Bollinger Bands and Width in python and plotly смотреть онлайн

In this micro video you will learn: Bollinger Bands and Width in python and plotly
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Code Snippet: ?

import yfinance as yf
import plotly.graph_objs as go
from plotly.subplots import make_subplots

# Download historical data for ^GDAXI
data = yf.download("^GDAXI", start="2021-01-01", end="2023-04-11")

sma_period=20

# Define Bollinger Bands
def get_bollinger_bands(close, window_size=sma_period, num_std=2):
rolling_mean = close.rolling(window=window_size).mean()
rolling_std = close.rolling(window=window_size).std()
upper_band = rolling_mean + (rolling_std * num_std)
lower_band = rolling_mean - (rolling_std * num_std)
return rolling_mean, upper_band, lower_band

# Calculate Bollinger Bands for close price
close = data["Close"]
rolling_mean, upper_band, lower_band = get_bollinger_bands(close)

# Calculate Bollinger Bands Width
band_width = (upper_band - lower_band) / rolling_mean

# Calculate SMA of Bollinger Bands Width
sma_band_width = band_width.rolling(window=sma_period).mean()

# Create main plot with close price and Bollinger Bands
fig = make_subplots(rows=2, cols=1, shared_xaxes=True)

fig.add_trace(go.Scatter(x=data.index, y=data["Close"], name="Close"), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=upper_band, name="Upper Band"), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=rolling_mean, name="Rolling Mean"), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=lower_band, name="Lower Band"), row=1, col=1)

# Create sub plot with Bollinger Band Width and SMA of Band Width
fig.add_trace(go.Bar(x=data.index, y=band_width, name="Band Width"), row=2, col=1)
fig.add_trace(go.Scatter(x=data.index, y=sma_band_width, name="SMA of Band Width"), row=2, col=1)

# Set x-axis and y-axis titles
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price", row=1, col=1)
fig.update_yaxes(title_text="Width", row=2, col=1)

# Set main plot title
fig.update_layout(title_text="Bollinger Bands for GDAXI")

# Show plot
fig.show()



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