Hugging Face's logo

Spaces:
kollis
/
Portfoliotracking
Sleeping

App Files Community
Portfoliotracking
File size: 5,118 Bytes 
74e0921
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
 
import gradio as gr
import pandas as pd
import yfinance as yf
from datetime import timedelta,datetime
import pytz
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from IPython.display import display
import functions

nifty_list = ["ADANIENT","ADANIPORTS","APOLLOHOSP","ASIANPAINT","AXISBANK","BAJAJ-AUTO","BAJFINANCE","BAJAJFINSV","BPCL","BHARTIARTL","BRITANNIA","CIPLA","COALINDIA","DIVISLAB","DRREDDY","EICHERMOT","GRASIM","HCLTECH","HDFCBANK","HDFCLIFE","HEROMOTOCO","HINDALCO","HINDUNILVR","ICICIBANK","ITC","INDUSINDBK","INFY","JSWSTEEL","KOTAKBANK","LTIM","LT","M&M","MARUTI","NTPC","NESTLEIND","ONGC","POWERGRID","RELIANCE","SBILIFE","SBIN","SUNPHARMA","TCS","TATACONSUM","TATAMOTORS","TATASTEEL","TECHM","TITAN","UPL","ULTRACEMCO","WIPRO","%5ENSEI"]

class Stocks:
    def __init__(self, symbol):
        self.symbol = symbol
        self.data = self.fetch_data()

    def fetch_data(self):
        try:
            # Construct the ticker symbol based on the first letter
            ticker_symbol = self.symbol if self.symbol[0] == '%' else f"{self.symbol}.ns"

            # Fetch historical data based on the constructed ticker symbol
            data = yf.Ticker(ticker_symbol).history(period="10y", auto_adjust=True)
            return data
        except Exception as e:
            print(f"Error fetching data for {self.symbol}: {e}")
            return None

    def currentdateavailability(self, curDate):
        if curDate in self.data.index:
            return curDate
        else:
            # Convert curDate to datetime and subtract one day
            curDate_dt = datetime.strptime(curDate, "%Y-%m-%d")
            newcDate_dt = curDate_dt - timedelta(days=1)

            # Convert newcDate to string and call the method again
            newcDate_str = newcDate_dt.strftime("%Y-%m-%d")
            return self.currentdateavailability(newcDate_str)

    def CurPrice(self, curDate=None):
        curDate = self.currentdateavailability(curDate)
        return self.data.loc[curDate, 'Close'] if curDate is not None else self.data.iloc[-1]['Close']

    def NDayRet(self, N, curDate):
        curDate = self.currentdateavailability(curDate)
        NDate = self.data.index[self.data.index.get_loc(curDate) - N]
        return self.data.loc[curDate, 'Close'] - self.data.loc[NDate, 'Close']

    def DailyRet(self, curDate):
        curDate = self.currentdateavailability(curDate)
        return self.data.loc[curDate, 'Close'] - self.data.loc[curDate, 'Open']


    def Last30daysPrice(self, curDate=None):
        if curDate is not None:
            curDate = self.currentdateavailability(curDate)
            curDate_index = self.data.index.get_loc(curDate)
            return self.data.iloc[curDate_index - 30:curDate_index]['Close'].values
        else:
            return self.data.iloc[-30:]['Close'].values


    # This below function returns last 30 calender days close prices i.e. 30 days including holidays so less than 30 days close values are returned. Above fuction gives last 30 trading day close prices.
    # def Last30daysPrice(self, curDate=None):
    #     curDate = self.currentdateavailability(curDate)

    #     if curDate is not None:
    #         # Calculate date 30 days ago
    #         curDate_dt = datetime.strptime(curDate, "%Y-%m-%d")
    #         days_ago_30 = curDate_dt - timedelta(days=30)
    #         thirty_days_ago_date = days_ago_30.strftime("%Y-%m-%d")

    #         # Ensure the availability of 30 days ago date
    #         thirty_days_ago_date = self.currentdateavailability(thirty_days_ago_date)

    #         # Get the index of curDate and 30 days ago date in the data
    #         curDate_index = self.data.index.get_loc(curDate)
    #         thirty_days_ago_index = self.data.index.get_loc(thirty_days_ago_date)

    #         # Return close values from 30 days ago to curDate in an array
    #         return self.data.iloc[thirty_days_ago_index:curDate_index + 1]['Close'].values
    #     else:
    #         return self.data.iloc[-30:]['Close'].values


stocks_dict = {symbol: Stocks(symbol) for symbol in nifty_list}

nifty_stocks = {symbol: stocks_dict[symbol] for symbol in nifty_list[:-1]}

nifty50 = {"nifty50": stocks_dict[nifty_list[-1]]}

title = "Portfolio tracking Nifty50 Stocks"
description = """
This App Demo is made for an Assignment. This Demo takes Initial Equity, Start Date, End Date, Time Window as inputs
"""


iface = gr.Interface(
    fn=final_function,
    inputs=[
        gr.Textbox(label="Equity"),
        gr.Textbox(label="Start Date"),
        gr.Textbox(label="End Date"),
        gr.Textbox(label="N-day Window")
    ],
    outputs=[
        gr.Image(type="pil"),
        gr.Textbox(label="Strategy CAGR (%)"),
        gr.Textbox(label="Strategy Volatility (%)"),
        gr.Textbox(label="Strategy Sharpe Ratio"),
        gr.Textbox(label="Benchmark CAGR (%)"),
        gr.Textbox(label="Benchmark Volatility (%)"),
        gr.Textbox(label="Benchmark Sharpe Ratio")
    ],
    title=title,
    description=description,
    
)

if __name__ == "__main__":
    iface.launch()