def get_state(self, t):
"""
return the state (define state here) given time t
"""
# time feature
d = t - self.window_size
if d <= 0:
state = np.zeros((5, 5))
else:
feature1 = volume.acc_dist_index(
high=self.OHLCV_df[d - 1:t]["High"],
low=self.OHLCV_df[d - 1:t]["Low"],
close=self.OHLCV_df[d - 1:t]["Adj Close"],
volume=self.OHLCV_df[d - 1:t]['Volume'])
feature2 = volatility.average_true_range(
high=self.OHLCV_df[d - 1:t]["High"],
low=self.OHLCV_df[d - 1:t]["Low"],
close=self.OHLCV_df[d - 1:t]["Adj Close"])
feature3 = trend.macd(close=self.OHLCV_df[d - 1:t]["Adj Close"],
n_slow=self.window_size,
n_fast=self.window_size // 2,
fillna=True)
feature4 = momentum.rsi(close=self.OHLCV_df[d - 1:t]["Adj Close"])
feature5 = others.daily_return(
close=self.OHLCV_df[d - 1:t]["Adj Close"])
state = pd.concat(
[feature1, feature2, feature3, feature4, feature5],
axis=1).values[-5:, :]
# block = self.timeseries[d-1:t]
# state = np.diff(np.log(block))
state = state.flatten()
state = np.append(state, (self.inventory, self.cash))
return state
def ATR(df0):
df = df0
df['atr'] = average_true_range(df['high'],
df['low'],
df['low'],
14,
fillna=False)
return df
def test_atr(self):
target = 'ATR'
result = average_true_range(high=self._df['High'],
low=self._df['Low'],
close=self._df['Close'],
n=14,
fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def calculate_Volitality_Indicators():
JSON_sent = request.get_json()
df = pd.DataFrame(JSON_sent[0])
_, atr, bbsma, bbupper, bblower, keltnerC = JSON_sent
# Average True Range
if atr['displayATR']:
indicator_ATR = average_true_range(high=df['high'],
low=df['low'],
close=df['close'],
n=atr['nForATR'])
df['atr'] = indicator_ATR
# # Bollinger Band SMA
if bbsma['displayBBSMA']:
indicator_BBSMA = bollinger_mavg(close=df['close'],
n=bbsma['nForBBSMA'])
df['bbsma'] = indicator_BBSMA
# # Bollinger Band Upper
if bbupper['displayBBUpper']:
indicator_BBUpper = bollinger_hband(close=df['close'],
n=bbupper['nForBBUpper'],
ndev=bbupper['ndevBBUpper'])
df['BBupper'] = indicator_BBUpper
# # Bollinger Band Lower
if bblower['displayBBLower']:
indicator_BBLower = bollinger_lband(close=df['close'],
n=bblower['nForBBLower'],
ndev=bblower['ndevBBLower'])
df['BBlower'] = indicator_BBLower
# # Keltner Channel Central
if keltnerC['displayKeltnerC']:
indicator_keltnerC = keltner_channel_mband(high=df['high'],
low=df['low'],
close=df['close'],
n=keltnerC['nForKeltnerC'])
df['keltnerC'] = indicator_keltnerC
df.fillna(0, inplace=True)
return (json.dumps(df.to_dict('records')))
def _calculate_keltner_bands(self, data: pd.DataFrame, period: int,
multiplier) -> tuple:
ema = self._calculate_ema(data, period)
data = data.swaplevel(0, 1, 1)
atr = (data.groupby(
level=0,
axis=1).apply(lambda stock_data: volatility.average_true_range(
stock_data.xs("High", level=1, axis=1).squeeze(),
stock_data.xs("Low", level=1, axis=1).squeeze(),
stock_data.xs("Adj Close", level=1, axis=1).squeeze(),
n=period,
)).iloc[-1])
upper_band = ema + multiplier * atr
lower_band = ema - multiplier * atr
return upper_band, lower_band
def test_atr2(self):
target = 'ATR'
result = average_true_range(**self._params)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def RiskRange(Price_Data,
window=10,
length=63,
volume_weighted=True,
vol_window=10,
mindiff=100000000.0,
maxdiff=-100000000.0):
"""
Function to Calculate Risk Ranges
"""
Windowminus1 = window - 1
Close = (Price_Data[['Close']]).dropna()
High = (Price_Data[['High']]).dropna()
Low = (Price_Data[['Low']]).dropna()
Volume = (Price_Data[['Volume']]).dropna()
Slope = (Close.apply(func=lambda x: x - (x.shift(Windowminus1))) /
(Windowminus1)).dropna()
for i in range(0, Windowminus1):
Min = np.minimum(
mindiff,
Close.apply(func=lambda x: x.shift(Windowminus1 - i) -
(x.shift(Windowminus1) + (Slope['Close'] * i)),
axis=0))
for i in range(0, Windowminus1):
Max = np.maximum(
maxdiff,
Close.apply(func=lambda x: x.shift(Windowminus1 - i) -
(x.shift(Windowminus1) + (Slope['Close'] * i)),
axis=0))
BridgeBottom = (Close + Min).dropna()
BridgeTop = (Close + Max).dropna()
ATR = average_true_range(High['High'],
Low['Low'],
Close['Close'],
window=window).dropna()
Hurst = ((np.log(
pd.DataFrame.rolling(High['High'], window=window).max() -
pd.DataFrame.rolling(Low['Low'], window=window).min()) - np.log(ATR)) /
(np.log(window)))
Hurst_New = pd.DataFrame(Hurst.values,
index=Hurst.index).rename(columns={0: 'Hurst'})
SD = (Close['Close']).rolling(vol_window).std()
if volume_weighted == True:
WMA = vwma(Close['Close'], Volume['Volume'], length=vol_window)
elif volume_weighted == False:
WMA = wma(Close['Close'], length=vol_window)
BBBottom = (WMA - (SD * 2)).dropna()
BBTop = (WMA + (SD * 2)).dropna()
Final_Bottom = BBBottom + ((BridgeBottom['Close'] - BBBottom) *
np.abs((Hurst_New['Hurst'] * 2) - 1))
Final_Top = BBTop + (
(BridgeTop['Close'] - BBTop) * np.abs((Hurst_New['Hurst'] * 2) - 1))
Final_Mid = Final_Bottom + ((Final_Top - Final_Bottom) / 2)
Trend = ((pd.DataFrame.rolling(Low['Low'], window=length).min()) +
((pd.DataFrame.rolling(High['High'], window=length).max() -
pd.DataFrame.rolling(Low['Low'], window=length).min()) / 2))
Output = pd.DataFrame(Final_Bottom).rename(columns={0: 'Bottom RR'})
Output['Top RR'] = Final_Top
Output['Mid RR'] = Final_Mid
Output['Trend'] = Trend
Output['Price'] = Close
Output = Output.dropna()
return Output
#jstring1 = '2018-09-27'
for entry in js:
if entry == jstring:
i = js[jstring].keys()
for jkeys in i:
if jkeys == '2018-09-27':
return((jkeys, symbol,
js[jstring][jkeys]['1. open'],
js[jstring][jkeys]['2. high'],
js[jstring][jkeys]['3. low'],
js[jstring][jkeys]['4. close'],
js[jstring][jkeys]['5. volume']))
return 'api limit'
for items in symbols:
stock_values.append((get_daily_data(items)))
stock_df = pd.DataFrame(stock_values)
stock_df = stock_df[[1,3,4,5]][0:4]
vol.average_true_range(stock_df[3].astype(float), stock_df[4].astype(float), stock_df[5].astype(float), n=1, fillna=False)
class TimeSeries(av):
def get_batch_stock_quotes(self, symbols):
_FUNCTION_KEY = "BATCH_STOCK_QUOTES"
return _FUNCTION_KEY, 'Stock Quotes', 'Meta Data'
ts.get_batch_stock_quotes(symbols)