Finance/technical_indicators/breadth_indicator.py at master · shashankvemuri/Finance · GitHub

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# 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()
import sys
import os
parent_dir = os.path.dirname(os.getcwd())
sys.path.append(parent_dir)
import ta_functions as ta

# input
symbol = "SPY"
start = dt.date.today() - dt.timedelta(days=365 * 4)
end = dt.date.today()

# Read data
df = yf.download(symbol, start, end)
df["Adj Close"][1:]

# ## On Balance Volume
OBV = ta.OBV(df["Adj Close"], df["Volume"])

import quandl as q

Advances = q.get("URC/NYSE_ADV", start_date="2017-07-27")["Numbers of Stocks"]
Declines = q.get("URC/NYSE_DEC", start_date="2017-07-27")["Numbers of Stocks"]

adv_vol = q.get("URC/NYSE_ADV_VOL", start_date="2017-07-27")["Numbers of Stocks"]
dec_vol = q.get("URC/NYSE_DEC_VOL", start_date="2017-07-27")["Numbers of Stocks"]

data = pd.DataFrame()
data["Advances"] = Advances
data["Declines"] = Declines
data["adv_vol"] = adv_vol
data["dec_vol"] = dec_vol

data["Net_Advances"] = data["Advances"] - data["Declines"]
data["Ratio_Adjusted"] = (
    data["Net_Advances"] / (data["Advances"] + data["Declines"])
) * 1000
data["19_EMA"] = ta.EMA(data["Ratio_Adjusted"], timeperiod=19)
data["39_EMA"] = ta.EMA(data["Ratio_Adjusted"], timeperiod=39)
data["RANA"] = (
    (data["Advances"] - data["Declines"]) / (data["Advances"] + data["Declines"]) * 1000
)

# Finding the TRIN Value
data["ad_ratio"] = data["Advances"].divide(data["Declines"])  # AD Ratio
data["ad_vol"] = data["adv_vol"].divide(data["dec_vol"])  # AD Volume Ratio
data["TRIN"] = data["ad_ratio"].divide(data["adv_vol"])  # TRIN Value

# ## Force Index

def ForceIndex(data, n):
    ForceIndex = pd.Series(df["Adj Close"].diff(n) * df["Volume"], name="ForceIndex")
    data = data.join(ForceIndex)
    return data

n = 10
ForceIndex = ForceIndex(data, n)
ForceIndex = ForceIndex["ForceIndex"]

fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(2, 1, 1)
ax.set_xticklabels([])
plt.plot(df["Adj Close"], lw=1)
plt.title("Market Price Chart")
plt.ylabel("Close Price")
plt.grid(True)
bx = fig.add_subplot(2, 1, 2)
plt.plot(ForceIndex, "k", lw=0.75, linestyle="-", label="Force Index")
plt.legend(loc=2, prop={"size": 9.5})
plt.ylabel("Force Index")
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.show()

# ## Chaikin Oscillator
def Chaikin(data):
    money_flow_volume = (
        (2 * df["Adj Close"] - df["High"] - df["Low"])
        / (df["High"] - df["Low"])
        * df["Volume"]
    )
    ad = money_flow_volume.cumsum()
    Chaikin = pd.Series(
        ad.ewm(com=(3 - 1) / 2).mean() - ad.ewm(com=(10 - 1) / 2).mean(), name="Chaikin"
    )
    data = data.join(Chaikin)
    return data

Chaikin(df)

Up = q.get("URC/NYSE_ADV", start_date="2017-07-27")["Numbers of Stocks"]
Down = q.get("URC/NYSE_DEC", start_date="2017-07-27")["Numbers of Stocks"]
Volume_Spread = Up - Down

Up = q.get("URC/NYSE_ADV", start_date="2017-07-27")["Numbers of Stocks"]
Down = q.get("URC/NYSE_DEC", start_date="2017-07-27")["Numbers of Stocks"]
Volume_Ratio = Up / Down

# ## Cumulative Volume Index
# CVI = Yesterday's CVI + (Advancing Volume - Declining Volume)
data["CVI"] = data["Net_Advances"][1:] + (data["Advances"] - data["Declines"])