mcclellan_oscillator.py

# Import dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
import datetime as dt
yf.pdr_override()

# input
symbol = "AAPL"
start = dt.date.today() - dt.timedelta(days=180)
end = dt.date.today()

# Read data
dfs = yf.download(symbol, start, end)

import talib as ta

change = dfs["Adj Close"].diff()
Advances = change[change > 0]
Declines = change[change <= 0]

# df[['Advances', 'Declines']] = df[['Advances', 'Declines']].fillna(0)
# df['ADL'] = df['Advances'].fillna(df['Declines'])
# ADL for stocks
dfs["ADL_Stock"] = Advances.combine_first(Declines)

import quandl as q

Advances = q.get("URC/NYSE_ADV", start_date="2018-01-01")["Numbers of Stocks"]
Declines = q.get("URC/NYSE_DEC", start_date="2018-01-01")["Numbers of Stocks"]

df = pd.DataFrame()
df["Advances"] = Advances
df["Declines"] = Declines
df.head()

# Ratio Adjusted Net Advances (RANA): (Advances - Declines)/(Advances + Declines)
# RANA = (advances - declines) / (advances + declines)
# df['Net_Advances'] = df['Advances'] - df['Declines']
# df['Ratio_Adjusted'] = (df['Net_Advances']/(df['Advances'] + df['Declines']))*1000
df["Net_Advances"] = df["Advances"] - df["Declines"]
df["Ratio_Adjusted"] = (df["Net_Advances"] / (df["Advances"] + df["Declines"])) * 1000
df["19_EMA"] = ta.EMA(df["Ratio_Adjusted"], timeperiod=19)
df["39_EMA"] = ta.EMA(df["Ratio_Adjusted"], timeperiod=39)
df["RANA"] = (
    (df["Advances"] - df["Declines"]) / (df["Advances"] + df["Declines"]) * 1000
)

plt.figure(figsize=(12, 6))
plt.plot(dfs.index, dfs["Adj Close"])
plt.axhline(y=dfs["Adj Close"].mean(), color="r")
plt.title("Stock Close Price")
plt.grid()
plt.ylabel("Price")
plt.show()

# ## Comparing Stock and McClellan Oscillator
# Line Chart
# See if the stock correlate with Market Indicator
fig = plt.figure(figsize=(14, 7))
ax1 = plt.subplot(2, 1, 1)
ax1.plot(dfs.index, dfs["Adj Close"])
ax1.axhline(y=dfs["Adj Close"].mean(), color="r")
ax1.grid()
ax1.set_ylabel("Price")

df["Positive"] = df["RANA"] > 0
ax2 = plt.subplot(2, 1, 2)
ax2.bar(df.index, df["RANA"], color=df.Positive.map({True: "g", False: "r"}))
ax2.grid()
ax2.set_ylabel("Ratio Adjusted Net Advances")
ax2.set_xlabel("Date")
plt.show()

# ## NYSE Advance and Declines
fig = plt.figure(figsize=(14, 7))
df["Positive"] = df["RANA"] > 0
ax = plt.subplot(2, 1, 1)
ax.bar(df.index, df["RANA"], color=df.Positive.map({True: "g", False: "r"}))
ax.grid()
ax.set_ylabel("Ratio Adjusted Net Advances")
ax.set_xlabel("Date")

ax2 = plt.subplot(2, 1, 2)
ax2.plot(df.index, df["19_EMA"], color="b", label="19-day EMA")
ax2.plot(df.index, df["39_EMA"], color="r", label="39-day EMA")
ax2.grid()
ax2.set_ylabel("Ratio Adjusted Net Advances")
ax2.legend(loc="best")
ax2.set_xlabel("Date")
plt.show()