Вот пример вывода терминал, мой Plotter.py, а затем мой код. Код находится под выводом
Код: Выделить всё
Support Slope: 0.0017904146337511123, Support Intercept: 6.270467036397379
Resistance Slope: 0.0015433163283149968, Resistance Intercept: 6.287617104916379
Support Slope: 0.0018135332025410028, Support Intercept: 6.270549335972292
Resistance Slope: 0.0015762954337882838, Resistance Intercept: 6.287418760297718
Support Levels: [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, np.float64(6.144023780
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Trendlines calculated successfully.
An error occurred: 'support_slope'
Код: Выделить всё
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
def fit_trendlines_single(data: np.array):
"""Fit a linear regression to the given data and return the slope and intercept."""
x = np.arange(len(data)).reshape(-1, 1) # Create an array of indices
model = LinearRegression().fit(x, data)
return model.coef_[0], model.intercept_ # Return slope and intercept
def fit_trendlines_high_low(high: np.array, low: np.array):
"""Fit trendlines to high and low prices."""
support_slope, support_intercept = fit_trendlines_single(low)
resist_slope, resist_intercept = fit_trendlines_single(high)
# Debugging statements
print(f"Support Slope: {support_slope}, Support Intercept: {support_intercept}")
print(f"Resistance Slope: {resist_slope}, Resistance Intercept: {resist_intercept}")
return (support_slope, support_intercept), (resist_slope, resist_intercept)
def calculate_trendlines(data: pd.DataFrame):
"""Calculate support and resistance trendlines from the given DataFrame."""
required_columns = ['high', 'low', 'close']
for col in required_columns:
if col not in data.columns:
raise KeyError(f"Column '{col}' is missing from the data.")
# Take natural log of data
data['close'] = np.log(data['close'])
data['high'] = np.log(data['high'].replace(0, np.nan))
data['low'] = np.log(data['low'].replace(0, np.nan))
lookback = 30
support_levels = [np.nan] * len(data)
resist_levels = [np.nan] * len(data)
for i in range(lookback - 1, len(data)):
candles = data.iloc[i - lookback + 1: i + 1]
try:
# Fit the trendlines and get slopes and intercepts
(support_slope, support_intercept), (resist_slope, resist_intercept) = fit_trendlines_high_low(
candles['high'].values,
candles['low'].values
)
except Exception as e:
print(f"Error fitting trendlines for index {i}: {e}")
continue # Skip to the next iteration if there's an error
# Calculate support and resistance levels
support_intercept = candles['low'].iloc[-1] - support_slope * (lookback - 1)
resist_intercept = candles['high'].iloc[-1] - resist_slope * (lookback - 1)
support_levels[i] = support_slope * (len(data) - 1 - i) + support_intercept
resist_levels[i] = resist_slope * (len(data) - 1 - i) + resist_intercept
# Convert back to price level and handle any potential issues
data['support'] = np.exp(support_levels) # Convert back to price level
data['resistance'] = np.exp(resist_levels) # Convert back to price level
# Debugging output for support and resistance levels
print("Support Levels:", support_levels)
print("Resistance Levels:", resist_levels)
return data[['support', 'resistance']]
Код: Выделить всё
import pandas as pd
import mplfinance as mpf
from trendline import calculate_trendlines
from directional_change import directional_change
def plot_stock_data(df: pd.DataFrame):
print("Starting to calculate trendlines...")
trendlines = calculate_trendlines(df)
print("Trendlines calculated successfully.")
# Access support and resistance lines
support_line = trendlines['support_slope'] # Adjusted to match returned DataFrame
resist_line = trendlines['resist_slope'] # Adjusted to match returned DataFrame
# Add support and resistance lines to the main DataFrame
df['support'] = support_line
df['resistance'] = resist_line
# Create addplots for support and resistance lines
apdict = mpf.make_addplot(df[['support', 'resistance']], panel=0, type='line', color=['g', 'r'])
# Plot the stock data as a candlestick chart with support and resistance lines
mpf.plot(df, type='candle', addplot=apdict,
title='Stock Price with Support and Resistance Levels',
ylabel='Price',
volume=False,
style='yahoo')
# Example of loading data and plotting
if __name__ == "__main__":
try:
# Load your CSV file into a DataFrame
df = pd.read_csv('bestdata.csv', parse_dates=['date'], index_col='date')
print("Data loaded successfully.")
# Ensure required columns are present
if not {'open', 'high', 'low', 'close'}.issubset(df.columns):
raise KeyError("The DataFrame must contain 'open', 'high', 'low', and 'close' columns.")
# Plot the stock data
plot_stock_data(df)
except Exception as e:
print(f"An error occurred: {e}")
Подробнее здесь: https://stackoverflow.com/questions/790 ... port-slope
