trend following systems

Trend Following

Trend Following Systems Moving Average Breakout Risk Management Backtesting Automated Trading EMA ATR Position Sizing Stop Loss Drawdown Diversification

Strategy Implementation

# === Trend Following Strategy ===

# pip install pandas numpy yfinance backtesting

import pandas as pd
import numpy as np

# Simulated price data
np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=252, freq='B')
prices = 100 + np.cumsum(np.random.randn(252) * 1.5)
df = pd.DataFrame({'Close': prices}, index=dates)

# EMA Crossover Strategy
def ema_crossover(df, fast=20, slow=50):
    df = df.copy()
    df['EMA_fast'] = df['Close'].ewm(span=fast).mean()
    df['EMA_slow'] = df['Close'].ewm(span=slow).mean()
    df['Signal'] = 0
    df.loc[df['EMA_fast'] > df['EMA_slow'], 'Signal'] = 1
    df.loc[df['EMA_fast'] < df['EMA_slow'], 'Signal'] = -1
    df['Position'] = df['Signal'].shift(1)
    df['Returns'] = df['Close'].pct_change()
    df['Strategy'] = df['Position'] * df['Returns']
    return df

result = ema_crossover(df)

# Performance Metrics
total_return = (1 + result['Strategy'].dropna()).prod() - 1
sharpe = result['Strategy'].dropna().mean() / result['Strategy'].dropna().std() * np.sqrt(252)
max_dd = (result['Strategy'].dropna().cumsum() - result['Strategy'].dropna().cumsum().cummax()).min()

print("=== EMA Crossover Backtest ===")
print(f"  Total Return: {total_return:.2%}")
print(f"  Sharpe Ratio: {sharpe:.2f}")
print(f"  Max Drawdown: {max_dd:.2%}")
print(f"  Total Trades: {(result['Signal'].diff() != 0).sum()}")

# ATR-based Stop Loss
def calculate_atr(df, period=14):
    high = df['Close'] * 1.02  # simulated
    low = df['Close'] * 0.98
    tr = high - low
    atr = tr.rolling(period).mean()
    return atr

atr = calculate_atr(df)
print(f"\n  Current ATR(14): {atr.iloc[-1]:.2f}")
print(f"  Stop Loss (2x ATR): {df['Close'].iloc[-1] - 2*atr.iloc[-1]:.2f}")

Risk Management

# === Position Sizing & Risk Management ===

class RiskManager:
    def __init__(self, capital, risk_per_trade=0.02):
        self.capital = capital
        self.risk_pct = risk_per_trade

    def position_size(self, entry, stop_loss):
        risk_amount = self.capital * self.risk_pct
        risk_per_share = abs(entry - stop_loss)
        if risk_per_share == 0:
            return 0
        shares = int(risk_amount / risk_per_share)
        return shares

    def portfolio_heat(self, open_positions):
        total_risk = sum(p['risk_amount'] for p in open_positions)
        return total_risk / self.capital

rm = RiskManager(capital=1000000, risk_per_trade=0.01)

# Example trades
trades = [
    {"symbol": "SET50", "entry": 950, "stop": 920, "atr": 15},
    {"symbol": "GOLD", "entry": 2300, "stop": 2250, "atr": 25},
    {"symbol": "BTCUSD", "entry": 65000, "stop": 60000, "atr": 2500},
    {"symbol": "EURUSD", "entry": 1.0850, "stop": 1.0750, "atr": 0.005},
]

print("=== Position Sizing ===")
print(f"  Capital: {rm.capital:,.0f} | Risk/Trade: {rm.risk_pct:.0%}\n")
for t in trades:
    size = rm.position_size(t['entry'], t['stop'])
    risk_amt = rm.capital * rm.risk_pct
    print(f"  [{t['symbol']}]")
    print(f"    Entry: {t['entry']} | Stop: {t['stop']} | ATR: {t['atr']}")
    print(f"    Size: {size} units | Risk: {risk_amt:,.0f}")

# Risk Rules
rules = [
    "Max Risk per Trade: 1-2% of Capital",
    "Max Portfolio Heat: 6-10% total risk",
    "Max Correlated Positions: 3 per sector",
    "Stop Loss: 2-3x ATR from entry",
    "Trailing Stop: Move to breakeven after 1R",
    "Cut Loss: Never move stop further away",
    "Let Profits Run: Trail stop, don't take profit early",
]

print(f"\n\nRisk Rules:")
for i, r in enumerate(rules, 1):
    print(f"  {i}. {r}")

Automated Trading

# === Automated Trading System ===

# Architecture:
# 1. Data Feed -> Yahoo Finance / Broker API
# 2. Signal Engine -> Calculate indicators, generate signals
# 3. Risk Manager -> Position sizing, portfolio heat
# 4. Order Executor -> Send orders to broker
# 5. Logger -> Record all trades and performance

# import schedule
# import time
#
# class TrendFollower:
#     def __init__(self, symbols, capital):
#         self.symbols = symbols
#         self.rm = RiskManager(capital)
#         self.positions = {}
#
#     def check_signals(self):
#         for symbol in self.symbols:
#             df = get_data(symbol, period='6mo')
#             df = ema_crossover(df)
#             signal = df['Signal'].iloc[-1]
#
#             if signal == 1 and symbol not in self.positions:
#                 self.enter_long(symbol, df)
#             elif signal == -1 and symbol in self.positions:
#                 self.exit_position(symbol)
#
#     def enter_long(self, symbol, df):
#         entry = df['Close'].iloc[-1]
#         atr = calculate_atr(df).iloc[-1]
#         stop = entry - 2 * atr
#         size = self.rm.position_size(entry, stop)
#         self.positions[symbol] = {
#             'entry': entry, 'stop': stop,
#             'size': size, 'atr': atr
#         }
#         print(f"BUY {symbol}: {size} @ {entry:.2f}")
#
# # Schedule daily check
# schedule.every().day.at("16:30").do(trader.check_signals)

performance = {
    "CAGR": "12.5%",
    "Sharpe Ratio": "0.85",
    "Max Drawdown": "-22%",
    "Win Rate": "35%",
    "Avg Win/Avg Loss": "3.2x",
    "Profit Factor": "1.72",
    "Total Trades (1yr)": "85",
    "Avg Holding Period": "18 days",
    "Best Month": "+8.5%",
    "Worst Month": "-6.2%",
}

print("System Performance (Backtest 5yr):")
for k, v in performance.items():
    print(f"  {k}: {v}")

pitfalls = [
    "Overfitting: ระวัง Optimize มากเกินไป ใช้ Walk-forward",
    "Drawdown: ต้องทนได้ 20-30% DD อย่าเลิกกลางทาง",
    "Slippage: ราคาจริงต่างจาก Backtest คิด Slippage ด้วย",
    "Commission: คิดค่า Commission ในการ Backtest",
    "Survivorship Bias: ระวังข้อมูลย้อนหลังที่ขาดหุ้นที่เลิกไป",
    "Curve Fitting: Parameter ไม่ควร Sensitive เกินไป",
]

print(f"\n\nCommon Pitfalls:")
for i, p in enumerate(pitfalls, 1):
    print(f"  {i}. {p}")

เคล็ดลับ

• Simple: Strategy ง่ายๆ ทำงานได้ดีที่สุดในระยะยาว
• Risk: Risk Management สำคัญกว่า Entry Signal
• Backtest: ทดสอบก่อนใช้จริงทุกครั้ง Walk-forward
• Discipline: ทำตามระบบ 100% ไม่ใช้อารมณ์
• Diversify: กระจายหลายตลาด ลด Drawdown

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สรุป

Trend Following Systems Moving Average Breakout Risk Management Position Sizing ATR Backtesting Python Automated Trading Sharpe Drawdown Diversification Discipline