79152163
Date: 2024-11-03 06:28:05
Score: 1.5
Natty:
user28102875,
There is an issue with MultiIndex in Dataframe, this way we can fix it
# Drop columns that are not needed
if isinstance(df.columns, pd.MultiIndex) and df.columns.nlevels > 1:
df.columns = df.columns.droplevel(1)
Also StochasticOscillator requires High, Low, Close
stoch_rsi = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14, smooth_window=3)
There is your full fixed code (maybe you need another changes for logic, but it works):
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from ta.trend import EMAIndicator
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.volatility import AverageTrueRange
# Data Retrieval
def get_data(ticker, start, end):
df = yf.download(ticker, start=start, end=end)
df.reset_index(inplace=True) # Reset the index to make it easier to work with
df['Date'] = df['Date'].dt.date # Convert to date type if needed
print("Initial DataFrame head:\n", df.head()) # Print the first few rows
print("Initial DataFrame shape:", df.shape) # Print the shape of the DataFrame
# Drop columns that are not needed
if isinstance(df.columns, pd.MultiIndex) and df.columns.nlevels > 1:
df.columns = df.columns.droplevel(1)
return df
# Indicator Calculation
def calculate_indicators(df):
# Exponential Moving Averages (EMAs)
df['EMA8'] = EMAIndicator(df['Close'], window=8).ema_indicator()
df['EMA14'] = EMAIndicator(df['Close'], window=14).ema_indicator()
df['EMA50'] = EMAIndicator(df['Close'], window=50).ema_indicator()
# Print shapes to debug
print("EMA8 shape:", df['EMA8'].shape)
print("EMA14 shape:", df['EMA14'].shape)
print("EMA50 shape:", df['EMA50'].shape)
# Relative Strength Index (RSI)
df['RSI14'] = RSIIndicator(df['Close'], window=14).rsi()
print("RSI14 shape:", df['RSI14'].shape)
# Stochastic RSI
stoch_rsi = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14, smooth_window=3)
df['StochRSI_K'] = stoch_rsi.stoch()
df['StochRSI_D'] = stoch_rsi.stoch_signal()
# Print shapes
print("StochRSI_K shape:", df['StochRSI_K'].shape)
print("StochRSI_D shape:", df['StochRSI_D'].shape)
# Average True Range (ATR)
atr = AverageTrueRange(df['High'], df['Low'], df['Close'], window=14)
df['ATR14'] = atr.average_true_range()
print("ATR14 shape:", df['ATR14'].shape)
# Drop rows with NaN values
df.dropna(inplace=True)
return df
# Entry Conditions
def signal_generator(df):
df['Long'] = (
(df['Close'] > df['EMA8']) &
(df['EMA8'] > df['EMA14']) &
(df['EMA14'] > df['EMA50']) &
(df['StochRSI_K'] > df['StochRSI_D'])
)
df['Short'] = (
(df['Close'] < df['EMA8']) &
(df['EMA8'] < df['EMA14']) &
(df['EMA14'] < df['EMA50']) &
(df['StochRSI_K'] < df['StochRSI_D'])
)
return df
# Position Management
def backtest_strategy(df):
position = None
entry_price = 0
results = []
for index, row in df.iterrows():
if position is None: # No open position
if row['Long']:
position = 'long'
entry_price = row['Close']
stop_loss = entry_price - (3 * row['ATR14'])
take_profit = entry_price + (2 * row['ATR14'])
elif row['Short']:
position = 'short'
entry_price = row['Close']
stop_loss = entry_price + (3 * row['ATR14'])
take_profit = entry_price - (2 * row['ATR14'])
elif position == 'long':
if row['Close'] >= take_profit or row['Close'] <= stop_loss:
results.append(row['Close'] - entry_price) # Profit or loss
position = None # Close position
elif position == 'short':
if row['Close'] <= take_profit or row['Close'] >= stop_loss:
results.append(entry_price - row['Close']) # Profit or loss
position = None # Close position
return results
# Performance Metrics
def calculate_performance(results, df):
df['Daily_Returns'] = df['Close'].pct_change()
df['Strategy_Returns'] = pd.Series(results).shift(1).fillna(0) # Align with dates
df['Cumulative_Strategy'] = (1 + df['Strategy_Returns']).cumprod()
df['Cumulative_Buy_Hold'] = (1 + df['Daily_Returns']).cumprod()
return df
# Visualization
def plot_performance(df):
plt.figure(figsize=(12, 6))
plt.plot(df['Cumulative_Strategy'], label='Strategy Returns', color='blue')
plt.plot(df['Cumulative_Buy_Hold'], label='Buy and Hold Returns', color='orange')
plt.title('Cumulative Returns: Strategy vs. Buy and Hold')
plt.xlabel('Date')
plt.ylabel('Cumulative Returns')
plt.legend()
plt.grid()
plt.show()
# Main Execution
ticker = 'IWV'
start_date = '2020-01-01'
end_date = '2024-01-01'
data = get_data(ticker, start_date, end_date)
# Check the DataFrame contents before calculating indicators
print("Data after retrieval:\n", data.head())
print("Data shape after retrieval:", data.shape)
# Now attempt to calculate indicators
data = calculate_indicators(data)
data = signal_generator(data)
results = backtest_strategy(data)
data = calculate_performance(results, data)
plot_performance(data)
Reasons:
Posted by: Evgenii Lazarev - Probably link only (1):
- Long answer (-1):
- Has code block (-0.5):
- User mentioned (1): user28102875
- Low reputation (1):
