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from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
app = FastAPI()
templates = Jinja2Templates(directory="templates")
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
return templates.TemplateResponse("hello.html", {"request": request})
# 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"}
# # @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) |