def output_feeds(instrument: str, st: datetime, et: datetime, short_win: int,
long_win: int, ema_period: int,
save_dir: str) -> pd.DataFrame:
"""
Output ohlc price feeds to csv for strategy back testing
:param instrument: ccy_pair
:param st: start date
:param et: end date
:param short_win:
:param long_win:
:param ema_period:
:param save_dir:
:return:
"""
pd_h1 = read_price_df(instrument=instrument,
granularity='H1',
start=st,
end=et)
pd_h1[f'last_{long_win}_high'] = pd_h1['high'].rolling(window=long_win *
24).max()
pd_h1[f'last_{short_win}_high'] = pd_h1['high'].rolling(window=short_win *
24).max()
pd_h1[f'last_{long_win}_low'] = pd_h1['low'].rolling(window=long_win *
24).min()
pd_h1[f'last_{short_win}_low'] = pd_h1['low'].rolling(window=short_win *
24).min()
pd_h1.to_csv(f'{save_dir}/{instrument.lower()}_h1.csv')
pd_d = read_price_df(instrument=instrument,
granularity='D',
start=st,
end=et)
pd_d[f'last_{long_win}_high'] = pd_d['high'].rolling(window=long_win).max()
pd_d[f'last_{short_win}_high'] = pd_d['high'].rolling(
window=short_win).max()
pd_d[f'last_{long_win}_low'] = pd_d['low'].rolling(window=long_win).min()
pd_d[f'last_{short_win}_low'] = pd_d['low'].rolling(window=short_win).min()
pd_d['atr'] = TA.ATR(pd_d, period=14)
pd_d['adx'] = TA.ADX(pd_d, period=14)
pd_d['rsi'] = TA.RSI(pd_d, period=14)
pd_d[f'ema_{ema_period}'] = TA.EMA(pd_d, period=ema_period)
pd_d.to_csv(f'{save_dir}/{instrument.lower()}_d.csv')
pd_h1.reset_index(level=0, inplace=True)
pd_merged = pd_h1.apply(partial(enrich, pd_d, ema_period),
axis=1).set_index('time')
logger.info(pd_merged.info())
pd_merged.to_csv(f"{save_dir}/{instrument.lower()}_h1_enrich.csv")
logger.info(f'output feeds complete for [{instrument}]!')
return pd_merged
def check_for_signals(self, instrument: str) -> pd.DataFrame:
param = {"count": 240, "granularity": "H1"}
resp = api_request(instrument=instrument, p=param)
df = pd.DataFrame(transform(resp['candles'])).set_index('time')
df['ema_short'] = TA.EMA(df, period=self.short_win)
df['ema_long'] = TA.EMA(df, period=self.long_win)
df['atr'] = TA.ATR(df, 14)
df['signal'] = 0.0
# Create a 'signal' (invested or not invested) when the short moving average crosses the long
# moving average, but only for the period greater than the shortest moving average window
df['signal'][self.short_win:] = np.where(
df['ema_short'][self.short_win:] > df['ema_long'][self.short_win:],
1.0, 0.0)
# Take the difference of the signals in order to generate actual trading orders
df['position'] = df['signal'].diff()
# df.to_csv(r'C:\temp\signals.csv')
return df
def sample_data(instrument: str,
start: datetime,
end: datetime,
short_window: int = 100,
long_window: int = 350) -> pd.DataFrame:
price_feed = read_price_df(instrument=instrument,
granularity='D',
start=start,
end=end)
price_feed[f'smma_{short_window}'] = TA.SMMA(price_feed,
period=short_window,
adjust=False)
price_feed[f'smma_{long_window}'] = TA.SMMA(price_feed,
period=long_window,
adjust=False)
price_feed['atr'] = TA.ATR(price_feed[['high', 'low', 'close']])
price_feed['signal'] = price_feed.apply(partial(signal, short_window,
long_window),
axis=1)
return price_feed
def generate_price_feed(instrument: str,
start: datetime = None,
end: datetime = None,
persist_dir: str = 'c:/temp'):
start = start or datetime(2005, 1, 1) # earliest date support by Oanda
end = end or datetime.today() - timedelta(days=1)
pd_h1 = read_price_df(instrument=instrument,
granularity='H1',
start=start,
end=end)
pd_d = read_price_df(instrument=instrument,
granularity='D',
start=start,
end=end)
pd_h1[['macd', 'signal']] = TA.MACD(pd_h1)
pd_h1['ema_200'] = TA.EMA(pd_h1, period=200)
pd_h1['atr'] = TA.ATR(pd_h1)
pd_h1['rsi'] = TA.RSI(pd_h1)
pd_d['atr'] = TA.ATR(pd_d)
pd_d['rsi'] = TA.RSI(pd_d)
pd_h1.reset_index(level=0, inplace=True)
pd_h1 = pd_h1.apply(partial(_enrich, pd_d), axis=1).set_index('time')
print(pd_h1)
pd_h1.to_csv(f'{persist_dir}/{instrument.lower()}_macd.csv')
def output_feeds(instrument: str,
short_win: int,
long_win: int,
ema_period: int,
save_dir: str,
st: datetime,
et: datetime = None) -> pd.DataFrame:
"""
Output daily ohlc price feeds to csv
:param instrument: ccy_pair
:param st: start date
:param et: end date
:param short_win:
:param long_win:
:param ema_period:
:param save_dir:
:return:
"""
pd_d = read_price_df(instrument=instrument,
granularity='D',
start=st,
end=et)
pd_d[f'last_{long_win}_high'] = pd_d['high'].rolling(window=long_win).max()
pd_d[f'last_{short_win}_high'] = pd_d['high'].rolling(
window=short_win).max()
pd_d[f'last_{long_win}_low'] = pd_d['low'].rolling(window=long_win).min()
pd_d[f'last_{short_win}_low'] = pd_d['low'].rolling(window=short_win).min()
pd_d['atr'] = TA.ATR(pd_d, period=14)
pd_d['adx'] = TA.ADX(pd_d, period=14)
pd_d['rsi'] = TA.RSI(pd_d, period=14)
pd_d[f'ema_{ema_period}'] = TA.EMA(pd_d, period=ema_period)
pd_d.to_csv(f'{save_dir}/{instrument.lower()}_d.csv')
logger.info(f'output feeds complete for [{instrument}]!')
return pd_d