Technical analysis by ProjectSyndicate about Symbol PAXG on 6/14/2025

________________________________________🏆 Intraday Gold Trading System with Neural Networks: Step-by-Step Practical Guide________________________________________📌 Step 1: Overview and GoalThe goal is to build a neural network system to predict intraday short-term gold price movements—typically forecasting the next 15 to 30 minutes.________________________________________📈 Step 2: Choosing Indicators (TradingView Equivalents)Key indicators for intraday gold trading:•📊 Moving Averages (EMA, SMA)•📏 Relative Strength Index (RSI)•🌀 Moving Average Convergence Divergence (MACD)•📉 Bollinger Bands•📦 Volume Weighted Average Price (VWAP)•⚡ Average True Range (ATR)________________________________________🗃 Step 3: Data Acquisition (Vectors and Matrices)Use Python's yfinance to fetch intraday gold data:import yfinance as yfimport pandas as pddata = yf.download('GC=F', period='30d', interval='15m')________________________________________🔧 Step 4: Technical Indicator CalculationUse Python’s pandas_ta library to generate all required indicators:import pandas_ta as tadata['EMA_20'] = ta.ema(data['Close'], length=20)data['EMA_50'] = ta.ema(data['Close'], length=50)data['RSI'] = ta.rsi(data['Close'], length=14)macd = ta.macd(data['Close'])data['MACD'] = macd['MACD_12_26_9']data['MACD_signal'] = macd['MACDs_12_26_9']bbands = ta.bbands(data['Close'], length=20)data['BBL'] = bbands['BBL_20_2.0']data['BBM'] = bbands['BBM_20_2.0']data['BBU'] = bbands['BBU_20_2.0']data['ATR'] = ta.atr(data['High'], data['Low'], data['Close'], length=14)data.dropna(inplace=True)________________________________________🧹 Step 5: Data Preprocessing and Matrix CreationStandardize your features and shape data for neural networks:from sklearn.preprocessing import StandardScalerimport numpy as npfeatures = ['EMA_20', 'EMA_50', 'RSI', 'MACD', 'MACD_signal', 'BBL', 'BBM', 'BBU', 'ATR']scaler = StandardScaler()data_scaled = scaler.fit_transform(data[features])def create_matrix(data_scaled, window_size=10): X, y = [], [] for i in range(len(data_scaled) - window_size - 1): X.append(data_scaled[i:i+window_size]) y.append(data['Close'].iloc[i+window_size+1]) return np.array(X), np.array(y)X, y = create_matrix(data_scaled, window_size=10)________________________________________🤖 Step 6: Neural Network Construction with TensorFlowUse LSTM neural networks for sequential, time-series prediction:import tensorflow as tffrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import LSTM, Dense, Dropoutmodel = Sequential([ LSTM(64, activation='relu', return_sequences=True, input_shape=(X.shape[1], X.shape[2])), Dropout(0.2), LSTM(32, activation='relu'), Dense(1)])model.compile(optimizer='adam', loss='mse')________________________________________🎯 Step 7: Training the Neural Networkhistory = model.fit(X, y, epochs=50, batch_size=32, validation_split=0.2)________________________________________📊 Step 8: Evaluating Model PerformanceVisualize actual vs. predicted prices:import matplotlib.pyplot as pltpredictions = model.predict(X)plt.plot(y, label='Actual Price')plt.plot(predictions, label='Predicted Price')plt.xlabel('Time Steps')plt.ylabel('Gold Price')plt.legend()plt.show()________________________________________🚦 Step 9: Developing a Trading StrategyTranslate predictions into trading signals:def trade_logic(predicted, current, threshold=0.3): diff = predicted - current if diff > threshold: return "Buy" elif diff < -threshold: return "Sell" else: return "Hold"latest_data = X[-1].reshape(1, X.shape[1], X.shape[2])predicted_price = model.predict(latest_data)[0][0]current_price = data['Close'].iloc[-1]decision = trade_logic(predicted_price, current_price)print("Trading Decision:", decision)________________________________________⚙️ Step 10: Real-Time DeploymentAutomate the model for live trading via broker APIs (pseudocode):while market_open: live_data = fetch_live_gold_data() live_data_processed = preprocess(live_data) prediction = model.predict(live_data_processed) decision = trade_logic(prediction, live_data['Close']) execute_order(decision)________________________________________📅 Step 11: BacktestingUse frameworks like Backtrader or Zipline to validate your strategy:import backtrader as btclass NNStrategy(bt.Strategy): def next(self): if self.data.predicted[0] > self.data.close[0] + threshold: self.buy() elif self.data.predicted[0] < self.data.close[0] - threshold: self.sell()cerebro = bt.Cerebro()cerebro.addstrategy(NNStrategy)# Add data feeds and run cerebrocerebro.run()________________________________________🔍 Practical Use-Cases•⚡ Momentum Trading: EMA crossovers, validated by neural network.•🔄 Mean Reversion: Trade at Bollinger Band extremes, validated with neural network predictions.•🌩️ Volatility-based: Use ATR plus neural net for optimal entry/exit timing.________________________________________🛠 Additional Recommendations•Frameworks: TensorFlow/Keras, PyTorch, scikit-learn•Real-time monitoring and risk management are crucial—use volatility indicators!________________________________________📚 Final ThoughtsThis practical guide arms you to build, deploy, and manage a neural network-based intraday gold trading system—from data acquisition through backtesting—ensuring you have the tools for robust, data-driven, and risk-managed trading strategies.________________________________________Top 10 Small-Cap Biotechs with Upcoming Catalysts for 2025GOLD H4 Update: Bulls will target 3600/3750 USD Market UpdateUpdate of the Bullish/Bearish Catalysts for Gold pricesTOP 10 Stats of the Current Gold Bull Market in 2025 and OutlookGBPUSD H1 compression BUY/HOLD TP1 +100 TP2 +200 pips low riskGold Market Update: Bulls Will target 3750 USD after 3500 USD