Psychological Line (PSY)
References
Definition
Psychological line (PSY), as an indicator, is the ratio of the number of rising periods over the total number of periods. It reflects the buying power in relation to the selling power.
If PSY is above 50%, it indicates that buyers are in control. Likewise, if it is below 50%, it indicates the sellers are in control. If the PSY moves along the 50% area, it indicates balance between the buyers and sellers and therefore there is no direction movement for the market.
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', 'GKOS']
#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
2022-08-27 19:18:31.874246 ^GSPC (5701, 7) 1999-12-31 00:00:00 2022-08-26 00:00:00
2022-08-27 19:18:32.234071 GSK (5701, 7) 1999-12-31 00:00:00 2022-08-26 00:00:00
2022-08-27 19:18:32.579770 NVO (5701, 7) 1999-12-31 00:00:00 2022-08-26 00:00:00
2022-08-27 19:18:32.733716 GKOS (1807, 7) 2015-06-25 00:00:00 2022-08-26 00:00:00
ticker = 'GKOS'
dfs[ticker].tail(5)
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2022-08-22 | 49.290001 | 50.110001 | 48.810001 | 49.049999 | 243200 | 0 | 0 |
| 2022-08-23 | 49.509998 | 49.759998 | 48.439999 | 49.410000 | 260300 | 0 | 0 |
| 2022-08-24 | 50.290001 | 52.680000 | 49.269001 | 52.000000 | 628200 | 0 | 0 |
| 2022-08-25 | 52.820000 | 52.840000 | 52.040001 | 52.590000 | 349400 | 0 | 0 |
| 2022-08-26 | 52.310001 | 52.580002 | 49.480000 | 49.889999 | 550400 | 0 | 0 |
Define PSY calculation function
def cal_psy(ohlc: pd.DataFrame, period: int = 12, ma_period: int = 6, column: str = "close") -> pd.DataFrame:
"""
PSY
reference:
- https://github.com/mpquant/MyTT/blob/ea4f14857ecc46a3739a75ce2e6974b9057a6102/MyTT.py#L71
def PSY(CLOSE,N=12, M=6):
PSY=COUNT(CLOSE>REF(CLOSE,1),N)/N*100
PSYMA=MA(PSY,M)
return RD(PSY),RD(PSYMA)
"""
psy = (ohlc[column]>ohlc[column].shift(1)).rolling(period).sum()/period*100
psy_ma = psy.rolling(ma_period).mean()
return pd.DataFrame(data={'PSY': psy.values, 'PSY_MA': psy_ma.values, }, index=ohlc.index)
Calculate PSY
df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]
df = df.round(2)
cal_psy
<function __main__.cal_psy(ohlc: pandas.core.frame.DataFrame, period: int = 12, ma_period: int = 6, column: str = 'close') -> pandas.core.frame.DataFrame>
df_ta = cal_psy(df, period = 20, ma_period = 9, column = "Close")
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )
del df_ta
gc.collect()
80
display(df.head(5))
display(df.tail(5))
| Open | High | Low | Close | Volume | PSY | PSY_MA | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2015-06-25 | 29.11 | 31.95 | 28.00 | 31.22 | 7554700 | NaN | NaN |
| 2015-06-26 | 30.39 | 30.39 | 27.51 | 28.00 | 1116500 | NaN | NaN |
| 2015-06-29 | 27.70 | 28.48 | 27.51 | 28.00 | 386900 | NaN | NaN |
| 2015-06-30 | 27.39 | 29.89 | 27.39 | 28.98 | 223900 | NaN | NaN |
| 2015-07-01 | 28.83 | 29.00 | 27.87 | 28.00 | 150000 | NaN | NaN |
| Open | High | Low | Close | Volume | PSY | PSY_MA | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2022-08-22 | 49.29 | 50.11 | 48.81 | 49.05 | 243200 | 55.0 | 58.333333 |
| 2022-08-23 | 49.51 | 49.76 | 48.44 | 49.41 | 260300 | 60.0 | 58.333333 |
| 2022-08-24 | 50.29 | 52.68 | 49.27 | 52.00 | 628200 | 60.0 | 58.888889 |
| 2022-08-25 | 52.82 | 52.84 | 52.04 | 52.59 | 349400 | 60.0 | 59.444444 |
| 2022-08-26 | 52.31 | 52.58 | 49.48 | 49.89 | 550400 | 55.0 | 58.888889 |
df[['PSY', 'PSY_MA']].hist(bins=50)
array([[<AxesSubplot:title={'center':'PSY'}>,
<AxesSubplot:title={'center':'PSY_MA'}>]], 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",
#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, 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=(4,2,1), tight_layout=True, style=style, returnfig=True)
if names is None:
names = {'main_title': '', 'sub_tile': ''}
added_plots = {
'PSY': mpf.make_addplot(mid_panel['PSY'], panel=1, color='dodgerblue', width=1, secondary_y=False),
'PSY_MA': mpf.make_addplot(mid_panel['PSY_MA'], panel=1, color='orange', width=1, secondary_y=False),
}
fb_psy_up = dict(y1=50*np.ones(mid_panel.shape[0]),y2=mid_panel['PSY'].values,where=mid_panel['PSY'].values<50,color="#93c47d",alpha=0.6,interpolate=True)
fb_psy_dn = dict(y1=50*np.ones(mid_panel.shape[0]),y2=mid_panel['PSY'].values,where=mid_panel['PSY'].values>50,color="#e06666",alpha=0.6,interpolate=True)
fb_psy_up['panel'] = 1
fb_psy_dn['panel'] = 1
fb_bbands= [fb_psy_up, fb_psy_dn]
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=0.135)
axes[0].set_title(names['sub_tile'], fontsize=10, style='italic', loc='left')
axes[2].set_ylabel('PSY')
axes[2].legend([None]*3)
handles = axes[2].get_legend().legendHandles
axes[2].legend(handles=handles,labels=list(added_plots.keys()))
return fig, axes
start = -200
end = -100#df.shape[0]
names = {'main_title': f'{ticker}',
'sub_tile': 'PSY: buy when PSY line cross to below 50; sell whey PSY line cross to above 50'}
aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']],
df.iloc[start:end][['PSY', 'PSY_MA']],
chart_type='hollow_and_filled',
names = names,
)
