# Created: 20250104

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml312-2024; pyv: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]'

print(pyu.ps2("yfinance pandas matplotlib"))

yfinance==0.2.51
pandas==2.2.3
matplotlib==3.9.3

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Step 1: Download historical data
symbol = "^GSPC"  # S&P 500 as an example
start = "2020-01-01"
end = "2023-12-31"
data = yf.download(symbol, start=start, end=end)

# Step 2: Helper function to calculate RSI
def calculate_rsi(series, period):
    delta = series.diff(1)
    gain = delta.where(delta > 0, 0).rolling(window=period).mean()
    loss = -delta.where(delta < 0, 0).rolling(window=period).mean()
    rs = gain / loss
    return 100 - (100 / (1 + rs))

# Step 3: Calculate streak
def calculate_streak(data):
    streak = np.zeros(len(data))
    for i in range(1, len(data)):
        if data['Close'].iloc[i] > data['Close'].iloc[i - 1]:
            streak[i] = streak[i - 1] + 1
        elif data['Close'].iloc[i] < data['Close'].iloc[i - 1]:
            streak[i] = streak[i - 1] - 1
        else:
            streak[i] = 0
    data['Streak'] = streak
    return data

# Step 4: Calculate Connors RSI (CRSI)
def calculate_connors_rsi(data, rsi_period=3, streak_rsi_period=2, percent_rank_period=100):
    # Calculate RSI
    data['RSI'] = calculate_rsi(data['Close'], rsi_period)

    # Calculate Streak and Streak RSI
    data = calculate_streak(data)
    data['Streak RSI'] = calculate_rsi(data['Streak'], streak_rsi_period)

    # Calculate Percentage Rank
    data['Price Change'] = data['Close'].diff(1)
    data['Percent Rank'] = data['Price Change'].rolling(window=percent_rank_period).apply(
        lambda x: (x.rank(pct=True).iloc[-1]) * 100, raw=False
    )

    # Calculate Connors RSI
    data['Connors RSI'] = (data['RSI'] + data['Streak RSI'] + data['Percent Rank']) / 3
    return data

# Apply Connors RSI calculation
data = calculate_connors_rsi(data)

# Step 5: Plot Close Price and Connors RSI
plt.figure(figsize=(14, 7))

# Plot Close Price
plt.subplot(2, 1, 1)
plt.plot(data['Close'], label='Close Price', color='blue')
plt.title(f'{symbol} Close Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid(True)

# Plot Connors RSI
plt.subplot(2, 1, 2)
plt.plot(data['Connors RSI'], label='Connors RSI', color='green', linewidth=1.5)
plt.axhline(70, color='red', linestyle='--', linewidth=1, label='Overbought (70)')
plt.axhline(30, color='blue', linestyle='--', linewidth=1, label='Oversold (30)')
plt.title(f'Connors RSI for {symbol}')
plt.xlabel('Date')
plt.ylabel('CRSI')
plt.legend(loc='best')
plt.grid(True)

plt.tight_layout()
plt.show()

[*********************100%***********************]  1 of 1 completed



---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

/tmp/ipykernel_1279737/2295550227.py in ?()
     49     data['Connors RSI'] = (data['RSI'] + data['Streak RSI'] + data['Percent Rank']) / 3
     50     return data
     51 
     52 # Apply Connors RSI calculation
---> 53 data = calculate_connors_rsi(data)
     54 
     55 # Step 5: Plot Close Price and Connors RSI
     56 plt.figure(figsize=(14, 7))


/tmp/ipykernel_1279737/2295550227.py in ?(data, rsi_period, streak_rsi_period, percent_rank_period)
     35     # Calculate RSI
     36     data['RSI'] = calculate_rsi(data['Close'], rsi_period)
     37 
     38     # Calculate Streak and Streak RSI
---> 39     data = calculate_streak(data)
     40     data['Streak RSI'] = calculate_rsi(data['Streak'], streak_rsi_period)
     41 
     42     # Calculate Percentage Rank


/tmp/ipykernel_1279737/2295550227.py in ?(data)
     21 def calculate_streak(data):
     22     streak = np.zeros(len(data))
     23     for i in range(1, len(data)):
---> 24         if data['Close'].iloc[i] > data['Close'].iloc[i - 1]:
     25             streak[i] = streak[i - 1] + 1
     26         elif data['Close'].iloc[i] < data['Close'].iloc[i - 1]:
     27             streak[i] = streak[i - 1] - 1


~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/generic.py in ?(self)
   1575     @final
   1576     def __nonzero__(self) -> NoReturn:
-> 1577         raise ValueError(
   1578             f"The truth value of a {type(self).__name__} is ambiguous. "
   1579             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1580         )


ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

def show_graph(symbol):

pass

show_graph("AMZN")

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

Cell In[5], line 1
----> 1 show_graph("AMZN")


NameError: name 'show_graph' is not defined