ASI
References
Load basic packages
import pandas as pd
import numpy as np
import os
import gc
import copy
from pathlib import Path
from datetime import datetime, timedelta, time, date
#this package is to download equity price data from yahoo finance
#the source code of this package can be found here: https://github.com/ranaroussi/yfinance/blob/main
import yfinance as yf
pd.options.display.max_rows = 100
pd.options.display.max_columns = 100
import warnings
warnings.filterwarnings("ignore")
import pytorch_lightning as pl
random_seed=1234
pl.seed_everything(random_seed)
Global seed set to 1234
1234
#S&P 500 (^GSPC), Dow Jones Industrial Average (^DJI), NASDAQ Composite (^IXIC)
#Russell 2000 (^RUT), Crude Oil Nov 21 (CL=F), Gold Dec 21 (GC=F)
#Treasury Yield 10 Years (^TNX)
#benchmark_tickers = ['^GSPC', '^DJI', '^IXIC', '^RUT', 'CL=F', 'GC=F', '^TNX']
benchmark_tickers = ['^GSPC']
tickers = benchmark_tickers + ['GSK', 'NVO', 'AROC', 'RETA']
#https://github.com/ranaroussi/yfinance/blob/main/yfinance/base.py
# def history(self, period="1mo", interval="1d",
# start=None, end=None, prepost=False, actions=True,
# auto_adjust=True, back_adjust=False,
# proxy=None, rounding=False, tz=None, timeout=None, **kwargs):
dfs = {}
for ticker in tickers:
cur_data = yf.Ticker(ticker)
hist = cur_data.history(period="max", start='2000-01-01')
print(datetime.now(), ticker, hist.shape, hist.index.min(), hist.index.max())
dfs[ticker] = hist
2022-09-10 21:41:25.263501 ^GSPC (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:41:25.678381 GSK (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:41:26.077133 NVO (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:41:26.349350 AROC (3791, 7) 2007-08-21 00:00:00 2022-09-09 00:00:00
2022-09-10 21:41:26.597204 RETA (1584, 7) 2016-05-26 00:00:00 2022-09-09 00:00:00
ticker = 'GSK'
dfs[ticker].tail(5)
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2022-09-02 | 31.600000 | 31.969999 | 31.469999 | 31.850000 | 8152600 | 0.0 | 0.0 |
| 2022-09-06 | 31.650000 | 31.760000 | 31.370001 | 31.469999 | 5613900 | 0.0 | 0.0 |
| 2022-09-07 | 31.209999 | 31.590000 | 31.160000 | 31.490000 | 4822000 | 0.0 | 0.0 |
| 2022-09-08 | 30.910000 | 31.540001 | 30.830000 | 31.510000 | 6620900 | 0.0 | 0.0 |
| 2022-09-09 | 31.950001 | 31.969999 | 31.730000 | 31.889999 | 3556800 | 0.0 | 0.0 |
Define ASI calculation function
def ASI(OPEN,CLOSE,HIGH,LOW,M1=26,M2=10): #振动升降指标
LC=REF(CLOSE,1); AA=ABS(HIGH-LC); BB=ABS(LOW-LC);
CC=ABS(HIGH-REF(LOW,1)); DD=ABS(LC-REF(OPEN,1));
R=IF( (AA>BB) & (AA>CC),AA+BB/2+DD/4,IF( (BB>CC) & (BB>AA),BB+AA/2+DD/4,CC+DD/4));
X=(CLOSE-LC+(CLOSE-OPEN)/2+LC-REF(OPEN,1));
SI=16*X/R*MAX(AA,BB); ASI=SUM(SI,M1); ASIT=MA(ASI,M2);
return ASI,ASIT
#https://github.com/mpquant/MyTT/blob/ea4f14857ecc46a3739a75ce2e6974b9057a6102/MyTT.py
def cal_asi(
ohlc: pd.DataFrame, slow_period: int = 26, fast_period: int = 10
) -> pd.DataFrame:
ohlc = ohlc.copy(deep=True)
ohlc.columns = [c.lower() for c in ohlc.columns]
lc = ohlc['close'].shift(1).values
lo = ohlc['open'].shift(1).values
lh = ohlc['high'].shift(1).values
ll = ohlc['low'].shift(1).values
h = ohlc['high'].values
l = ohlc['low'].values
o = ohlc['open'].values
c = ohlc['close'].values
aa = np.abs(h - lc)
bb = np.abs(l - lc)
cc = np.abs(h - ll)
dd = np.abs(lc - lo)
r = np.where((aa>bb) & (aa>cc), aa+bb/2+dd/4, np.where((bb>cc) & (bb>aa), bb+aa/2+dd/4, cc+dd/4))
x=c-lc+(c-o)/2+lc-lo
si=16*x/r*np.maximum(aa,bb)
asi=pd.Series(si).rolling(slow_period).sum().values if slow_period>0 else pd.Series(si).cumsum().values
asit=pd.Series(asi).rolling(fast_period).mean().values
return pd.DataFrame(data={'ASI': asi, 'ASIT': asit}, index=ohlc.index)
Calculate ASI
df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]
df = df.round(2)
cal_asi
<function __main__.cal_asi(ohlc: pandas.core.frame.DataFrame, slow_period: int = 26, fast_period: int = 10) -> pandas.core.frame.DataFrame>
# df_ta = cal_do(df, slow_period = 20, fast_period = 7)
df_ta = cal_asi(df, slow_period = 26, fast_period = 10)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )
del df_ta
gc.collect()
122
display(df.head(5))
display(df.tail(5))
| Open | High | Low | Close | Volume | ASI | ASIT | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 1999-12-31 | 19.60 | 19.67 | 19.52 | 19.56 | 139400 | NaN | NaN |
| 2000-01-03 | 19.58 | 19.71 | 19.25 | 19.45 | 556100 | NaN | NaN |
| 2000-01-04 | 19.45 | 19.45 | 18.90 | 18.95 | 367200 | NaN | NaN |
| 2000-01-05 | 19.21 | 19.58 | 19.08 | 19.58 | 481700 | NaN | NaN |
| 2000-01-06 | 19.38 | 19.43 | 18.90 | 19.30 | 853800 | NaN | NaN |
| Open | High | Low | Close | Volume | ASI | ASIT | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2022-09-02 | 31.60 | 31.97 | 31.47 | 31.85 | 8152600 | -183.111913 | -147.826165 |
| 2022-09-06 | 31.65 | 31.76 | 31.37 | 31.47 | 5613900 | -175.636523 | -150.434876 |
| 2022-09-07 | 31.21 | 31.59 | 31.16 | 31.49 | 4822000 | -162.394891 | -152.071589 |
| 2022-09-08 | 30.91 | 31.54 | 30.83 | 31.51 | 6620900 | -141.448991 | -147.766271 |
| 2022-09-09 | 31.95 | 31.97 | 31.73 | 31.89 | 3556800 | -124.366614 | -147.613592 |
df[['ASI', 'ASIT']].hist(bins=50)
array([[<AxesSubplot:title={'center':'ASI'}>,
<AxesSubplot:title={'center':'ASIT'}>]], dtype=object)
#https://github.com/matplotlib/mplfinance
#this package help visualize financial data
import mplfinance as mpf
import matplotlib.colors as mcolors
# all_colors = list(mcolors.CSS4_COLORS.keys())#"CSS Colors"
# all_colors = list(mcolors.TABLEAU_COLORS.keys()) # "Tableau Palette",
# all_colors = list(mcolors.BASE_COLORS.keys()) #"Base Colors",
all_colors = ['dodgerblue', 'firebrick','limegreen','skyblue','lightgreen', 'navy','yellow','plum', 'yellowgreen']
#https://github.com/matplotlib/mplfinance/issues/181#issuecomment-667252575
#list of colors: https://matplotlib.org/stable/gallery/color/named_colors.html
#https://github.com/matplotlib/mplfinance/blob/master/examples/styles.ipynb
def make_3panels2(main_data, mid_panel=None, chart_type='candle', names=None, figratio=(14,9)):
style = mpf.make_mpf_style(base_mpf_style='yahoo', #charles
base_mpl_style = 'seaborn-whitegrid',
# marketcolors=mpf.make_marketcolors(up="r", down="#0000CC",inherit=True),
gridcolor="whitesmoke",
gridstyle="--", #or None, or - for solid
gridaxis="both",
edgecolor = 'whitesmoke',
facecolor = 'white', #background color within the graph edge
figcolor = 'white', #background color outside of the graph edge
y_on_right = False,
rc = {'legend.fontsize': 'small',#or number
#'figure.figsize': (14, 9),
'axes.labelsize': 'small',
'axes.titlesize':'small',
'xtick.labelsize':'small',#'x-small', 'small','medium','large'
'ytick.labelsize':'small'
},
)
if (chart_type is None) or (chart_type not in ['ohlc', 'line', 'candle', 'hollow_and_filled']):
chart_type = 'candle'
len_dict = {'candle':2, 'ohlc':3, 'line':1, 'hollow_and_filled':2}
kwargs = dict(type=chart_type, figratio=figratio, volume=True, volume_panel=2,
panel_ratios=(4,2, 2), tight_layout=True, style=style, returnfig=True)
if names is None:
names = {'main_title': '', 'sub_tile': ''}
added_plots = {
}
fb_bbands = []
if mid_panel is not None:
i = 0
for name_, data_ in mid_panel.iteritems():
added_plots[name_] = mpf.make_addplot(data_, panel=1, width=1, color=all_colors[i],secondary_y=False)
i = i + 1
fb_bbands2_ = dict(y1=-50*np.ones(mid_panel.shape[0]),
y2=50*np.ones(mid_panel.shape[0]),color="lightskyblue",alpha=0.1,interpolate=True)
fb_bbands2_['panel'] = 1
fb_bbands.append(fb_bbands2_)
fig, axes = mpf.plot(main_data, **kwargs,
addplot=list(added_plots.values()),
fill_between = fb_bbands,
)
# add a new suptitle
fig.suptitle(names['main_title'], y=1.05, fontsize=12, x=0.1285)
# axes[0].legend([None]*4)
# handles = axes[0].get_legend().legendHandles
# axes[0].legend(handles=handles[2:],labels=['RS_EMA', 'EMA'])
# axes[0].set_title(names['sub_tile'], fontsize=10, style='italic', loc='left')
# axes[0].set_ylabel(names['y_tiles'][0])
# axes[2].set_ylabel(names['y_tiles'][1])
return fig, axes
start = -100
end = df.shape[0]
names = {'main_title': f'{ticker}',
'sub_tile': 'ASI'}
aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']],
df.iloc[start:end][['ASI', 'ASIT']],
chart_type='hollow_and_filled',names = names)
