def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(high=cls._df['High'],
low=cls._df['Low'],
close=cls._df['Close'],
volume=cls._df['Volume'],
fillna=False)
cls._indicator = VolumeWeightedAveragePrice(**cls._params)
def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
"""Add volume technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Accumulation Distribution Index
df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()
# On Balance Volume
df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()
# Chaikin Money Flow
df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()
# Force Index
df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
close=df[close], volume=df[volume], n=13, fillna=fillna).force_index()
# Money Flow Indicator
df[f'{colprefix}volume_mfi'] = MFIIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).money_flow_index()
# Ease of Movement
indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()
# Volume Price Trend
df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()
# Negative Volume Index
df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()
# Volume Weighted Average Price
df[f'{colprefix}volume_vwap'] = VolumeWeightedAveragePrice(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna
).volume_weighted_average_price()
return df
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(
high=cls._df["High"],
low=cls._df["Low"],
close=cls._df["Close"],
volume=cls._df["Volume"],
fillna=False,
)
cls._indicator = VolumeWeightedAveragePrice(**cls._params)
def get_vwap(client, symbol):
df = get_bars(client, symbol)
raw_vwap = VolumeWeightedAveragePrice(high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'],
window=14)
# print("vwap:")
return raw_vwap.vwap[len(raw_vwap.vwap) - 1]
def start_test(count):
cancel_all('all')
client = build_client()
if count < 1:
count += 1
symbols = ['CRBP', 'QD', 'WPG', 'ANY']
for symbol in symbols:
ph = client.get_price_history(
symbol=symbol,
period_type=client.PriceHistory.PeriodType.DAY,
period=client.PriceHistory.Period.ONE_DAY,
frequency_type=client.PriceHistory.FrequencyType.MINUTE,
frequency=client.PriceHistory.Frequency.EVERY_FIFTEEN_MINUTES,
start_datetime=None,
end_datetime=None,
need_extended_hours_data=None).json()
print(ph['symbol'])
df = pd.json_normalize(ph, 'candles')
# pprint(len(df))
# pprint(df)
# pprint(df['close'])
# pprint(df['high'])
# pprint(df['low'])
# pprint(df['volume'])
raw_vwap = VolumeWeightedAveragePrice(high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'],
window=14)
raw_ema = EMAIndicator(close=df['close'], window=9)
print("vwap:")
pprint(raw_vwap.vwap[len(raw_vwap.vwap) - 1])
print("ema:")
pprint(raw_ema._close[len(raw_ema._close) - 1])
print("len ema:")
print(len(raw_ema._close))