[Python] Error plotting stock trendlines 'support_slope'

Ive been trying to plot trendlines after theyve been calculated. It seems like the calculations are fine and its giving me the slope and intercept for resistance/support, but at the end i get the error in the terminal 'support_slope'

Heres some sample output from the terminal,my plotter.py, and then my code. The code is below the output

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
900805), np.float64(6.139272016303774), np.float64(6.139236054845795), np.float64(6.119863757404568), np.float64(6.133514713049237), np.float64(6.133697068080789), np.float64(6.1200345
68688048), np.float64(6.110688954469717), np.float64(6.108510722295112), np.float64(6.137271723388179), np.float64(6.160351804707739), np.float64(6.1813442733810025), np.float64(6.1916
032959539224), np.float64(6.208925947305385), np.float64(6.232421713945308), np.float64(6.249004195128121), np.float64(6.2638613148808115), np.float64(6.288766828797837), np.float64(6.
309077362254943), np.float64(6.31954801808132), np.float64(6.3380756247241905), np.float64(6.349634383922647), np.float64(6.355377536278531), np.float64(6.355900011928189), np.float64(
6.364599208546319), np.float64(6.365795236301613), np.float64(6.360730467321387), np.float64(6.346940148758835), np.float64(6.3329739658960476), np.float64(6.332096599388408), np.float
64(6.328854356895254), np.float64(6.331209666839308), np.float64(6.336403886964111), np.float64(6.330559710886555), np.float64(6.327838400676507), np.float64(6.323902898043602), np.flo
at64(6.336588173577053), np.float64(6.330674481859525), np.float64(6.325814536246106), np.float64(6.326863165767089), np.float64(6.336544775507685), np.float64(6.3337464975689635), np.
float64(6.331847691075078), np.float64(6.329740546356697), np.float64(6.328298764355487), np.float64(6.328005740754699), np.float64(6.329680350609911), np.float64(6.325719089567122), n
p.float64(6.3243307446886705), np.float64(6.325448927831566), np.float64(6.334578329454965), np.float64(6.33896391203015), np.float64(6.344131278178628), np.float64(6.345356344597908),
np.float64(6.346462198490341), np.float64(6.343113888450451), np.float64(6.343424216863268), np.float64(6.345341423373968), np.float64(6.346972516801389), np.float64(6.334264118794359
), np.float64(6.320089688105729), np.float64(6.312857554024797), np.float64(6.316468722278171), np.float64(6.318770614006674), np.float64(6.293275167243099), np.float64(6.2868357551319
41), np.float64(6.293672057142271), np.float64(6.295650261238481), np.float64(6.288105008093921), np.float64(6.304956932950881), np.float64(6.290524120516261), np.float64(6.27196138260
8476), np.float64(6.240503578113983), np.float64(6.259191315422179), np.float64(6.264032931716887), np.float64(6.275802149857954), np.float64(6.29233973650218), np.float64(6.3006824029
oat64(6.334235373638061), np.float64(6.341579565934726), np.float64(6.340000587817826), np.float64(6.338312257141873), np.float64(6.333532479392294), np.float64(6.332473569280897), np.float64(6.344098126493882), np.float64(6.339507945235541), np.float64(6.333704021994592), np.float64(6.3428688570829435), np.float64(6.342334160177025), np.float64(6.344709729292713), np.float64(6.337728487338099), np.float64(6.338619707086098), np.float64(6.333662929035197), np.float64(6.333684162928985), np.float64(6.333202487126668), np.float64(6.33928656376116), np.float64(6.330249065524638), np.float64(6.334848796670068), np.float64(6.339420206710769), np.float64(6.344802237117225), np.float64(6.346227097003739), np.float64(6.347123646336369), np.float64(6.354121989515852), np.float64(6.353924990190995), np.float64(6.3543024437610685), np.float64(6.353970228525719), np.float64(6.352181076792252), np.float64(6.341243716481276), np.float64(6.336772414427273), np.float64(6.324483795335574), np.float64(6.324418583207052), np.float64(6.3251664107730265), np.float64(6.309703929270188), np.float64(6.3052894397965975), np.float64(6.303937420069597), np.float64(6.303213765410882), np.float64(6.303689961844675), np.float64(6.314676405983143), np.float64(6.31701275659393), np.float64(6.285420637158929), np.float64(6.263110757880048), np.float64(6.27780083067335), np.float64(6.284769799572417), np.float64(6.28812757507824), np.float64(6.298014617933884), np.float64(6.303804648765466), np.float64(6.319345221944853), np.float64(6.326887545481974), np.float64(6.342850838796915), np.float64(6.345421387394117), np.float64(6.351798715882801), np.float64(6.3513087475600045), np.float64(6.349152466841291), np.float64(6.345642719249067), np.float64(6.339129510642452), np.float64(6.33303749352764), np.float64(6.321874547055881), np.float64(6.314012034357801), np.float64(6.309673336064435), np.float64(6.303752394332343), np.float64(6.291300389835199), np.float64(6.2747276885625975), np.float64(6.271091021114691)]
Trendlines calculated successfully.
An error occurred: 'support_slope'



Code for trendline.py

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 = support_slope * (len(data) - 1 - i) + support_intercept
resist_levels = 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']]




Heres my plotter.py code.

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}")



Ive tried a bunch of things, including adding debugging statements but im pretty lost on this. I appreciate any help given

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