This is a very simple yet effective MACD momentum trading strategy for GDXJ (Jr. Gold Miners ETF). Apply the MACD indicator with the classic 12/26/9 setting on GDXJ at the 30-minute interval. Go long when MACD issues a buy signal and go short when MACD issues a sell signal. Take profit = +25%, stop loss = -5%. Got pretty decent results off such a basic strategy already so I was wondering what my fellow Q wizards can do to spice this thing up even more!

Shout out to Mohammed Khalfan for porting my codes to Python!

For collaboration request or general consultation, please email: [email protected] and [email protected]

Clone Algorithm

1038

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Backtest from
to
with
initial capital

Cumulative performance:

Algorithm
Benchmark

Custom data:

Total Returns

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Alpha

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Beta

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Sharpe

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Sortino

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Max Drawdown

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Benchmark Returns

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Volatility

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Returns | 1 Month | 3 Month | 6 Month | 12 Month |

Alpha | 1 Month | 3 Month | 6 Month | 12 Month |

Beta | 1 Month | 3 Month | 6 Month | 12 Month |

Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |

Sortino | 1 Month | 3 Month | 6 Month | 12 Month |

Volatility | 1 Month | 3 Month | 6 Month | 12 Month |

Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |

#Author: Kory Hoang #Developer: Mohammed Khalfan #Email: [email protected] #Imports from quantopian.pipeline.data.builtin import USEquityPricing import statsmodels.api as sm import quantopian.pipeline.data import numpy as np import pandas as pd import talib import scipy def initialize(context): set_benchmark(symbol('GDXJ')) context.GDXJ = symbol('GDXJ') context.allocation = 1 context.TakeProfitPct = 0.25 context.StopLossPct = 0.05 context.BuyPrice = 0 context.bought = False context.sold = False # 30 min scheduler for x in [0,1,2,3,4,5]: schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=x, minutes=29)) schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=x, minutes=59)) schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_close()) schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close()) #Set commission and slippage set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.0)) set_slippage(slippage.FixedSlippage(spread=0.01)) def my_rebalance(context,data): GDXJ_prices = data.history(context.GDXJ, "price", 10000, "1m").resample('30T', closed='right', label='right') .last().dropna() #GDXJ_prices = data.history(context.GDXJ, "price", 100, "1d") ema12 = talib.EMA(GDXJ_prices,12) ema26 = talib.EMA(GDXJ_prices,26) macd = ema12 - ema26 signal = talib.EMA(macd,9) record(SIG=macd[-1] - signal[-1]) record(MACD=macd[-1]) if macd[-2] < signal[-2] and macd[-1] >= signal[-1] and not context.bought: set_fixed_stop_long(context, data) order_target_percent(context.GDXJ, context.allocation) context.bought = True context.sold = False if macd[-1] < signal[-1] and not context.sold: set_fixed_stop_short(context, data) order_target_percent(context.GDXJ, -context.allocation) context.bought = False context.sold = True def my_record_vars(context, data): leverage = context.account.leverage #record(leverage=leverage) def set_fixed_stop_long(context, data): #Only call this once when the stock is bought if data.can_trade(context.GDXJ): price = data.current(context.GDXJ, 'price') context.BuyPrice = price context.SellLossPrice= price - (context.StopLossPct * price) context.SellProfitPrice= (price * context.TakeProfitPct) + price def set_fixed_stop_short(context, data): #Only call this once when the stock is bought if data.can_trade(context.GDXJ): price = data.current(context.GDXJ, 'price') context.BuyPrice = price context.SellLossPrice= price + (context.StopLossPct * price) context.SellProfitPrice= price - (price * context.TakeProfitPct) def handle_data(context, data): #If we have a position check sell conditions if context.portfolio.positions[context.GDXJ].amount != 0 and context.bought: price = data.current(context.GDXJ, 'price') if price > context.SellProfitPrice and len(get_open_orders()) == 0: order_target_percent(context.GDXJ, 0) context.bought = False if price < context.SellLossPrice and len(get_open_orders()) == 0: order_target_percent(context.GDXJ, 0) context.bought = False if context.portfolio.positions[context.GDXJ].amount != 0 and context.sold: price = data.current(context.GDXJ, 'price') if price < context.SellProfitPrice and len(get_open_orders()) == 0: order_target_percent(context.GDXJ, 0) context.sold = False if price > context.SellLossPrice and len(get_open_orders()) == 0: order_target_percent(context.GDXJ, 0) context.sold = False