# 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 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: Calculate Money Flow Index (MFI)
def calculate_mfi(data, period=14):
    # Calculate Typical Price (TP) as a single Series
    data['Typical Price'] = (data['High'] + data['Low'] + data['Close']) / 3

    # Calculate Raw Money Flow (RMF)
    data['Raw Money Flow'] = data['Typical Price'] * data['Volume']

    # Identify Positive and Negative Money Flows
    data['Positive Money Flow'] = data['Raw Money Flow'].where(data['Typical Price'] > data['Typical Price'].shift(1), 0)
    data['Negative Money Flow'] = data['Raw Money Flow'].where(data['Typical Price'] < data['Typical Price'].shift(1), 0)

    # Calculate Money Flow Ratio (MFR)
    sum_positive = data['Positive Money Flow'].rolling(window=period).sum()
    sum_negative = data['Negative Money Flow'].rolling(window=period).sum()
    data['Money Flow Ratio'] = sum_positive / sum_negative

    # Calculate MFI
    data['MFI'] = 100 - (100 / (1 + data['Money Flow Ratio']))

    return data

# Apply MFI calculation
data = calculate_mfi(data)

# Step 3: Plot Close Price and MFI
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 MFI
plt.subplot(2, 1, 2)
plt.plot(data['MFI'], label='MFI (Money Flow Index)', color='green', linewidth=1.5)
plt.axhline(80, color='red', linestyle='--', linewidth=1, label='Overbought (80)')
plt.axhline(20, color='blue', linestyle='--', linewidth=1, label='Oversold (20)')
plt.title(f'Money Flow Index (MFI) for {symbol}')
plt.xlabel('Date')
plt.ylabel('MFI')
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_1248056/1062155594.py in ?()
     30 
     31     return data
     32 
     33 # Apply MFI calculation
---> 34 data = calculate_mfi(data)
     35 
     36 # Step 3: Plot Close Price and MFI
     37 plt.figure(figsize=(14, 7))


/tmp/ipykernel_1248056/1062155594.py in ?(data, period)
     13     # Calculate Typical Price (TP) as a single Series
     14     data['Typical Price'] = (data['High'] + data['Low'] + data['Close']) / 3
     15 
     16     # Calculate Raw Money Flow (RMF)
---> 17     data['Raw Money Flow'] = data['Typical Price'] * data['Volume']
     18 
     19     # Identify Positive and Negative Money Flows
     20     data['Positive Money Flow'] = data['Raw Money Flow'].where(data['Typical Price'] > data['Typical Price'].shift(1), 0)


~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/frame.py in ?(self, key, value)
   4297             self._setitem_frame(key, value)
   4298         elif isinstance(key, (Series, np.ndarray, list, Index)):
   4299             self._setitem_array(key, value)
   4300         elif isinstance(value, DataFrame):
-> 4301             self._set_item_frame_value(key, value)
   4302         elif (
   4303             is_list_like(value)
   4304             and not self.columns.is_unique


~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/frame.py in ?(self, key, value)
   4455 
   4456             return self.isetitem(locs, value)
   4457 
   4458         if len(value.columns) > 1:
-> 4459             raise ValueError(
   4460                 "Cannot set a DataFrame with multiple columns to the single "
   4461                 f"column {key}"
   4462             )


ValueError: Cannot set a DataFrame with multiple columns to the single column Raw Money Flow

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