_state['buy']['ema'] = False
elif ((self.data.ema_1.iloc[-1] < self.data.ema_2.iloc[-1])):
self.condictions_state['buy']['ema'] = True
self.condictions_state['sell']['ema'] = False
else:
self.condictions_state['buy']['ema'] = False
self.condictions_state['sell']['ema'] = False
collected_score_buy = 0
collected_score_sell = 0
for k, v in self.conditions_weights.items():
if self.condictions_state['buy'][k] == True:
collected_score_buy += v
elif self.condictions_state['sell'][k] == True:
collected_score_sell += v
if collected_score_buy >= self.required_score:
return 'buy'
elif collected_score_sell >= self.required_score:
return 'sell'
else:
return None
except Exception as e:
print(e, 98555555986668)
return None
def check_exit(self, current_position):
try:
self.get_data()
self.data['ema_1'] = ema(list(self.data.bidclose),
self.ema_1_period)
self.data['ema_2'] = ema(list(self.data.bidclose),
self.ema_2_period)
if current_position == 'buy':
if ((self.data.ema_1.iloc[-1] > self.data.ema_2.iloc[-1])):
self.condictions_state['sell']['ema'] = True
self.condictions_state['buy']['ema'] = False
return 'exit'
elif current_position == 'sell':
if ((self.data.ema_1.iloc[-1] < self.data.ema_2.iloc[-1])):
self.condictions_state['buy']['ema'] = True
self.condictions_state['sell']['ema'] = False
return 'exit'
else:
self.condictions_state['buy']['ema'] = False
self.condictions_state['sell']['ema'] = False
return None
except Exception as e:
print(e, 454545)
def add_dataframe_indicators(stock_frame: pd.DataFrame) -> pd.DataFrame:
"""Adds the different technical indicator to our DataFrame.
### Parameters
----------
stock_frame : pd.DataFrame
A pandas DataFrame with the Open, Close,
High, and Low price.
### Returns
-------
pd.DataFrame
A Pandas DataFrame with the indicators added
to the data frame.
"""
# Add the MACD Indicator.
stock_frame = macd(stock_frame=stock_frame, fast_period=12, slow_period=26)
# Add the EMA Indicator.
stock_frame = ema(stock_frame=stock_frame, period=50, alpha=1.0)
return stock_frame
def check_exit(self, current_position):
try:
self.get_data()
linear_regression_exit, upper_line_exit, lower_line_exit, slope_exit, channel_width_exit = linear_regression_channel(
self.data, self.lrc_period_exit, self.lrc_std_exit)
self.data['cmf'] = cmf(list(self.data.bidclose),
list(self.data.bidhigh),
list(self.data.bidlow),
list(self.data.tickqty), self.cmf_period)
self.data['cmf_ma'] = ema(list(self.data.cmf), self.cmf_ma_period)
self.data['r_percent'] = r_percent(self.data,
self.r_percent_period)
if current_position == 'buy':
if ((self.data.bidclose.iloc[-1] > upper_line_exit[-1])):
self.condictions_state['sell'][
'linear_regression_channel'] = True
self.condictions_state['buy'][
'linear_regression_channel'] = False
return 'exit'
elif current_position == 'sell':
if ((self.data.bidclose.iloc[-1] < lower_line_exit[-1])):
self.condictions_state['buy'][
'linear_regression_channel'] = True
self.condictions_state['sell'][
'linear_regression_channel'] = False
return 'exit'
else:
self.condictions_state['buy'][
'linear_regression_channel'] = False
self.condictions_state['sell'][
'linear_regression_channel'] = False
return None
except Exception as e:
print(e)
return None
data = get_google_finance_intraday(ticker, period=900, days=100)
#30 mins
#data = get_google_finance_intraday(ticker, period=1800, days=100)
#60 mins
#data = get_google_finance_intraday(ticker, period=3600, days=100)
#daily chart
#data = get_google_finance_intraday(ticker, period=86400, days=100)
#data = get_google_finance_intraday("HCLTECH", 600)
#data.reset_index(drop=False)
#new_index = ['CLOSE','OPEN']
#data.reindex(new_index)
#print data
#exit(0)
#print ema(data)
ema(data, 5, 'CLOSE')
ema(data, 10, 'CLOSE')
#ema2 = ema(data, 5, 'CLOSE')
print " ---------------------"
add_row_count(data)
#print data
#print ema2
#print money_flow_index(data)
#data2 = data
#print data
print " ---------------------"
# RSI calculation
##['money_flow_index']
data2 = data
data = data.set_index('row_index')
def check_entry(self):
try:
self.get_data()
linear_regression_1, upper_line_1, lower_line_1, slope_1, channel_width_1 = linear_regression_channel(
self.data, self.lrc_period_1, self.lrc_std_1)
linear_regression_2, upper_line_2, lower_line_2, slope_2, channel_width_2 = linear_regression_channel(
self.data, self.lrc_period_2, self.lrc_std_2)
linear_regression_3, upper_line_3, lower_line_3, slope_3, channel_width_3 = linear_regression_channel(
self.data, self.lrc_period_3, self.lrc_std_3)
self.data['cmf'] = cmf(list(self.data.bidclose),
list(self.data.bidhigh),
list(self.data.bidlow),
list(self.data.tickqty), self.cmf_period)
self.data['cmf_ma'] = ema(list(self.data.cmf), self.cmf_ma_period)
self.data['r_percent'] = r_percent(self.data,
self.r_percent_period)
if ((self.data.bidclose.iloc[-1] > upper_line_1[-1])
or (self.data.bidclose.iloc[-1] > upper_line_2[-1])
or (self.data.bidclose.iloc[-1] > upper_line_3[-1])):
self.condictions_state['sell'][
'linear_regression_channel'] = True
self.condictions_state['buy'][
'linear_regression_channel'] = False
elif ((self.data.bidclose.iloc[-1] < lower_line_1[-1])
or (self.data.bidclose.iloc[-1] < lower_line_2[-1])
or (self.data.bidclose.iloc[-1] < lower_line_3[-1])):
self.condictions_state['buy'][
'linear_regression_channel'] = True
self.condictions_state['sell'][
'linear_regression_channel'] = False
else:
self.condictions_state['buy'][
'linear_regression_channel'] = False
self.condictions_state['sell'][
'linear_regression_channel'] = False
if self.data.r_percent.iloc[-2] > -20 and self.data.r_percent.iloc[
-1] < -20:
self.condictions_state['sell']['r_percent'] = True
self.condictions_state['buy']['r_percent'] = False
elif self.data.r_percent.iloc[
-2] < -80 and self.data.r_percent.iloc[-1] > -80:
self.condictions_state['buy']['r_percent'] = True
self.condictions_state['sell']['r_percent'] = False
else:
self.condictions_state['buy']['r_percent'] = False
self.condictions_state['sell']['r_percent'] = False
if self.data.cmf.iloc[-1] > self.data.cmf_ma.iloc[-1]:
self.condictions_state['sell']['cmf'] = False
self.condictions_state['buy']['cmf'] = True
else:
self.condictions_state['sell']['cmf'] = True
self.condictions_state['buy']['cmf'] = False
collected_score_buy = 0
collected_score_sell = 0
for k, v in self.conditions_weights.items():
if self.condictions_state['buy'][k] == True:
collected_score_buy += v
elif self.condictions_state['sell'][k] == True:
collected_score_sell += v
if collected_score_buy >= self.required_score:
return 'buy'
elif collected_score_sell >= self.required_score:
return 'sell'
else:
return None
except Exception as e:
print(e, 9898989898)
return None
def emaCloseBelow():
return bars['C'] <= ind.ema(bars['C'], 20)
def backtest_entry(self, data):
try:
self.condictions_state_backtest = {
'buy': {
'linear_regression_channel': False,
'r_percent': False,
'cmf': False
},
'sell': {
'linear_regression_channel': False,
'r_percent': False,
'cmf': False
}
}
if len(data.date) > max(self.lrc_period_1, self.lrc_period_2,
self.lrc_period_3):
data = data.iloc[(len(data.date) -
(2 * max(self.lrc_period_1, self.
lrc_period_2, self.lrc_period_3))):]
linear_regression_1, upper_line_1, lower_line_1, slope_1, channel_width_1 = linear_regression_channel(
data, self.lrc_period_1, self.lrc_std_1)
linear_regression_2, upper_line_2, lower_line_2, slope_2, channel_width_2 = linear_regression_channel(
data, self.lrc_period_2, self.lrc_std_2)
linear_regression_3, upper_line_3, lower_line_3, slope_3, channel_width_3 = linear_regression_channel(
data, self.lrc_period_3, self.lrc_std_3)
data['cmf'] = cmf(list(data.bidclose), list(data.bidhigh),
list(data.bidlow), list(data.tickqty),
self.cmf_period)
data['cmf_ma'] = ema(list(data.cmf), self.cmf_ma_period)
data['r_percent'] = r_percent(data, self.r_percent_period)
if ((data.bidclose.iloc[-1] > upper_line_1[-1])
or (data.bidclose.iloc[-1] > upper_line_2[-1])
or (data.bidclose.iloc[-1] > upper_line_3[-1])):
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = True
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = False
elif ((data.bidclose.iloc[-1] < lower_line_1[-1])
or (data.bidclose.iloc[-1] < lower_line_2[-1])
or (data.bidclose.iloc[-1] < lower_line_3[-1])):
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = True
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = False
else:
self.condictions_state_backtest['buy'][
'linear_regression_channel'] = False
self.condictions_state_backtest['sell'][
'linear_regression_channel'] = False
if data.r_percent.iloc[-2] > -10 and data.r_percent.iloc[
-1] < -20:
self.condictions_state_backtest['sell']['r_percent'] = True
self.condictions_state_backtest['buy']['r_percent'] = False
elif data.r_percent.iloc[-2] < -90 and data.r_percent.iloc[
-1] > -80:
self.condictions_state_backtest['buy']['r_percent'] = True
self.condictions_state_backtest['sell'][
'r_percent'] = False
else:
self.condictions_state_backtest['buy']['r_percent'] = False
self.condictions_state_backtest['sell'][
'r_percent'] = False
if data.cmf.iloc[-1] > data.cmf_ma.iloc[-1]:
self.condictions_state_backtest['sell']['cmf'] = False
self.condictions_state_backtest['buy']['cmf'] = True
else:
self.condictions_state_backtest['sell']['cmf'] = True
self.condictions_state_backtest['buy']['cmf'] = False
collected_score_buy = 0
collected_score_sell = 0
for k, v in self.conditions_weights.items():
if self.condictions_state_backtest['buy'][k] == True:
collected_score_buy += v
elif self.condictions_state_backtest['sell'][k] == True:
collected_score_sell += v
if collected_score_buy >= self.required_score:
return 'buy'
elif collected_score_sell >= self.required_score:
return 'sell'
else:
return None
else:
return None
except Exception as e:
print(e, 11111111111111)
return None
import client
import indicators
import pandas as pd
df = pd.read_csv('equities.csv', index_col='symbol')
df['price'] = pd.Series()
df['average_volatility'] = pd.Series()
df['average_volume'] = pd.Series()
for index, item in df.iterrows():
symbol = item.name
equity = client.get_history(symbol)
equity = indicators.day_range_pct(equity)
equity = indicators.ema(equity, 'volume', 'ema_volume')
equity = equity.join(
pd.Series(equity['ema_volume'] / 1000000, name="vol(M)"))
equity = indicators.ema(equity, 'day_range_pct', 'ema_day_range_pct')
if len(equity) > 0:
df.loc[symbol, 'price'] = equity['close'].tail(1).values
df.loc[symbol,
'average_volatility'] = equity['ema_day_range_pct'].tail(
1).values
df.loc[symbol, 'average_volume'] = equity['vol(M)'].tail(1).values
df.to_pickle('./equities.pkl')
def emaCloseAbove():
return bars['C'] >= ind.ema(bars['C'], 20)
def AmazingCrossoverStrategy(accountID, token, instrument, window, weight):
api = API(token, accountID)
history = api.get_candles(instrument, window)
try:
n = len(history) - 1
price = history['Close'][n]
decimal = len(price.split('.')[1])
price = float(price)
history['High'] = history['High'].astype(float)
history['Low'] = history['Low'].astype(float)
history['median_price'] = (history['High'] + history['Low']) / 2
history['rsi'] = rsi(history['median_price'], 10)
history['fast_sma'] = ema(history['Close'], 5)
history['slow_sma'] = ema(history['Close'], 10)
history[-1:].to_sql(instrument + '_' + window,
conn,
if_exists='append')
oo = api.openOrders()
# long logic
if history['fast_sma'][n] > history['slow_sma'][n]:
if instrument in oo:
if oo[instrument]['Bias'] == 'Short':
api.close(instrument)
else:
tradeID = oo[instrument]['tradeIDs'][0]
price = history['Close'][n]
tsl = api.update_order(tradeID,
price,
endpoint='trailingStopLoss')
print("AMAZING TRAILING STOP LOSS UPDATED")
else:
# if not look for an opportunity
#if history['rsi'][n] > 50.0:
stop_loss = str(round(float(price - (price * 0.002)), decimal))
take_profit = str(round(float(price + (price * 0.02)),
decimal))
try:
mo = api.order(instrument, weight, price, stop_loss,
take_profit)
print("AMAZING went long %s" % instrument)
except Exception as e:
print(e)
if history['fast_sma'][n] < history['slow_sma'][n]:
if instrument in oo:
if oo[instrument]['Bias'] == 'Long':
api.close(instrument)
else:
tradeID = oo[instrument]['tradeIDs'][0]
price = history['Close'][n]
tsl = api.update_order(tradeID,
price,
endpoint='trailingStopLoss')
print("AMAZING TRAILING STOP LOSS UPDATED")
else:
#if history['rsi'][n] < 50.0:
stop_loss = str(round(float(price + (price * 0.002)), decimal))
take_profit = str(round(float(price - (price * 0.02)),
decimal))
try:
mo = api.order(instrument, -weight, price, stop_loss,
take_profit)
print("AMAZING went short %s" % instrument)
except Exception as e:
print(e)
except:
print("candles unavailable right now: %s" % instrument)
def testPolicy(self, symbol, sd, ed, sv):
# setting up
symbol = symbol[0]
df = get_data([symbol], pd.date_range(sd, ed))
df_price = df[[symbol]]
df_price = df_price.ffill().bfill()
normalized_df_price = df_price[symbol] / df_price[symbol][0]
df_trades = df[['SPY']]
df_trades = df_trades.rename(columns={
'SPY': symbol
}).astype({symbol: 'int32'})
df_trades[:] = 0
dates = df_trades.index
# getting indicators
ema_20 = indicators.ema(sd, ed, symbol, plot=True, window_size=20)
macd_raw, macd_signal = indicators.macd(sd, ed, symbol, plot=True)
tsi = indicators.tsi(sd, ed, symbol, plot=True)
# current_cash = sv
current_position = 0
last_action = 0
# making trades
for i in range(len(dates)):
today = dates[i]
last_action += 1
# EMA_20 Vote
normalized_df_price_today = normalized_df_price.loc[today]
ema_20_today = ema_20.loc[today]
# if normalized_df_price_today > ema_20_today + 0.1:
if normalized_df_price_today > ema_20_today:
ema_vote = 1
# elif normalized_df_price_today < ema_20_today - 0.1:
elif normalized_df_price_today < ema_20_today:
ema_vote = -1
else:
ema_vote = 0
# MACD Vote
macd_raw_today = macd_raw.loc[today].loc[symbol]
macd_signal_today = macd_signal.loc[today].loc[symbol]
# if macd_signal_today > macd_raw_today + 0.2:
if macd_signal_today > macd_raw_today:
macd_vote = 2
# elif macd_signal_today < macd_raw_today - 0.2:
elif macd_signal_today < macd_raw_today:
macd_vote = -10
else:
macd_vote = 1
# TSI vote
tsi_today = tsi.loc[today].loc[symbol]
# if tsi_today > 0.05:
if tsi_today > 0.1:
tsi_vote = 1
# elif tsi_today < -0.05:
elif tsi_today < 0.1:
tsi_vote = -1
else:
tsi_vote = 0
# pooling the votes
pool = macd_vote + tsi_vote + ema_vote
if pool >= 3:
# long
action = 1000 - current_position
elif pool <= -3:
# short
action = -1000 - current_position
else:
# out
action = -current_position
# don't trade too frequent
if last_action >= 3:
df_trades.loc[dates[i]].loc[symbol] = action
current_position += action
last_action = 0
return df_trades
def emaCloseBelow():
return bars['C'] <= ind.ema(bars['C'], 20)
def emaCloseAbove():
return bars['C'] >= ind.ema(bars['C'], 20)
def chart():
"""
For plotting normal candle chart or along with indicators
:return: None
"""
# prop, data = data_parser.get_data(start_date="2017-08-18")
# result = Strategies.rsi(data, data_properties=prop)
# data_properties = result['data_properties']
# main_chart = []
# for key, values in data_properties.items():
# main_chart.append([key, values])
# params = result['params']
# data = result['data']
# print(params,data_with_indicators)
# final_data = data_with_indicators[1:]
# print(final_data)
data_prop, data = data_parser.get_data(start_date="2007-01-18")
high = data_parser.get_high(data)
low = data_parser.get_low(data)
close = data_parser.get_close(data)
sma = indicators.sma(close)
ema = indicators.ema(close)
macd = indicators.macd(close)
rsi = indicators.rsi(close)
stoch = indicators.stoch(high, low, close)
bbands = indicators.bollinger_bands(close)
pivot = indicators.pivot(data)
chart_1 = ChartElement(data=sma,
label="sma",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.YELLOW)
chart_2 = ChartElement(data=ema,
label="ema",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.PINK)
chart_3 = ChartElement(data=stoch,
label="stoch",
chart_type=ChartType.LINE,
plot=ChartAxis.DIFFERENT_AXIS,
color=ChartColor.PURPLE)
chart_4 = ChartElement(data=bbands,
label="bbands",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.BLUE)
chart_5 = ChartElement(data=pivot,
label="pivot",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.GREEN)
chart_6 = ChartElement(data=rsi,
label="rsi",
chart_type=ChartType.LINE,
plot=1,
color=ChartColor.RED)
chart_7 = ChartElement(data=macd,
label="macd",
chart_type=ChartType.LINE,
plot=2,
color="magenta")
charts = [chart_4, chart_6]
buy = Condition(data1=sma, data2=ema, operation=Operation.CROSSOVER)
sell = Condition(data1=rsi, data2=70, operation=Operation.GREATER_THAN)
result = strategy.strategy_builder(data_properties=data_prop,
data_list=data,
charts=charts,
buy=buy,
sell=sell,
target=1.0,
sl=0.5,
strategy=strategy.BUY)
# strategy.show_back_testing_reports(result, auto_open=True)
data_properties = result['data_properties']
main_chart = []
for key, values in data_properties.items():
main_chart.append([key, values])
params = result['params']
# print(params)
data_list = result['data']
return render_template("chart.html",
chartData=data_list,
chart_params=params,
main_chart_properties=main_chart)
def cum_pl_short():
"""
Chart for back test reports of Cumulative profit and loss
:return: None
"""
data_prop, data = data_parser.get_data(start_date="2000-01-18")
high = data_parser.get_high(data)
low = data_parser.get_low(data)
close = data_parser.get_close(data)
sma = indicators.sma(close)
ema = indicators.ema(close)
macd = indicators.macd(close)
rsi = indicators.rsi(close)
stoch = indicators.stoch(high, low, close)
bbands = indicators.bollinger_bands(close)
pivot = indicators.pivot(data)
chart_1 = ChartElement(data=sma,
label="sma",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.YELLOW)
chart_2 = ChartElement(data=ema,
label="ema",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.PINK)
chart_3 = ChartElement(data=stoch,
label="stoch",
chart_type=ChartType.LINE,
plot=ChartAxis.DIFFERENT_AXIS,
color=ChartColor.PURPLE)
chart_4 = ChartElement(data=bbands,
label="bbands",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.BLUE)
chart_5 = ChartElement(data=pivot,
label="pivot",
chart_type=ChartType.LINE,
plot=ChartAxis.SAME_AXIS,
color=ChartColor.GREEN)
chart_6 = ChartElement(data=rsi,
label="rsi",
chart_type=ChartType.LINE,
plot=1,
color=ChartColor.RED)
chart_7 = ChartElement(data=macd,
label="macd",
chart_type=ChartType.LINE,
plot=2,
color="magenta")
charts = [chart_1, chart_2, chart_3, chart_6]
buy = Condition(data1=sma, data2=ema, operation=Operation.CROSSOVER)
sell = Condition(data1=rsi, data2=70, operation=Operation.GREATER_THAN)
result = strategy.strategy_builder(data_properties=data_prop,
data_list=data,
charts=charts,
buy=buy,
sell=sell,
target=1.0,
sl=0.5,
strategy=strategy.BUY)
cum_short = result['short']['DATE_CUM_PL']
# print(cum_short)
return render_template("cum_pl_short.html", shortData=cum_short)
import numpy as np
import sys
import indicators
CANDLE_SIZE = 10
inds = [
indicators.ema(5),
indicators.ema(10),
indicators.ema(50),
indicators.ema(100),
indicators.ema(200),
indicators.sma(20),
indicators.sma(50),
indicators.rsi(14),
indicators.macd(12, 26),
indicators.accdistdelt(),
indicators.ichimoku_tenkan(),
indicators.ichimoku_kijun()
]
def main():
print "Loadng from txt"
idata = np.genfromtxt(sys.argv[1],
delimiter=',',
skip_header=2,
usecols=[1, 2, 3, 4, 6])
i = 0
def test_ema(self): input_quotes = [1.1403,1.1406,1.1406,1.1404,1.1406,1.1406,1.1405,1.1405,1.1406,1.1406,1.1406,1.1404,1.1405,1.1405,1.1405,1.1403,1.1404,1.1404,1.1405,1.1408,1.1406,1.1406,1.1404,1.1403,1.1404,1.1402,1.1401,1.1401,1.1388,1.1389,1.139,1.1391,1.139,1.1392,1.1392,1.1391,1.1396,1.14,1.1399,1.14,1.1401,1.1401,1.1399,1.1401,1.14,1.1396,1.1396,1.1398,1.14,1.1401,1.1402,1.1402,1.1401,1.14,1.1405,1.1407,1.1408,1.1411,1.1411,1.1413,1.1413,1.1408,1.1415,1.1415,1.1416,1.1415,1.1416,1.1418,1.1417,1.1415,1.142,1.1421,1.1421,1.1416,1.1415] wanted_ema = [1.1405,1.14053333333333,1.14052222222222,1.14051481481481,1.14054320987654,1.1405621399177,1.14057475994513,1.14051650663009,1.14051100442006,1.14050733628004,1.14050489085336,1.14043659390224,1.14042439593483,1.14041626395655,1.14044417597103,1.14056278398069,1.14057518932046,1.14058345954697,1.14052230636465,1.1404482042431,1.14043213616207,1.14035475744138,1.14026983829425,1.1402132255295,1.139742150353,1.13946143356867,1.13930762237911,1.13923841491941,1.13915894327961,1.13917262885307,1.13918175256871,1.13915450171248,1.13930300114165,1.13953533409443,1.13965688939629,1.13977125959753,1.13988083973168,1.13995389315446,1.13993592876964,1.13999061917976,1.13999374611984,1.13986249741323,1.13977499827548,1.13978333218366,1.1398555547891,1.13993703652607,1.14002469101738,1.14008312734492,1.14008875156328,1.14005916770885,1.1402061118059,1.14037074120394,1.14051382746929,1.14070921831286,1.14083947887524,1.14099298591683,1.14109532394455,1.1409968826297,1.1411645884198,1.14127639227987,1.14138426151991,1.14142284101327,1.14148189400885,1.14158792933923,1.14162528622616,1.14158352415077,1.14172234943385,1.1418482329559,1.14193215530393,1.14182143686929,1.14171429124619] def minus(x, y): return x-y diff = map(minus, wanted_ema, indicators.ema(input_quotes,5)) #diff for each item must be near zero self.assertTrue(sum(diff)/len(diff)<0.000001)
import client
import indicators
from datetime import datetime, timedelta
import pandas as pd
import matplotlib.pyplot as plt
# retrieve history
symbol = "AC"
df = client.get_history(symbol, days=800)
df
# %%
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
date_ago = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
# %%
dd = indicators.ema(df, "close", "ema_close", n=200)
dd = dd[date_ago:]
fw_low = dd[date_ago:]['close'].min()
fw_high = dd[date_ago:]['close'].max()
# normalize
dd
# %%
dd[['close','ema_close']].plot()
plt.show()
