from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from fastapi import FastAPI, Request, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import warnings
import yfinance as yf
import pandas as pd
import requests

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

df_logo = pd.read_csv("https://raw.githubusercontent.com/jarvisx17/nifty500/main/Nifty500.csv")

async 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

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

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

async def latestprice(ticker):
    ticker = ticker.split(".")[0]
    url = f"https://stock-market-lo24myw5sq-el.a.run.app/currentprice?ticker={ticker}"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['ltp']
    else:
        return "N/A"

async 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

async def fin_data(ticker, startdate):

  ltp = await latestprice(ticker)
  df=yf.download(ticker, period="36mo", progress=False)
  df = await 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

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

  df = await fin_data(ticker, startdate)
  logo, Industry = await 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 = await 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'] = await calculate_percentage_loss(buying_price, ltp)
              data['StockInfo']['Stock']['Values']['Total P/L'] = await 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'] = await 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'] = await 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'] = await 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


app = FastAPI()

origins = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# @app.get('/')
# def index():
#     return {"message": "welcome to Investify"}

templates = Jinja2Templates(directory="templates")

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("hello.html", {"request": request})

# @app.post('/process_stock_details')
# async def process_stock_details(request: Request):
#     data = await request.json()
#     processed_data = {
#         'symbol': data['symbol'],
#         'date': data['date'],
#         'share': data['share']
#     }
#     return processed_data

@app.get('/data')
async def get_data(ticker: str, date: str, qty: int):
    try:
        response = await eqt(ticker, date, qty)
        return response
    except:
        return {"Timeout" : "Error"}
        

# @app.post('/data')
# async def post_data(request: Request):
#     data = await request.json()
#     ticker = data.get('ticker')
#     date = data.get('date')
#     share_qty = data.get('qty')
#     response = data_manager.get_equity_data(ticker, date, share_qty)
#     return response
    
# if __name__ == "__main__":
#     import uvicorn
#     uvicorn.run(app, host="0.0.0.0", port=8000)