from webpage_background import *
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
from matplotlib.image import imread
img = imread('dataframe.png')
import warnings
warnings.filterwarnings('ignore')
# current quarter start
print(pd.date_range(end=pd.Timestamp.now(), periods=1, freq='QS'))
# current quarter end
print(pd.date_range(start=pd.Timestamp.now(), periods=1, freq='Q'))
ndays,tdays=daysuntil()
print("There are", ndays, " normal and ", tdays, " trading days until the next quarter.")

DatetimeIndex(['2023-07-01 09:29:14.886762'], dtype='datetime64[ns]', freq='QS-JAN')
DatetimeIndex(['2023-09-30 09:29:14.887216'], dtype='datetime64[ns]', freq='Q-DEC')
There are 70  normal and  50  trading days until the next quarter.

average_rank_neo = get_average_rank(symbols_sw)

[*********************100%***********************]  257 of 257 completed

7 Failed downloads:
['FDCB.SW', 'BSSANB.SW', 'SUPCON.SW', 'GSI.SW', 'ZHT.SW', 'DFSH.SW', 'YJET.SW']: Exception('%ticker%: No timezone found, symbol may be delisted')

[*********************100%***********************]  10 of 10 completed
Units per stock  ADEN.SW     3
ARYN.SW     4
DUFN.SW     2
EFGN.SW     9
KARN.SW     4
MOBN.SW    26
SCR.SW     19
SKAN.SW     6
VPBN.SW    23
YTME.SW     0
Name: 0, dtype: int64
Total Units  96
Expected annual return: 6.5%
Annual volatility: 13.9%
Sharpe Ratio: 0.32
(0.06469321822427757, 0.1390768170430547, 0.32135634949455066)
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print('Swiss Important Stocks ',dt_string)
print(average_rank_neo)

Swiss Important Stocks  23/07/2023 09:29:14
     symbols    relvol  breakout30  breakout52  truerange      frog  final_rank
238  VPBN.SW  0.720056           1           1   4.101371 -0.099206        16.2
20   ARYN.SW  0.422234           1           1   7.442922 -0.099206        13.2
69   DUFN.SW  0.728079           1           1   5.271852 -0.083333         9.6
198  SKAN.SW  0.557694           1           1   7.499739 -0.071429        17.4
120  KARN.SW  1.526518           1           1   7.675323 -0.071429        15.0
5    ADEN.SW  0.641805           1           1   7.400674 -0.055556         5.4
72   EFGN.SW  1.301376           1           1   4.736111 -0.039683        10.2
248  YTME.SW  1.266012           0           0  21.280984 -0.035714        12.0
148  MOBN.SW  0.694211           1           1   3.238564 -0.027778        15.6
188   SCR.SW  0.000000           1           1   5.752380 -0.027778        13.8

average_rank_neo = get_average_rank(stock_list)

[*********************100%***********************]  135 of 135 completed

12 Failed downloads:
['ARCUS.OL', 'NPRO.OL', 'MRCEL.OL', 'SBANK.OL', 'NRS.OL', 'SOLON.OL', 'FJORD.OL', 'FKRFT.OL', 'BANO.OL', 'OCY.OL']: Exception('%ticker%: No timezone found, symbol may be delisted')
['NOR.OL', 'NTS.OL']: Exception('%ticker%: No data found, symbol may be delisted')

[*********************100%***********************]  10 of 10 completed
Units per stock  AKRBP.OL     6
AKSO.OL      3
CADLR.OL    14
DNB.OL      25
KAHOT.OL     3
RECSI.OL     3
SCHA.OL      8
STB.OL      17
SUBC.OL      4
YAR.OL      12
Name: 0, dtype: int64
Total Units  95
Expected annual return: 16.5%
Annual volatility: 19.9%
Sharpe Ratio: 0.73
(0.165309354517166, 0.19857660662829874, 0.731754646151045)
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print('OsloBors Important Stocks ',dt_string)
print(average_rank_neo)

OsloBors Important Stocks  23/07/2023 09:29:14
      symbols    relvol  breakout30  breakout52  truerange      frog  final_rank
118    STB.OL  0.596078           1           1   4.280756 -0.123016        15.6
32   CADLR.OL  0.194112           1           1   6.912679 -0.099206        18.0
119   SUBC.OL  0.459353           1           1   7.715179 -0.091270         2.4
107   SCHA.OL  0.843390           0           0   9.986961 -0.087302        11.4
36     DNB.OL  1.170322           1           1   5.510634 -0.051587        14.4
134    YAR.OL  0.543721           1           1   6.899368 -0.043651        16.2
56   KAHOT.OL  0.564951           1           1  10.480341 -0.031746         3.6
96   RECSI.OL  1.218552           1           0   9.651280 -0.027778         9.0
12    AKSO.OL  1.075701           1           1   9.949501 -0.019841         0.6
11   AKRBP.OL  0.897682           1           0   6.009049 -0.019841         6.6

img = imread('dataframe.png')
fig_im, axim = plt.subplots(figsize=(18,6))
axim.imshow(img,aspect='auto')
plt.show()

average_rank = get_average_rank(symbols_pa)

[*********************100%***********************]  40 of 40 completed

14 Failed downloads:
['EI.PA', 'MT.PA', 'GSZ.PA', 'ALU.PA', 'TEC.PA', 'PP.PA', 'DEXB.PA', 'EAD.PA', 'LG.PA', 'FTE.PA']: Exception('%ticker%: No price data found, symbol may be delisted (1d 2020-01-01 -> 2023-07-23)')
['EDF.PA', 'STM.PA']: Exception('%ticker%: No data found, symbol may be delisted')
['UG.PA', 'FP.PA']: Exception('%ticker%: No timezone found, symbol may be delisted')

[*********************100%***********************]  10 of 10 completed
Units per stock  AC.PA      7
ACA.PA    11
BNP.PA     8
CA.PA     19
DG.PA      9
EN.PA     13
LR.PA      8
RNO.PA     6
SGO.PA     8
SU.PA      5
Name: 0, dtype: int64
Total Units  94
Expected annual return: 12.1%
Annual volatility: 20.1%
Sharpe Ratio: 0.51
(0.12131573885738411, 0.2005918150103192, 0.5050841124906919)
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print('CAC40 Important Stocks ',dt_string)
print(average_rank)

CAC40 Important Stocks  23/07/2023 09:29:14
   symbols    relvol  breakout30  breakout52  truerange      frog  final_rank
15   EN.PA  0.681511           1           1   4.812119 -0.170635         4.2
1   ACA.PA  0.733923           1           1   5.957227 -0.134921         4.8
6   BNP.PA  0.987594           1           1   6.976336 -0.115079         5.4
0    AC.PA  0.588291           1           1   8.039725 -0.111111        10.2
33   SU.PA  0.867013           1           1   5.558842 -0.103175        12.0
11   DG.PA  0.781190           1           1   5.362917 -0.103175         9.0
31  SGO.PA  0.540168           1           1   7.334962 -0.099206         3.0
21   LR.PA  0.883397           1           1   5.453641 -0.099206         8.4
7    CA.PA  0.969929           1           1   5.876706 -0.095238         3.6
29  RNO.PA  0.765737           1           1  10.216693 -0.087302         1.8

average_rank = get_average_rank(symbols_ist)
print('BIST100 Important Stocks',dt_string)
print(average_rank)

[*********************100%***********************]  100 of 100 completed
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Units per stock  ECZYT.IS    10
ENJSA.IS    12
FROTO.IS     6
IPEKE.IS     9
ISMEN.IS    10
MGROS.IS     8
OTKAR.IS    11
SELEC.IS    14
SOKM.IS      8
TTRAK.IS     7
Name: 0, dtype: int64
Total Units  95
Expected annual return: 100.7%
Annual volatility: 33.7%
Sharpe Ratio: 2.93
(1.006881027875822, 0.33667036269049805, 2.931297605138663)
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BIST100 Important Stocks 23/07/2023 09:29:14
     symbols    relvol  breakout30  breakout52  truerange      frog  final_rank
24  ECZYT.IS  4.129043           1           1  14.351569 -0.150794         8.4
63  MGROS.IS  1.146623           1           1  16.176524 -0.130952        14.4
77  SELEC.IS  1.492940           1           1  14.137672 -0.126984        12.0
90  TTRAK.IS  1.245950           1           1  18.165511 -0.119048         3.0
34  FROTO.IS  1.772138           1           1  12.624336 -0.107143        18.0
46  IPEKE.IS  2.319349           0           0  12.720281 -0.107143        15.0
80   SOKM.IS  0.946643           1           1  14.933616 -0.099206        16.8
50  ISMEN.IS  1.426573           1           1  23.669489 -0.099206         2.4
69  OTKAR.IS  1.850998           1           1  16.536143 -0.091270         5.4
28  ENJSA.IS  1.702054           1           1  16.211790 -0.071429        10.8

def convert_prices(tickers, currency, end_date=None):
    # Fetch stock prices for all tickers from Yahoo Finance
    stock_data = yf.download(tickers, start='2020-01-01', end=end_date)
    stock_data = stock_data.fillna(method='ffill')
    Volume= stock_data['Volume']
    # Extract the adjusted close prices for each ticker
    adj_close_data = pd.DataFrame()
    for ticker in tickers:
        adj_close_data[ticker] = stock_data['Adj Close'][ticker]

    # Fetch exchange rate data from Yahoo Finance
    exchange_data = yf.download(currency, start='2020-01-01', end=end_date)
    exchange_data = exchange_data.fillna(method='ffill')
    # Calculate exchange rate for each day
    exchange_data['Adj Close ' + currency] = 1 / exchange_data['Adj Close']

    # Join adjusted close data and exchange rate data based on date
    merged_data = adj_close_data.join(exchange_data['Adj Close ' + currency], how='inner')

    # Convert prices to specified currency
    for ticker in tickers:
        merged_data[ticker] = merged_data[ticker] * merged_data['Adj Close ' + currency]

    return merged_data, Volume

result_sp500 = yf.download(symbols_sp500, start='2020-01-01', end=None)
result_sp500 = result_sp500.fillna(method='ffill')
Volume_sp500= result_sp500['Volume']
result_sp500 = result_sp500['Adj Close']

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Failed to get ticker 'BRK/A' reason: Expecting value: line 1 column 1 (char 0)

[**********************47%                       ]  99 of 212 completed

Failed to get ticker 'BRK/B' reason: Expecting value: line 1 column 1 (char 0)

[*********************100%***********************]  212 of 212 completed

2 Failed downloads:
['BRK/A', 'BRK/B']: Exception('%ticker%: No timezone found, symbol may be delisted')

result, Volume = convert_prices(symbols_pa, 'USDEUR=X', end_date=None)
result_Oslo, Volume_Oslo = convert_prices(stock_list, 'USDNOK=X', end_date=None)
result_SW, Volume_SW = convert_prices(symbols_sw, 'USDCHF=X', end_date=None)

[*********************100%***********************]  40 of 40 completed

14 Failed downloads:
['GSZ.PA', 'TEC.PA', 'MT.PA', 'EI.PA', 'ALU.PA', 'PP.PA', 'DEXB.PA', 'EAD.PA', 'LG.PA', 'FTE.PA']: Exception('%ticker%: No price data found, symbol may be delisted (1d 2020-01-01 -> 2023-07-23)')
['EDF.PA', 'STM.PA']: Exception('%ticker%: No data found, symbol may be delisted')
['UG.PA', 'FP.PA']: Exception('%ticker%: No timezone found, symbol may be delisted')

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[*********************100%***********************]  135 of 135 completed

12 Failed downloads:
['ARCUS.OL', 'NPRO.OL', 'MRCEL.OL', 'SBANK.OL', 'NRS.OL', 'SOLON.OL', 'FKRFT.OL', 'FJORD.OL', 'BANO.OL', 'OCY.OL']: Exception('%ticker%: No timezone found, symbol may be delisted')
['NOR.OL', 'NTS.OL']: Exception('%ticker%: No data found, symbol may be delisted')

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[*********************100%***********************]  257 of 257 completed

7 Failed downloads:

['FDCB.SW', 'BSSANB.SW', 'SUPCON.SW', 'GSI.SW', 'DFSH.SW', 'ZHT.SW', 'YJET.SW']: Exception('%ticker%: No timezone found, symbol may be delisted')

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

import pandas as pd

# Merge two data frames: result and result_2
merged_data = pd.merge(result, result_Oslo, how='outer', left_index=True, right_index=True)
merged_data = pd.merge(merged_data, result_SW, how='outer', left_index=True, right_index=True)
merged_data = pd.merge(merged_data, result_sp500, how='outer', left_index=True, right_index=True)
merged_Vol= pd.merge(Volume, Volume_Oslo, how='outer', left_index=True, right_index=True)
merged_Vol= pd.merge(merged_Vol, Volume_SW, how='outer', left_index=True, right_index=True)
merged_Vol= pd.merge(merged_Vol, Volume_sp500, how='outer', left_index=True, right_index=True)
# Forward-fill NaN values
merged_data.ffill(inplace=True)



# Print the merged and filled data frame
print(merged_data.shape)
print(merged_Vol.shape)

(920, 647)
(920, 644)

# Compare columns of two data frames
df1_columns = set(merged_data.columns)
df2_columns = set(merged_Vol.columns)

# Find columns not in common
columns_not_in_common = df1_columns.symmetric_difference(df2_columns)

# Print the columns not in common
print("Columns not in common:")
for column in columns_not_in_common:
   merged_data = merged_data.drop(columns=[column])

print(merged_data.shape)

Columns not in common:
(920, 644)

top_50_stocks_neo=neo_rank_stocks_by_return(merged_data,merged_Vol)
plotter(top_50_stocks_neo.iloc[:,0].values.tolist())

[*********************100%***********************]  10 of 10 completed
Units per stock  ABNB         4
AKRBP.OL    12
AKSO.OL      7
BX           5
KAHOT.OL     4
LECN.SW      7
MIKN.SW     25
SCHW        12
SUBC.OL     15
VK.PA        4
Name: 0, dtype: int64
Total Units  95
Expected annual return: 27.4%
Annual volatility: 21.2%
Sharpe Ratio: 1.19
(0.2738627510384993, 0.21244146159952723, 1.194977426379485)
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#GLOBAL COMPARISON OS + PA + SW + US
top_50_stocks_neo

	symbols	relvol	breakout30	breakout52	truerange	frog	final_rank
168	SUBC.OL	0.209704	1	1	10.088080	-0.087302	5.4
149	KAHOT.OL	0.325032	1	1	12.162404	-0.055556	6.6
605	SCHW	NaN	0	0	7.271825	-0.051587	8.4
324	MIKN.SW	1.243973	1	1	6.996241	-0.043651	13.2
36	VK.PA	0.810386	0	0	9.829210	-0.027778	14.4
310	LECN.SW	5.913383	1	1	9.740706	-0.027778	3.6
434	ABNB	NaN	1	1	9.860749	-0.023810	16.8
469	BX	NaN	1	1	9.455960	-0.007937	15.6
43	AKSO.OL	0.386474	1	1	11.628397	-0.007937	2.4
135	AKRBP.OL	0.532988	0	0	8.991531	-0.007937	12.6

# #todo for 3.9+ python!!!!
# import yfinance_cache as yfc

# def download_history(tickers, start=None, end=None):
#     if isinstance(tickers, str):
#         tickers = [tickers]
    
#     # Initialize the YahooFinanceCached object
    
#     # Download historical data for each ticker

#     cntr = 0
#     for ticker in tickers:

#        if cntr is 0:
#         toti1=yfc.Ticker('QQQ')
#         data1 = toti1.history(start=start, end=end, interval='1d',adjust_splits=True, adjust_divs=True)
#         data1.index = data1.index.date
#         data1 = data1.groupby(data1.index).first()
#         data1 = data1.fillna(method='ffill') 
#         volume1 = data1['Volume']  
#         data1 = data1['Close']
#         data1.name='QQQ'
#         volume1.name='QQQ'
#         cntr = 1

#        else:      
#             try:
#                 if start and end:
#                     toti=yfc.Ticker(ticker)
#                     data = toti.history(start=start, end=end, interval='1d',adjust_splits=True, adjust_divs=True)
#                 else:
#                     toti=yfc.Ticker(ticker)
#                     data = toti.history(interval='1d',adjust_splits=True, adjust_divs=True)
#                 data.index = data.index.date
#                 data = data.groupby(data.index).first()
#                 data = data.fillna(method='ffill')    
#                 volume = data['Volume']  
#                 data = data['Close']
#                 volume.name=ticker
#                 data.name=ticker

#                 data1 = pd.merge(data1, data, how='outer', left_index=True, right_index=True)
#                 volume1 = pd.merge(volume1, volume, how='outer', left_index=True, right_index=True)   

#             except:
#                 print(ticker, 'symbol error')
     
       
    
#     # Merge the dataframes into a single dataframe
#     #merged_data = pd.concat(dataframes, axis=1)
    
#     return data1, volume1

# xx, vol = download_history(symbols_sp500, start='2020-01-01', end='2023-01-01')
# xx

# xx = yf.download('LEPU.SW', start='2020-01-01', end='2023-01-01')
# xx['Close'].plot()

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