# 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 DMI and ADX
def calculate_dmi_adx(data, period=14):
    high = data['High']
    low = data['Low']
    close = data['Close']

    # Calculate True Range (TR)
    tr1 = high - low
    tr2 = abs(high - close.shift(1))
    tr3 = abs(low - close.shift(1))
    tr = pd.DataFrame({'TR1': tr1, 'TR2': tr2, 'TR3': tr3}).max(axis=1)

    # Calculate +DM and -DM
    plus_dm = high.diff()
    minus_dm = low.diff()
    plus_dm[plus_dm < 0] = 0
    minus_dm[minus_dm > 0] = 0
    minus_dm = abs(minus_dm)

    # Smooth TR, +DM, -DM
    atr = tr.rolling(window=period).mean()
    smoothed_plus_dm = plus_dm.rolling(window=period).mean()
    smoothed_minus_dm = minus_dm.rolling(window=period).mean()

    # Calculate +DI and -DI
    plus_di = (smoothed_plus_dm / atr) * 100
    minus_di = (smoothed_minus_dm / atr) * 100

    # Calculate DX and ADX
    dx = (abs(plus_di - minus_di) / (plus_di + minus_di)) * 100
    adx = dx.rolling(window=period).mean()

    # Assign results to DataFrame
    data['+DI'] = plus_di
    data['-DI'] = minus_di
    data['ADX'] = adx

    return data

# Apply DMI and ADX calculation
period = 14
data = calculate_dmi_adx(data, period)

# Step 3: Plot Close Price, +DI, -DI, and ADX
plt.figure(figsize=(14, 10))

# Plot Close Price
plt.subplot(3, 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 +DI and -DI
plt.subplot(3, 1, 2)
plt.plot(data['+DI'], label='+DI', color='green')
plt.plot(data['-DI'], label='-DI', color='red')
plt.title(f'Directional Movement Index (+DI and -DI) for {symbol}')
plt.xlabel('Date')
plt.ylabel('Index Value')
plt.legend(loc='best')
plt.grid(True)

# Plot ADX
plt.subplot(3, 1, 3)
plt.plot(data['ADX'], label='ADX', color='purple')
plt.axhline(25, color='black', linestyle='--', linewidth=1, label='Trend Threshold (25)')
plt.title(f'Average Directional Index (ADX) for {symbol}')
plt.xlabel('Date')
plt.ylabel('ADX Value')
plt.legend(loc='best')
plt.grid(True)

plt.tight_layout()
plt.show()

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



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

ValueError                                Traceback (most recent call last)

Cell In[6], line 53
     51 # Apply DMI and ADX calculation
     52 period = 14
---> 53 data = calculate_dmi_adx(data, period)
     55 # Step 3: Plot Close Price, +DI, -DI, and ADX
     56 plt.figure(figsize=(14, 10))


Cell In[6], line 22, in calculate_dmi_adx(data, period)
     20 tr2 = abs(high - close.shift(1))
     21 tr3 = abs(low - close.shift(1))
---> 22 tr = pd.DataFrame({'TR1': tr1, 'TR2': tr2, 'TR3': tr3}).max(axis=1)
     24 # Calculate +DM and -DM
     25 plus_dm = high.diff()


File ~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/frame.py:778, in DataFrame.__init__(self, data, index, columns, dtype, copy)
    772     mgr = self._init_mgr(
    773         data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
    774     )
    776 elif isinstance(data, dict):
    777     # GH#38939 de facto copy defaults to False only in non-dict cases
--> 778     mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
    779 elif isinstance(data, ma.MaskedArray):
    780     from numpy.ma import mrecords


File ~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/internals/construction.py:503, in dict_to_mgr(data, index, columns, dtype, typ, copy)
    499     else:
    500         # dtype check to exclude e.g. range objects, scalars
    501         arrays = [x.copy() if hasattr(x, "dtype") else x for x in arrays]
--> 503 return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)


File ~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/internals/construction.py:114, in arrays_to_mgr(arrays, columns, index, dtype, verify_integrity, typ, consolidate)
    111 if verify_integrity:
    112     # figure out the index, if necessary
    113     if index is None:
--> 114         index = _extract_index(arrays)
    115     else:
    116         index = ensure_index(index)


File ~/miniconda3/envs/ml312-2024/lib/python3.12/site-packages/pandas/core/internals/construction.py:667, in _extract_index(data)
    664         raise ValueError("Per-column arrays must each be 1-dimensional")
    666 if not indexes and not raw_lengths:
--> 667     raise ValueError("If using all scalar values, you must pass an index")
    669 if have_series:
    670     index = union_indexes(indexes)


ValueError: If using all scalar values, you must pass an index

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