Finance/technical_indicators/GMMA.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 = "AAPL"
start = dt.date.today() - dt.timedelta(days=365 * 2)
end = dt.date.today()

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

# Short-term for EMA
df["EMA3"] = ta.EMA(df["Adj Close"], timeperiod=3)
df["EMA5"] = ta.EMA(df["Adj Close"], timeperiod=5)
df["EMA8"] = ta.EMA(df["Adj Close"], timeperiod=8)
df["EMA10"] = ta.EMA(df["Adj Close"], timeperiod=10)
df["EMA12"] = ta.EMA(df["Adj Close"], timeperiod=12)
df["EMA15"] = ta.EMA(df["Adj Close"], timeperiod=15)

# Long-term for EMA
df["EMA30"] = ta.EMA(df["Adj Close"], timeperiod=30)
df["EMA35"] = ta.EMA(df["Adj Close"], timeperiod=35)
df["EMA40"] = ta.EMA(df["Adj Close"], timeperiod=40)
df["EMA45"] = ta.EMA(df["Adj Close"], timeperiod=45)
df["EMA50"] = ta.EMA(df["Adj Close"], timeperiod=50)
df["EMA60"] = ta.EMA(df["Adj Close"], timeperiod=60)

EMA_Short = df[["EMA3", "EMA5", "EMA8", "EMA10", "EMA12", "EMA15"]]
EMA_Long = df[["EMA30", "EMA35", "EMA40", "EMA45", "EMA50", "EMA60"]]

# Short-term for SMA
df["SMA3"] = ta.SMA(df["Adj Close"], timeperiod=3)
df["SMA5"] = ta.SMA(df["Adj Close"], timeperiod=5)
df["SMA8"] = ta.SMA(df["Adj Close"], timeperiod=8)
df["SMA10"] = ta.SMA(df["Adj Close"], timeperiod=10)
df["SMA12"] = ta.SMA(df["Adj Close"], timeperiod=12)
df["SMA15"] = ta.SMA(df["Adj Close"], timeperiod=15)

# Long-term for SMA
df["SMA30"] = ta.SMA(df["Adj Close"], timeperiod=30)
df["SMA35"] = ta.SMA(df["Adj Close"], timeperiod=35)
df["SMA40"] = ta.SMA(df["Adj Close"], timeperiod=40)
df["SMA45"] = ta.SMA(df["Adj Close"], timeperiod=45)
df["SMA50"] = ta.SMA(df["Adj Close"], timeperiod=50)
df["SMA60"] = ta.SMA(df["Adj Close"], timeperiod=60)

SMA_Short = df[["SMA3", "SMA5", "SMA8", "SMA10", "SMA12", "SMA15"]]
SMA_Long = df[["SMA30", "SMA35", "SMA40", "SMA45", "SMA50", "SMA60"]]

plt.figure(figsize=(14, 7))
plt.plot(df["Adj Close"])
plt.plot(EMA_Short, color="blue")
plt.plot(EMA_Long, color="orange")
plt.title("Guppy Multiple Moving Averages of EMA")
plt.show()

plt.figure(figsize=(14, 7))
plt.plot(df["Adj Close"])
plt.plot(SMA_Short, color="blue")
plt.plot(SMA_Long, color="orange")
plt.title("Guppy Multiple Moving Averages of SMA")
plt.show()

# ## Candlestick with GMMA
from matplotlib import dates as mdates

dfc = df.copy()
dfc["VolumePositive"] = dfc["Open"] < dfc["Adj Close"]
# dfc = dfc.dropna()
dfc = dfc.reset_index()
dfc["Date"] = mdates.date2num(dfc["Date"].tolist())
from mplfinance.original_flavor import candlestick_ohlc

fig = plt.figure(figsize=(14, 7))
ax1 = plt.subplot(2, 1, 1)
candlestick_ohlc(ax1, dfc.values, width=0.5, colorup="g", colordown="r", alpha=1.0)
ax1.plot(SMA_Short, color="orange")
ax1.plot(SMA_Long, color="blue")
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%d-%m-%Y"))
ax1.grid(True, which="both")
ax1.minorticks_on()
ax1v = ax1.twinx()
colors = dfc.VolumePositive.map({True: "g", False: "r"})
ax1v.bar(dfc.Date, dfc["Volume"], color=colors, alpha=0.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.set_ylim(0, 3 * df.Volume.max())
ax1.set_title("Stock " + symbol + " Closing Price")
ax1.set_ylabel("Price")

ax2 = plt.subplot(2, 1, 2)
df["VolumePositive"] = df["Open"] < df["Adj Close"]
ax2.bar(
    df.index,
    df["Volume"],
    color=df.VolumePositive.map({True: "g", False: "r"}),
    label="macdhist",
)
ax2.grid()
ax2.set_ylabel("Volume")
ax2.set_xlabel("Date")
plt.show()

from mplfinance.original_flavor import candlestick_ohlc

fig = plt.figure(figsize=(14, 7))
ax1 = plt.subplot(2, 1, 1)
candlestick_ohlc(ax1, dfc.values, width=0.5, colorup="g", colordown="r", alpha=1.0)
ax1.plot(EMA_Short, color="orange")
ax1.plot(EMA_Long, color="blue")
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%d-%m-%Y"))
ax1.grid(True, which="both")
ax1.minorticks_on()
ax1v = ax1.twinx()
colors = dfc.VolumePositive.map({True: "g", False: "r"})
ax1v.bar(dfc.Date, dfc["Volume"], color=colors, alpha=0.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.set_ylim(0, 3 * df.Volume.max())
ax1.set_title("Stock " + symbol + " Closing Price")
ax1.set_ylabel("Price")

ax2 = plt.subplot(2, 1, 2)
df["VolumePositive"] = df["Open"] < df["Adj Close"]
ax2.bar(
    df.index,
    df["Volume"],
    color=df.VolumePositive.map({True: "g", False: "r"}),
    label="macdhist",
)
ax2.grid()
ax2.set_ylabel("Volume")
ax2.set_xlabel("Date")
plt.show()