def price_acc_ema(self):
# Finally let's smooth out the acceleration by taking its ema:
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
asset_pct_ema_pct = asset_pct_ema.pct_change()
asset_pct_ema_pct_ema = series_ema(asset_pct_ema_pct, self.acc_ema_window)
return asset_pct_ema_pct_ema
def plot_(self):
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
asset_pct_ema_pct = asset_pct_ema.pct_change()
asset_pct_ema_pct_ema = series_ema(asset_pct_ema_pct,
self.acc_ema_window)
two_separate_graphs(asset_pct_ema_pct_ema, self.ticker_close)
def stats(self):
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
asset_pct_ema_pct = asset_pct_ema.pct_change()
asset_pct_ema_pct_ema = series_ema(asset_pct_ema_pct, self.acc_ema_window).dropna(0)
plt.hist(asset_pct_ema_pct_ema)
plt.title("Price EMA Acc Distribution")
plt.xlabel("Value")
plt.ylabel("Frequency")
print (asset_pct_ema_pct_ema.describe())
def current_level_acc(self):
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
asset_pct_ema_pct = asset_pct_ema.pct_change()
asset_pct_ema_pct_ema = series_ema(asset_pct_ema_pct,
self.acc_ema_window).dropna(0)
p_stats_20 = asset_pct_ema_pct_ema.rolling(
window=self.acc_ema_window).quantile(0.2).values[-1]
p_stats_40 = asset_pct_ema_pct_ema.rolling(
window=self.acc_ema_window).quantile(0.4).values[-1]
p_stats_60 = asset_pct_ema_pct_ema.rolling(
window=self.acc_ema_window).quantile(0.6).values[-1]
p_stats_80 = asset_pct_ema_pct_ema.rolling(
window=self.acc_ema_window).quantile(0.8).values[-1]
p_stats_100 = asset_pct_ema_pct_ema.rolling(
window=self.acc_ema_window).quantile(1.0).values[-1]
if p_stats_80 < asset_pct_ema_pct_ema.values[-1] <= p_stats_100:
current_level = '80-100. Decrease or Exit Position.'
elif p_stats_60 < asset_pct_ema_pct_ema.values[
-1] <= p_stats_80 and asset_pct_ema_pct_ema.values[
-1] > asset_pct_ema_pct_ema.values[-2]:
current_level = '60-80. Prep to Unload...'
elif p_stats_60 < asset_pct_ema_pct_ema.values[
-1] <= p_stats_80 and asset_pct_ema_pct_ema.values[
-1] < asset_pct_ema_pct_ema.values[-2]:
current_level = '60-80. Momentum Acc Reducing, Decrease or Exit Position.'
elif p_stats_20 < asset_pct_ema_pct_ema.values[
-1] <= p_stats_40 and asset_pct_ema_pct_ema.values[
-1] > asset_pct_ema_pct_ema.values[-2]:
current_level = '20-40. Up-ward Momentum Still strong'
elif asset_pct_ema_pct_ema.values[
-2] <= p_stats_20 and asset_pct_ema_pct_ema.values[
-1] > asset_pct_ema_pct_ema.values[-2]:
current_level = '0-20. Momentum Bouncing Back after Big Mkt Drop.'
elif asset_pct_ema_pct_ema.values[
-2] <= p_stats_20 and asset_pct_ema_pct_ema.values[
-1] < asset_pct_ema_pct_ema.values[-2]:
current_level = '0-20. Market Dropping, Panicking.'
else:
current_level = 'Normal Level'
# what's the current level?
# print ("p_stats_20:{}".format(p_stats_20))
# print ("-"*60)
# print ("p_stats_40:{}".format(p_stats_40))
# print ("-"*60)
# print ("p_stats_60:{}".format(p_stats_60))
# print ("-"*60)
# print ("p_stats_80:{}".format(p_stats_80))
# print ("-"*60)
# print ("p_stats_100:{}".format(p_stats_100))
# print ("-"*60)
print("Current Acc signal level by {}: Acc_Signal_Level: {:0.02f}; {}".
format(self.end, asset_pct_ema_pct_ema.values[-1],
current_level))
def rsi(series, window, upper_threshold, lower_shreshold):
window_length = window
delta = series.diff()
# Get rid of the first row, which is NaN since it did not have a previous
# row to calculate the differences
delta = delta[1:]
# Make the positive gains (up) and negative gains (down) Series
up, down = delta.copy(), delta.copy()
up[up < 0] = 0
down[down > 0] = 0
# Calculate the EWMA
roll_up1 = series_ema(up, window_length)
roll_down1 = series_ema(down.abs(), window_length)
# Calculate the RSI based on EWMA
RS1 = roll_up1 / roll_down1
RSI1 = 100.0 - (100.0 / (1.0 + RS1))
return RSI1
def price_acc(self):
# Second to last step, let's calculate velocity ema's momentum velocity, which means price moving average's acceleration:
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
asset_pct_ema_pct = asset_pct_ema.pct_change()
return asset_pct_ema_pct
def price_vlcty_ema(self):
asset_pct = self.ticker_close.pct_change()
asset_pct_ema = series_ema(asset_pct, self.velocity_ema_window)
return asset_pct_ema
def price_ema(self):
asset_ema = series_ema(self.ticker_close, self.close_ema_window)
return asset_ema