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fix: dtype causing future warning to pop up #293

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merged 1 commit into from Apr 24, 2022
Merged

fix: dtype causing future warning to pop up #293

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61 changes: 39 additions & 22 deletions ta/trend.py
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Expand Up @@ -36,7 +36,8 @@ def __init__(self, close: pd.Series, window: int = 25, fillna: bool = False):

def _run(self):
min_periods = 0 if self._fillna else self._window
rolling_close = self._close.rolling(self._window, min_periods=min_periods)
rolling_close = self._close.rolling(
self._window, min_periods=min_periods)
self._aroon_up = rolling_close.apply(
lambda x: float(np.argmax(x) + 1) / self._window * 100, raw=True
)
Expand Down Expand Up @@ -315,7 +316,8 @@ def _run(self):
ema1 = _ema(amplitude, self._window_fast, self._fillna)
ema2 = _ema(ema1, self._window_fast, self._fillna)
mass = ema1 / ema2
self._mass = mass.rolling(self._window_slow, min_periods=min_periods).sum()
self._mass = mass.rolling(
self._window_slow, min_periods=min_periods).sum()

def mass_index(self) -> pd.Series:
"""Mass Index (MI)
Expand Down Expand Up @@ -368,11 +370,13 @@ def _run(self):
min_periods_n2 = 0 if self._fillna else self._window2
self._conv = 0.5 * (
self._high.rolling(self._window1, min_periods=min_periods_n1).max()
+ self._low.rolling(self._window1, min_periods=min_periods_n1).min()
+ self._low.rolling(self._window1,
min_periods=min_periods_n1).min()
)
self._base = 0.5 * (
self._high.rolling(self._window2, min_periods=min_periods_n2).max()
+ self._low.rolling(self._window2, min_periods=min_periods_n2).min()
+ self._low.rolling(self._window2,
min_periods=min_periods_n2).min()
)

def ichimoku_conversion_line(self) -> pd.Series:
Expand Down Expand Up @@ -489,7 +493,8 @@ def _run(self):
(
(
self._close
- self._close.shift(self._r1, fill_value=self._close.mean())
- self._close.shift(self._r1,
fill_value=self._close.mean())
)
/ self._close.shift(self._r1, fill_value=self._close.mean())
)
Expand All @@ -500,7 +505,8 @@ def _run(self):
(
(
self._close
- self._close.shift(self._r2, fill_value=self._close.mean())
- self._close.shift(self._r2,
fill_value=self._close.mean())
)
/ self._close.shift(self._r2, fill_value=self._close.mean())
)
Expand All @@ -511,7 +517,8 @@ def _run(self):
(
(
self._close
- self._close.shift(self._r3, fill_value=self._close.mean())
- self._close.shift(self._r3,
fill_value=self._close.mean())
)
/ self._close.shift(self._r3, fill_value=self._close.mean())
)
Expand All @@ -522,7 +529,8 @@ def _run(self):
(
(
self._close
- self._close.shift(self._r4, fill_value=self._close.mean())
- self._close.shift(self._r4,
fill_value=self._close.mean())
)
/ self._close.shift(self._r4, fill_value=self._close.mean())
)
Expand Down Expand Up @@ -649,7 +657,8 @@ def _mad(x):
typical_price = (self._high + self._low + self._close) / 3.0
self._cci = (
typical_price
- typical_price.rolling(self._window, min_periods=min_periods).mean()
- typical_price.rolling(self._window,
min_periods=min_periods).mean()
) / (
self._constant
* typical_price.rolling(self._window, min_periods=min_periods).apply(
Expand Down Expand Up @@ -718,8 +727,10 @@ def _run(self):

self._trs_initial = np.zeros(self._window - 1)
self._trs = np.zeros(len(self._close) - (self._window - 1))
self._trs[0] = diff_directional_movement.dropna()[0 : self._window].sum()
diff_directional_movement = diff_directional_movement.reset_index(drop=True)
self._trs[0] = diff_directional_movement.dropna()[
0: self._window].sum()
diff_directional_movement = diff_directional_movement.reset_index(
drop=True)

for i in range(1, len(self._trs) - 1):
self._trs[i] = (
Expand All @@ -734,7 +745,7 @@ def _run(self):
neg = abs(((diff_down > diff_up) & (diff_down > 0)) * diff_down)

self._dip = np.zeros(len(self._close) - (self._window - 1))
self._dip[0] = pos.dropna()[0 : self._window].sum()
self._dip[0] = pos.dropna()[0: self._window].sum()

pos = pos.reset_index(drop=True)

Expand All @@ -746,7 +757,7 @@ def _run(self):
)

self._din = np.zeros(len(self._close) - (self._window - 1))
self._din[0] = neg.dropna()[0 : self._window].sum()
self._din[0] = neg.dropna()[0: self._window].sum()

neg = neg.reset_index(drop=True)

Expand Down Expand Up @@ -776,11 +787,12 @@ def adx(self) -> pd.Series:
directional_index = 100 * np.abs((dip - din) / (dip + din))

adx_series = np.zeros(len(self._trs))
adx_series[self._window] = directional_index[0 : self._window].mean()
adx_series[self._window] = directional_index[0: self._window].mean()

for i in range(self._window + 1, len(adx_series)):
adx_series[i] = (
(adx_series[i - 1] * (self._window - 1)) + directional_index[i - 1]
(adx_series[i - 1] * (self._window - 1)) +
directional_index[i - 1]
) / float(self._window)

adx_series = np.concatenate((self._trs_initial, adx_series), axis=0)
Expand Down Expand Up @@ -859,8 +871,10 @@ def _run(self):
trn = true_range.rolling(self._window, min_periods=min_periods).sum()
vmp = np.abs(self._high - self._low.shift(1))
vmm = np.abs(self._low - self._high.shift(1))
self._vip = vmp.rolling(self._window, min_periods=min_periods).sum() / trn
self._vin = vmm.rolling(self._window, min_periods=min_periods).sum() / trn
self._vip = vmp.rolling(
self._window, min_periods=min_periods).sum() / trn
self._vin = vmm.rolling(
self._window, min_periods=min_periods).sum() / trn

def vortex_indicator_pos(self):
"""+VI
Expand Down Expand Up @@ -935,8 +949,8 @@ def _run(self): # noqa
down_trend_low = self._low.iloc[0]

self._psar = self._close.copy()
self._psar_up = pd.Series(index=self._psar.index)
self._psar_down = pd.Series(index=self._psar.index)
self._psar_up = pd.Series(index=self._psar.index, dtype="float64")
self._psar_down = pd.Series(index=self._psar.index, dtype="float64")

for i in range(2, len(self._close)):
reversal = False
Expand All @@ -946,7 +960,8 @@ def _run(self): # noqa

if up_trend:
self._psar.iloc[i] = self._psar.iloc[i - 1] + (
acceleration_factor * (up_trend_high - self._psar.iloc[i - 1])
acceleration_factor *
(up_trend_high - self._psar.iloc[i - 1])
)

if min_low < self._psar.iloc[i]:
Expand All @@ -969,7 +984,8 @@ def _run(self): # noqa
self._psar.iloc[i] = low1
else:
self._psar.iloc[i] = self._psar.iloc[i - 1] - (
acceleration_factor * (self._psar.iloc[i - 1] - down_trend_low)
acceleration_factor *
(self._psar.iloc[i - 1] - down_trend_low)
)

if max_high > self._psar.iloc[i]:
Expand Down Expand Up @@ -1104,7 +1120,8 @@ def _run(self):

_stoch_d_min = _stoch_d.rolling(window=self._cycle).min()
_stoch_d_max = _stoch_d.rolling(window=self._cycle).max()
_stoch_kd = 100 * (_stoch_d - _stoch_d_min) / (_stoch_d_max - _stoch_d_min)
_stoch_kd = 100 * (_stoch_d - _stoch_d_min) / \
(_stoch_d_max - _stoch_d_min)
self._stc = _ema(_stoch_kd, self._smooth2, self._fillna)

def stc(self):
Expand Down