import yfinance as yf
import pandas as pd
import requests
import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings('ignore')

df_logo = pd.read_csv("https://raw.githubusercontent.com/jarvisx17/nifty500/main/Stocks.csv")

def calculate_profit(ltp, share, entry):
  tgt1 = entry + (0.02 * entry)
  tgt2 = entry + (0.04 * entry)
  if ltp > tgt2:
      profit = round((share / 3 * (tgt1-entry)) + (share / 3 * (tgt2-entry)) + (share / 3 * (ltp-entry)), 2)
  elif ltp > tgt1 and ltp < tgt2:
      profit = round((share / 3 * (tgt1-entry)) + ((share / 3) * 2 * (ltp-entry)), 2)
  elif ltp > tgt1:
      profit = round(share * (ltp-entry), 2)   
  else:
      profit = round(share * (ltp-entry), 2)
  return profit

def info(ticker):
    data = df_logo[df_logo['Symbol'] == ticker]
    logo = data.logo.values[0]
    Industry = data.Industry.values[0]
    return logo, Industry


def calculate_percentage_loss(buying_price, ltp):
    percentage_loss = ((ltp - buying_price) / buying_price) * 100
    return f"{percentage_loss:.2f}%"

def latestprice(ticker):
    ticker = ticker.split(".")[0]
    url = f'https://groww.in/v1/api/stocks_data/v1/accord_points/exchange/NSE/segment/CASH/latest_prices_ohlc/{ticker}'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0'
    }
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        data = response.json()
        return float(data['ltp'])
    else:
        return 0.0

def process_dataframe(df):

    def get_rsi(close, lookback):
        ret = close.diff()
        up = []
        down = []
        for i in range(len(ret)):
            if ret[i] < 0:
                up.append(0)
                down.append(ret[i])
            else:
                up.append(ret[i])
                down.append(0)
        up_series = pd.Series(up)
        down_series = pd.Series(down).abs()
        up_ewm = up_series.ewm(com=lookback - 1, adjust=False).mean()
        down_ewm = down_series.ewm(com=lookback - 1, adjust=False).mean()
        rs = up_ewm / down_ewm
        rsi = 100 - (100 / (1 + rs))
        rsi_df = pd.DataFrame(rsi).rename(columns={0: 'RSI'}).set_index(close.index)
        rsi_df = rsi_df.dropna()
        return rsi_df[3:]

    df['RSI'] = get_rsi(df['Close'], 14)
    df['SMA20'] = df['Close'].rolling(window=20).mean()
    df.drop(['Adj Close'], axis=1, inplace=True)
    df = df.dropna()

    return df

def fin_data(ticker, startdate):

  ltp = latestprice(ticker)
  df=yf.download(ticker, period="36mo", progress=False)
  df = process_dataframe(df)
  df.reset_index(inplace=True)
  df['Prev_RSI'] = df['RSI'].shift(1).round(2)
  df = df.dropna()
  df.reset_index(drop=True, inplace=True)
  df[['Open', 'High', 'Low', 'Close',"RSI","SMA20"]] = df[['Open', 'High', 'Low', 'Close',"RSI", "SMA20"]].round(2)
  df = df[200:]
  df['Target1'] = df['High'] + (df['High'] * 0.02)
  df['Target1'] = df['Target1'].round(2)
  df['Target2'] = df['High'] + (df['High'] * 0.04)
  df['Target2'] = df['Target2'].round(2)
  df['Target3'] = "will announced"
  df['SL'] = df['Low']
  df['LTP'] = ltp
  date_index = df.loc[df['Date'] == startdate].index[0]
  df = df.loc[date_index-1:]
  df['Date'] = pd.to_datetime(df['Date'])
  df.reset_index(drop=True,inplace=True)

  return df

def eqt(ticker, startdate, share_qty = 90):

  df = fin_data(ticker, startdate)
  logo, Industry = info(ticker)
  entry = False
  trading = False
  shares_held = 0
  buy_price = 0
  target1 = False
  target2 = False
  target3 = False
  tgt1 = 0
  tgt2 = 0
  tgt3 = 0
  total_profit = 0
  profits = []
  stop_loss = 0
  capital_list = []
  data = {}
  totalshares = share_qty
  ltp = latestprice(ticker)

  for i in range(1, len(df)-1):
      try:
          if df.at[i, 'RSI'] > 60 and df.at[i - 1, 'RSI'] < 60 and df.at[i, 'High'] < df.at[i + 1, 'High'] and not entry and not trading:
              buy_price = df.at[i, 'High']
              stop_loss = df.at[i, 'Low']
              capital = buy_price * share_qty
              capital_list.append(capital)
              shares_held = share_qty
              entdate = df.at[i+1, 'Date']
              entry_info = {"Date": pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Note": "Entry Successful", "SL": stop_loss}
              entryStock_info = df.iloc[i: i+1].reset_index(drop=True).to_dict(orient='records')[0] # Entry info
              entryStock_info['Date'] = str(pd.to_datetime(df.at[i, 'Date']).strftime('%d-%m-%Y'))
              data['StockInfo'] = {}
              data['StockInfo']['Stock'] = {}
              data['StockInfo']['Stock']['Name'] = ticker
              data['StockInfo']['Stock']['Industry'] = Industry
              data['StockInfo']['Stock']['Logo'] = logo
              data['StockInfo']['Stock']['Status'] = "Active"
              data['StockInfo']['Stock']['Levels'] = "Entry"
              data['StockInfo']['Stock']['Values'] = entryStock_info
              buying_price = entryStock_info['High']
              ltp = entryStock_info['LTP']
              data['StockInfo']['Stock']['Values']['Share QTY'] = share_qty
              data['StockInfo']['Stock']['Values']['Invested Amount'] = (share_qty * buy_price).round(2)
              data['StockInfo']['Stock']['Values']['Percentage'] = calculate_percentage_loss(buying_price, ltp)
              data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price)
              data['StockInfo']['Entry'] = entry_info
              entry = True
              trading = True

          if trading and not target1:
              if (df.at[i + 1, 'High'] - buy_price) >= 0.02 * buy_price:
                  stop_loss = buy_price
                  target1 = True
                  tgt1 = 0.02 * buy_price * (share_qty / 3)
                  shares_held -= (share_qty / 3)
                  total_profit = round(tgt1,2)
                  target1_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt1,2), "Remaining Shares": shares_held,"Note" : "TGT1 Achieved Successfully", "SL" : stop_loss}
                  data['StockInfo']['TGT1'] = target1_info
                  data['StockInfo']['Stock']['Values']['SL'] = stop_loss
                  data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT1"
                  data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price)
                  data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...."

          if trading and target1 and not target2:
              if (df.at[i + 1, 'High'] - buy_price) >= 0.04 * buy_price:
                  target2 = True
                  tgt2 = 0.04 * buy_price * (share_qty / 3)
                  total_profit += round(tgt2,2)
                  shares_held -= (share_qty / 3)
                  data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT2"
                  data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price)
                  target2_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt2,2), "Remaining Shares": shares_held,"Note" : "TGT2 Achieved Successfully", "SL" : stop_loss}
                  data['StockInfo']['TGT2'] = target2_info
                  data['StockInfo']['Entry']['Trade Status']  = "Trading is ongoing...."

          if trading and target2 and not target3:
              if (df.at[i + 1, 'Open'] < df.at[i + 1, 'SMA20'] < df.at[i + 1, 'Close']) or (df.at[i + 1, 'Open'] > df.at[i + 1, 'SMA20'] > df.at[i + 1, 'Close']):
                  stop_loss = df.at[i + 1, 'Low']
                  data['StockInfo']['Stock']['Values']['SL'] = stop_loss
                  if df.at[i + 2, 'Low'] < stop_loss:
                      target3 = True
                      tgt3 = stop_loss * (share_qty / 3)
                      shares_held -= (share_qty / 3)
                      total_profit += round(tgt3,2)
                      target3_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt3,2), "Remaining Shares": shares_held,"Note" : "TGT3 Achieved Successfully", "SL" : stop_loss}
                      data['StockInfo']['Stock']['Values']['Target3'] = tgt3
                      data['StockInfo']['TGT3'] = target3_info
                      data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" TGT3"
                      data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price)
                      data['StockInfo']['TotalProfit'] = {}
                      data['StockInfo']['TotalProfit']['Profit'] = total_profit
                      data['StockInfo']['Entry']['Trade Status']  = "Trade closed successfully...."
                      data['StockInfo']['TotalProfit']['Trade Status']  = "Trade closed successfully...."
                      break

          if (df.at[i + 1, 'Low'] < stop_loss and trading and entdate != df.at[i + 1, 'Date']) or stop_loss > ltp:
              profit_loss = (shares_held * stop_loss) - (shares_held * buy_price)
              total_profit += profit_loss
              profits.append(total_profit)
              shares_held = 0
              if data['StockInfo']['Stock']['Values']['Target3'] == "will announced" :
                data['StockInfo']['Stock']['Values']['Target3'] = "-"
              data['StockInfo']['Stock']['Status'] = "Closed"
              data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" SL"
              stoploss_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : total_profit, "SL" : stop_loss, "Remaining Shares": shares_held,"Note" : "SL Hit Successfully"}
              data['StockInfo']['SL'] = stoploss_info
              data['StockInfo']['TotalProfit'] = {}
              data['StockInfo']['TotalProfit']['Profit'] = round(total_profit, 2)
              data['StockInfo']['Stock']['Values']['Total P/L'] = round(total_profit, 2)
              data['StockInfo']['Entry']['Trade Status']  = "Trade closed successfully...."
              data['StockInfo']['TotalProfit']['Trade Status']  = "Trade closed successfully...."
              buy_price = 0
              entry = False
              trading = False
              target1 = target2 = target3 = False
              tgt1 = tgt2 = tgt3 = 0
              total_profit = 0
              break

      except IndexError:
          continue

  if capital_list and profits:

    return data

  else:
    if data:

      return data

    else:
      data['StockInfo'] = {}
      data['StockInfo']['Stock'] = {}
      data['StockInfo']['Stock']['Name']  = ticker
      data['StockInfo']['Stock']['Industry'] = Industry
      data['StockInfo']['Stock']['Logo'] = logo
      data['StockInfo']['Stock']['Status'] = "Waiting for entry"
      entryStock_info = df.iloc[1: 2].reset_index(drop=True).to_dict(orient='records')[0] # Entry info
      entryStock_info['Date'] = str(pd.to_datetime(df.at[1, 'Date']).strftime('%d-%m-%Y'))
      data['StockInfo']['Stock']['Values'] = entryStock_info
      data['StockInfo']['Stock']['Values']['Target3'] = "-"
      data['StockInfo']['Info'] = "Don't buy stock right now...."

      return data