# 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: Calculate Fisher Transform
def calculate_fisher(data, period=10):
    # Ensure Lowest Low and Highest High are single-column Series
    data['Lowest Low'] = data['Low'].rolling(window=period, min_periods=1).min()
    data['Highest High'] = data['High'].rolling(window=period, min_periods=1).max()

    # Calculate normalized price (x) as a single-column Series
    data['x'] = 2 * ((data['Close'] - data['Lowest Low']) / 
                     (data['Highest High'] - data['Lowest Low']) - 0.5)

    # Clip x to stay within the range [-0.999, 0.999]
    data['x'] = data['x'].clip(lower=-0.999, upper=0.999)

    # Apply Fisher Transform
    data['Fisher'] = 0.5 * np.log((1 + data['x']) / (1 - data['x']))
    data['Fisher Signal'] = data['Fisher'].shift(1)  # Signal line

    return data

# Apply Fisher Transform calculation
period = 10
data = calculate_fisher(data, period)

# Step 3: Plot Close Price and Fisher Transform
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 Fisher Transform
plt.subplot(2, 1, 2)
plt.plot(data['Fisher'], label='Fisher Transform', color='purple', linewidth=1.5)
plt.plot(data['Fisher Signal'], label='Fisher Signal', color='orange', linestyle='--', linewidth=1.5)
plt.axhline(0, color='black', linestyle='--', linewidth=1, label='Zero Line')
plt.title(f'Fisher Transform for {symbol}')
plt.xlabel('Date')
plt.ylabel('Value')
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_1236625/3302577336.py in ?()
     29     return data
     30 
     31 # Apply Fisher Transform calculation
     32 period = 10
---> 33 data = calculate_fisher(data, period)
     34 
     35 # Step 3: Plot Close Price and Fisher Transform
     36 plt.figure(figsize=(14, 7))


/tmp/ipykernel_1236625/3302577336.py in ?(data, period)
     15     data['Lowest Low'] = data['Low'].rolling(window=period, min_periods=1).min()
     16     data['Highest High'] = data['High'].rolling(window=period, min_periods=1).max()
     17 
     18     # Calculate normalized price (x) as a single-column Series
---> 19     data['x'] = 2 * ((data['Close'] - data['Lowest Low']) / 
     20                      (data['Highest High'] - data['Lowest Low']) - 0.5)
     21 
     22     # Clip x to stay within the range [-0.999, 0.999]


~/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 x

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