Spaces:
Sleeping
Sleeping
File size: 3,130 Bytes
539f8db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
import pandas as pd
import mplfinance as mpf
from io import BytesIO
import base64
import matplotlib.pyplot as plt
def create_mplfinance_chart(df, ticker, predictions=None):
"""
Creates a custom mplfinance candlestick chart and returns it as a base64 encoded image.
Implements the exact layout: Candlestick + Volume + MACD + Stochastic.
"""
if df.empty:
return None
# Define style - Yahoo style as requested
mc = mpf.make_marketcolors(
up='#00ff00', down='#ff0000', # Green/Red candles
wick='black',
edge='black',
volume='#00bfff',
inherit=True
)
s = mpf.make_mpf_style(
base_mpf_style='yahoo',
marketcolors=mc,
facecolor='white',
edgecolor='black',
gridcolor='lightgray',
gridstyle='-',
figcolor='white',
rc={'axes.labelcolor': 'black',
'xtick.color': 'black',
'ytick.color': 'black',
'figure.titlesize': 16,
'axes.titlesize': 14,
'axes.titleweight': 'bold'}
)
# Define panels: [Candlestick, Volume, MACD, Stochastic]
apds = []
# MACD Panel (Panel 2 - index 1 for addplot)
# MACD Line
apds.append(
mpf.make_addplot(df['MACD'], color='#606060', panel=2, ylabel='MACD', secondary_y=False)
)
# Signal Line
apds.append(
mpf.make_addplot(df['MACD_signal'], color='#1f77b4', panel=2, secondary_y=False)
)
# Positive Histogram Bars
apds.append(
mpf.make_addplot(df['MACD_bar_positive'], type='bar', color='#4dc790', panel=2, width=0.8)
)
# Negative Histogram Bars
apds.append(
mpf.make_addplot(df['MACD_bar_negative'], type='bar', color='#fd6b6c', panel=2, width=0.8)
)
# Stochastic Panel (Panel 3 - index 2 for addplot)
apds.append(
mpf.make_addplot(df[['%D', '%SD', 'UL', 'DL']], panel=3, ylabel='Stoch (14,3)', ylim=[0, 100])
)
# Prediction overlay on main chart
if predictions is not None and predictions.any():
last_date = df.index[-1]
future_index = pd.date_range(start=last_date, periods=len(predictions) + 1, freq=df.index.freq or 'D')[1:]
future_series = pd.Series(predictions, index=future_index)
apds.append(
mpf.make_addplot(future_series, color='blue', linestyle='-.', width=2, marker='o', markersize=4)
)
# Plotting
fig, axes = mpf.plot(
df,
type='candle',
style=s,
title=f'{ticker} Price Chart and Analysis',
ylabel='Price (USD)',
volume=True,
addplot=apds,
mav=(5, 20), # Moving averages as requested
figratio=(16, 9),
figscale=1.5,
panel_ratios=(3, 1, 3, 3), # Ratio as requested
returnfig=True
)
# Convert to Base64
buf = BytesIO()
fig.savefig(buf, format='png', bbox_inches='tight', dpi=100)
plt.close(fig)
image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
return f'<img src="data:image/png;base64,{image_base64}" style="width: 100%; height: auto;">' |