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import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import yfinance as yf
from plotly.subplots import make_subplots
def get_stock_price(stockticker: str) -> str:
ticker = yf.Ticker(stockticker)
todays_data = ticker.history(period='1d')
return str(round(todays_data['Close'][0], 2))
def plot_candlestick_stock_price(historical_data):
"""Useful for plotting candlestick plot for stock prices.
Use historical stock price data from yahoo finance for the week and plot them."""
df=historical_data[['Close','Open','High','Low']]
df.index=pd.to_datetime(df.index)
df.index.names=['Date']
df=df.reset_index()
fig = go.Figure(data=[go.Candlestick(x=df['Date'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'])])
fig.show()
def historical_stock_prices(stockticker, days_ago):
"""Upload accurate data to accurate dates from yahoo finance."""
ticker = yf.Ticker(stockticker)
end_date = datetime.now()
start_date = end_date - timedelta(days=days_ago)
start_date = start_date.strftime('%Y-%m-%d')
end_date = end_date.strftime('%Y-%m-%d')
historical_data = ticker.history(start=start_date, end=end_date)
return historical_data
def plot_macd2(df):
try:
# Debugging: Print the dataframe columns and a few rows
print("DataFrame columns:", df.columns)
print("DataFrame head:\n", df.head())
# Convert DataFrame index and columns to numpy arrays
index = df.index.to_numpy()
close_prices = df['Close'].to_numpy()
macd = df['MACD'].to_numpy()
signal_line = df['Signal_Line'].to_numpy()
macd_histogram = df['MACD_Histogram'].to_numpy()
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})
# Subplot 1: Candlestick chart
ax1.plot(index, close_prices, label='Close', color='black')
ax1.set_title("Candlestick Chart")
ax1.set_ylabel("Price")
ax1.legend()
# Subplot 2: MACD
ax2.plot(index, macd, label='MACD', color='blue')
ax2.plot(index, signal_line, label='Signal Line', color='red')
histogram_colors = np.where(macd_histogram >= 0, 'green', 'red')
ax2.bar(index, macd_histogram, color=histogram_colors, alpha=0.6)
ax2.set_title("MACD")
ax2.set_ylabel("MACD Value")
ax2.legend()
plt.xlabel("Date")
plt.tight_layout()
return fig
except Exception as e:
print(f"Error in plot_macd: {e}")
return None
def plot_macd(df):
# Create Figure
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.2, 0.1],
vertical_spacing=0.15, # Adjust vertical spacing between subplots
subplot_titles=("Candlestick Chart", "MACD")) # Add subplot titles
# Subplot 1: Plot candlestick chart
fig.add_trace(go.Candlestick(
x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
increasing_line_color='#00cc96', # Green for increasing
decreasing_line_color='#ff3e3e', # Red for decreasing
showlegend=False
), row=1, col=1) # Specify row and column indices
# Subplot 2: Plot MACD
fig.add_trace(
go.Scatter(
x=df.index,
y=df['MACD'],
mode='lines',
name='MACD',
line=dict(color='blue')
),
row=2, col=1
)
fig.add_trace(
go.Scatter(
x=df.index,
y=df['Signal_Line'],
mode='lines',
name='Signal Line',
line=dict(color='red')
),
row=2, col=1
)
# Plot MACD Histogram with different colors for positive and negative values
histogram_colors = ['green' if val >= 0 else 'red' for val in df['MACD_Histogram']]
fig.add_trace(
go.Bar(
x=df.index,
y=df['MACD_Histogram'],
name='MACD Histogram',
marker_color=histogram_colors
),
row=2, col=1
)
# Update layout with zoom and pan tools enabled
layout = go.Layout(
title='MSFT Candlestick Chart and MACD Subplots',
title_font=dict(size=12), # Adjust title font size
plot_bgcolor='#f2f2f2', # Light gray background
height=600,
width=1200,
xaxis_rangeslider=dict(visible=True, thickness=0.03),
)
# Update the layout of the entire figure
fig.update_layout(layout)
fig.update_yaxes(fixedrange=False, row=1, col=1)
fig.update_yaxes(fixedrange=True, row=2, col=1)
fig.update_xaxes(type='category', row=1, col=1)
fig.update_xaxes(type='category', nticks=10, row=2, col=1)
fig.show()
#return fig
def calculate_MACD(df, fast_period=12, slow_period=26, signal_period=9):
"""
Calculates the MACD (Moving Average Convergence Divergence) and related indicators.
Parameters:
df (DataFrame): A pandas DataFrame containing at least a 'Close' column with closing prices.
fast_period (int): The period for the fast EMA (default is 12).
slow_period (int): The period for the slow EMA (default is 26).
signal_period (int): The period for the signal line EMA (default is 9).
Returns:
DataFrame: A pandas DataFrame with the original data and added columns for MACD, Signal Line, and MACD Histogram.
"""
df['EMA_fast'] = df['Close'].ewm(span=fast_period, adjust=False).mean()
df['EMA_slow'] = df['Close'].ewm(span=slow_period, adjust=False).mean()
df['MACD'] = df['EMA_fast'] - df['EMA_slow']
df['Signal_Line'] = df['MACD'].ewm(span=signal_period, adjust=False).mean()
df['MACD_Histogram'] = df['MACD'] - df['Signal_Line']
return df |