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import yfinance as yf |
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import pandas as pd |
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import requests |
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import warnings |
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warnings.simplefilter(action='ignore', category=FutureWarning) |
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warnings.filterwarnings('ignore') |
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df_logo = pd.read_csv("https://raw.githubusercontent.com/jarvisx17/nifty500/main/Stocks.csv") |
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def calculate_profit(ltp, share, entry): |
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tgt1 = entry + (0.02 * entry) |
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tgt2 = entry + (0.04 * entry) |
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if ltp > tgt2: |
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profit = round((share / 3 * (tgt1-entry)) + (share / 3 * (tgt2-entry)) + (share / 3 * (ltp-entry)), 2) |
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elif ltp > tgt1 and ltp < tgt2: |
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profit = round((share / 3 * (tgt1-entry)) + ((share / 3) * 2 * (ltp-entry)), 2) |
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elif ltp > tgt1: |
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profit = round(share * (ltp-entry), 2) |
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else: |
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profit = round(share * (ltp-entry), 2) |
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return profit |
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def info(ticker): |
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data = df_logo[df_logo['Symbol'] == ticker] |
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logo = data.logo.values[0] |
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Industry = data.Industry.values[0] |
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return logo, Industry |
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def calculate_percentage_loss(buying_price, ltp): |
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percentage_loss = ((ltp - buying_price) / buying_price) * 100 |
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return f"{percentage_loss:.2f}%" |
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def latestprice(ticker): |
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ticker = ticker.split(".")[0] |
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url = f'https://groww.in/v1/api/stocks_data/v1/accord_points/exchange/NSE/segment/CASH/latest_prices_ohlc/{ticker}' |
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headers = { |
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'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' |
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} |
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response = requests.get(url, headers=headers) |
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if response.status_code == 200: |
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data = response.json() |
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return float(data['ltp']) |
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else: |
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return 0.0 |
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def process_dataframe(df): |
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def get_rsi(close, lookback): |
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ret = close.diff() |
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up = [] |
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down = [] |
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for i in range(len(ret)): |
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if ret[i] < 0: |
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up.append(0) |
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down.append(ret[i]) |
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else: |
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up.append(ret[i]) |
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down.append(0) |
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up_series = pd.Series(up) |
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down_series = pd.Series(down).abs() |
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up_ewm = up_series.ewm(com=lookback - 1, adjust=False).mean() |
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down_ewm = down_series.ewm(com=lookback - 1, adjust=False).mean() |
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rs = up_ewm / down_ewm |
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rsi = 100 - (100 / (1 + rs)) |
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rsi_df = pd.DataFrame(rsi).rename(columns={0: 'RSI'}).set_index(close.index) |
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rsi_df = rsi_df.dropna() |
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return rsi_df[3:] |
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df['RSI'] = get_rsi(df['Close'], 14) |
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df['SMA20'] = df['Close'].rolling(window=20).mean() |
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df.drop(['Adj Close'], axis=1, inplace=True) |
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df = df.dropna() |
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return df |
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def fin_data(ticker, startdate): |
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ltp = latestprice(ticker) |
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df=yf.download(ticker, period="36mo", progress=False) |
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df = process_dataframe(df) |
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df.reset_index(inplace=True) |
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df['Prev_RSI'] = df['RSI'].shift(1).round(2) |
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df = df.dropna() |
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df.reset_index(drop=True, inplace=True) |
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df[['Open', 'High', 'Low', 'Close',"RSI","SMA20"]] = df[['Open', 'High', 'Low', 'Close',"RSI", "SMA20"]].round(2) |
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df = df[200:] |
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df['Target1'] = df['High'] + (df['High'] * 0.02) |
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df['Target1'] = df['Target1'].round(2) |
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df['Target2'] = df['High'] + (df['High'] * 0.04) |
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df['Target2'] = df['Target2'].round(2) |
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df['Target3'] = "will announced" |
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df['SL'] = df['Low'] |
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df['LTP'] = ltp |
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date_index = df.loc[df['Date'] == startdate].index[0] |
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df = df.loc[date_index-1:] |
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df['Date'] = pd.to_datetime(df['Date']) |
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df.reset_index(drop=True,inplace=True) |
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return df |
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def eqt(ticker, startdate, share_qty = 90): |
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df = fin_data(ticker, startdate) |
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logo, Industry = info(ticker) |
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entry = False |
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trading = False |
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shares_held = 0 |
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buy_price = 0 |
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target1 = False |
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target2 = False |
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target3 = False |
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tgt1 = 0 |
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tgt2 = 0 |
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tgt3 = 0 |
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total_profit = 0 |
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profits = [] |
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stop_loss = 0 |
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capital_list = [] |
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data = {} |
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totalshares = share_qty |
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ltp = latestprice(ticker) |
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for i in range(1, len(df)-1): |
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try: |
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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: |
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buy_price = df.at[i, 'High'] |
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stop_loss = df.at[i, 'Low'] |
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capital = buy_price * share_qty |
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capital_list.append(capital) |
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shares_held = share_qty |
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entdate = df.at[i+1, 'Date'] |
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entry_info = {"Date": pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Note": "Entry Successful", "SL": stop_loss} |
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entryStock_info = df.iloc[i: i+1].reset_index(drop=True).to_dict(orient='records')[0] |
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entryStock_info['Date'] = str(pd.to_datetime(df.at[i, 'Date']).strftime('%d-%m-%Y')) |
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data['StockInfo'] = {} |
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data['StockInfo']['Stock'] = {} |
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data['StockInfo']['Stock']['Name'] = ticker |
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data['StockInfo']['Stock']['Industry'] = Industry |
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data['StockInfo']['Stock']['Logo'] = logo |
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data['StockInfo']['Stock']['Status'] = "Active" |
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data['StockInfo']['Stock']['Levels'] = "Entry" |
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data['StockInfo']['Stock']['Values'] = entryStock_info |
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buying_price = entryStock_info['High'] |
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ltp = entryStock_info['LTP'] |
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data['StockInfo']['Stock']['Values']['Share QTY'] = share_qty |
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data['StockInfo']['Stock']['Values']['Invested Amount'] = (share_qty * buy_price).round(2) |
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data['StockInfo']['Stock']['Values']['Percentage'] = calculate_percentage_loss(buying_price, ltp) |
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data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price) |
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data['StockInfo']['Entry'] = entry_info |
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entry = True |
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trading = True |
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if trading and not target1: |
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if (df.at[i + 1, 'High'] - buy_price) >= 0.02 * buy_price: |
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stop_loss = buy_price |
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target1 = True |
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tgt1 = 0.02 * buy_price * (share_qty / 3) |
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shares_held -= (share_qty / 3) |
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total_profit = round(tgt1,2) |
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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} |
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data['StockInfo']['TGT1'] = target1_info |
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data['StockInfo']['Stock']['Values']['SL'] = stop_loss |
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data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT1" |
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data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price) |
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data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...." |
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if trading and target1 and not target2: |
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if (df.at[i + 1, 'High'] - buy_price) >= 0.04 * buy_price: |
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target2 = True |
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tgt2 = 0.04 * buy_price * (share_qty / 3) |
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total_profit += round(tgt2,2) |
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shares_held -= (share_qty / 3) |
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data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT2" |
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data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price) |
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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} |
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data['StockInfo']['TGT2'] = target2_info |
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data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...." |
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if trading and target2 and not target3: |
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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']): |
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stop_loss = df.at[i + 1, 'Low'] |
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data['StockInfo']['Stock']['Values']['SL'] = stop_loss |
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if df.at[i + 2, 'Low'] < stop_loss: |
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target3 = True |
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tgt3 = stop_loss * (share_qty / 3) |
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shares_held -= (share_qty / 3) |
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total_profit += round(tgt3,2) |
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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} |
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data['StockInfo']['Stock']['Values']['Target3'] = tgt3 |
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data['StockInfo']['TGT3'] = target3_info |
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data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" TGT3" |
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data['StockInfo']['Stock']['Values']['Total P/L'] = calculate_profit(ltp, totalshares, buy_price) |
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data['StockInfo']['TotalProfit'] = {} |
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data['StockInfo']['TotalProfit']['Profit'] = total_profit |
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data['StockInfo']['Entry']['Trade Status'] = "Trade closed successfully...." |
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data['StockInfo']['TotalProfit']['Trade Status'] = "Trade closed successfully...." |
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break |
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if (df.at[i + 1, 'Low'] < stop_loss and trading and entdate != df.at[i + 1, 'Date']) or stop_loss > ltp: |
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profit_loss = (shares_held * stop_loss) - (shares_held * buy_price) |
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total_profit += profit_loss |
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profits.append(total_profit) |
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shares_held = 0 |
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if data['StockInfo']['Stock']['Values']['Target3'] == "will announced" : |
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data['StockInfo']['Stock']['Values']['Target3'] = "-" |
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data['StockInfo']['Stock']['Status'] = "Closed" |
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data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" SL" |
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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"} |
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data['StockInfo']['SL'] = stoploss_info |
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data['StockInfo']['TotalProfit'] = {} |
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data['StockInfo']['TotalProfit']['Profit'] = round(total_profit, 2) |
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data['StockInfo']['Stock']['Values']['Total P/L'] = round(total_profit, 2) |
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data['StockInfo']['Entry']['Trade Status'] = "Trade closed successfully...." |
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data['StockInfo']['TotalProfit']['Trade Status'] = "Trade closed successfully...." |
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buy_price = 0 |
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entry = False |
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trading = False |
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target1 = target2 = target3 = False |
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tgt1 = tgt2 = tgt3 = 0 |
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total_profit = 0 |
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break |
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except IndexError: |
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continue |
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if capital_list and profits: |
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return data |
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else: |
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if data: |
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return data |
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else: |
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data['StockInfo'] = {} |
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data['StockInfo']['Stock'] = {} |
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data['StockInfo']['Stock']['Name'] = ticker |
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data['StockInfo']['Stock']['Industry'] = Industry |
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data['StockInfo']['Stock']['Logo'] = logo |
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data['StockInfo']['Stock']['Status'] = "Waiting for entry" |
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entryStock_info = df.iloc[1: 2].reset_index(drop=True).to_dict(orient='records')[0] |
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entryStock_info['Date'] = str(pd.to_datetime(df.at[1, 'Date']).strftime('%d-%m-%Y')) |
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data['StockInfo']['Stock']['Values'] = entryStock_info |
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data['StockInfo']['Stock']['Values']['Target3'] = "-" |
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data['StockInfo']['Info'] = "Don't buy stock right now...." |
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return data |
