impressivePers2252

//version=5strategy("استراتژی ترکیبی RSI + EMA", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)/// محاسبه EMA هاema9 = ta.ema(close, 9)ema21 = ta.ema(close, 21)ema200 = ta.ema(close, 200)/// محاسبه RSIrsi = ta.rsi(close, 14)/// شروط خریدbuySignal = (rsi < 30) and (ema9 > ema21) and (close > ema200)/// شروط فروشsellSignal = (rsi > 70) and (ema9 < ema21) and (close < ema200)/// ورود به معاملهif (buySignal) strategy.entry("Buy", strategy.long)if (sellSignal) strategy.close("Buy")/// نمایش اندیکاتورها روی چارتplot(ema9, color=color.orange, title="EMA 9")plot(ema21, color=color.blue, title="EMA 21")plot(ema200, color=color.red, title="EMA 200")/// سیگنال‌های بصری (فلش)plotshape(buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="Buy")plotshape(sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell")

//// Author LonesomeTheBlue////version=3study("Sell / Buy Rates", overlay = false, precision = 0)prd = input(title="Period", defval = 34, minval = 1)tw = high - max(open, close) bw = min(open, close) - low body = abs(close - open) _rate(cond) => 0.5 * (tw + bw + (cond ? 2 * body : 0)) / (tw + bw + body) volup = volume * _rate(open <= close) voldown = volume * _rate(open >= close)rate = linreg(volup - voldown, prd, 0)col = rate > 0 ? (falling(rate, 5) ? green : lime) : rate < 0 ? rising(rate, 5) ? maroon : red : naplot(rate, color = col, style = columns)

import pandas as pdimport matplotlib.pyplot as plt# Load data# For this example, we'll use a CSV file with 'Date' and 'Close' columns.# Adjust the file path as needed.data = pd.read_csv('path/to/your/data.csv')# Convert 'Date' column to datetimedata['Date'] = pd.to_datetime(data['Date'])# Set 'Date' column as the indexdata.set_index('Date', inplace=True)# Define the short-term and long-term moving averagesshort_window = 50long_window = 200# Calculate the short-term and long-term moving averagesdata['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()# Create signalsdata['Signal'] = 0data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)# Generate trading ordersdata['Position'] = data['Signal'].diff()# Plotting the resultsplt.figure(figsize=(14, 7))# Plot the closing price and moving averagesplt.plot(data['Close'], label='Close Price', alpha=0.5)plt.plot(data['Short_MA'], label=f'Short {short_window}-Day MA', alpha=0.75)plt.plot(data['Long_MA'], label=f'Long {long_window}-Day MA', alpha=0.75)# Plot buy signalsplt.plot(data[data['Position'] == 1].index, data['Short_MA'][data['Position'] == 1], '^', markersize=10, color='g', lw=0, label='Buy Signal')# Plot sell signalsplt.plot(data[data['Position'] == -1].index, data['Short_MA'][data['Position'] == -1], 'v', markersize=10, color='r', lw=0, label='Sell Signal')# Add labels and legendplt.title('Moving Average Crossover Strategy')plt.xlabel('Date')plt.ylabel('Price')plt.legend()plt.show()