AmiBroker Trading Strategy Backtest
Budget: $250 – $750 USD
I need an experienced AmiBroker user/backtester to run a backtest of a frozen multi-sleeve trading strategy exactly as written. This is not a strategy design or optimization project. The rules are already finalized. I need accurate implementation, clear assumptions, and auditable results.
The strategy includes:
• a weekly breakout/ranking stock sleeve using S&P 400 stocks, with a monthly SPY 10-month SMA regime filter
• a daily SPXL mean reversion sleeve
• a monthly ETF trend/defensive allocation sleeve using SPY, QQQ, VEA, IEF, DBMF, GLD, and SGOV
Key requirements:
• use adjusted-price data for signal calculations
• respect next-session open execution after signals
• correctly handle daily, weekly, and monthly timing
• no optimization, no discretionary changes, and no reinterpretation of rules
• clearly state all assumptions, especially any limitations involving historical S&P 400 membership
• provide results that can be audited
Required deliverables:
• assumptions memo
• equity curve
• annual returns
• max drawdown and summary statistics including CAGR, Sortino, and Ulcer Index
• sample audit tables showing signals, rankings, selected positions, and executed trades
• brief explanation of how the backtest handled timing, entries, exits, and universe assumptions
Important:
I am specifically looking for someone with real AmiBroker portfolio backtesting experience, not just indicator scripting. You should understand lookahead bias, delayed execution, ranking systems, and portfolio-level trade logic.
I would like to begin with a small paid pilot milestone before awarding the full project.
If you apply, please briefly explain:
1. your experience running AmiBroker portfolio backtests
2. your experience with delayed execution / next-bar execution
3. whether you have backtested ranking / rotational / breakout systems before
4. how you would handle the S&P 400 historical universe issue in this project
The strategy includes:
• a weekly breakout/ranking stock sleeve using S&P 400 stocks, with a monthly SPY 10-month SMA regime filter
• a daily SPXL mean reversion sleeve
• a monthly ETF trend/defensive allocation sleeve using SPY, QQQ, VEA, IEF, DBMF, GLD, and SGOV
Key requirements:
• use adjusted-price data for signal calculations
• respect next-session open execution after signals
• correctly handle daily, weekly, and monthly timing
• no optimization, no discretionary changes, and no reinterpretation of rules
• clearly state all assumptions, especially any limitations involving historical S&P 400 membership
• provide results that can be audited
Required deliverables:
• assumptions memo
• equity curve
• annual returns
• max drawdown and summary statistics including CAGR, Sortino, and Ulcer Index
• sample audit tables showing signals, rankings, selected positions, and executed trades
• brief explanation of how the backtest handled timing, entries, exits, and universe assumptions
Important:
I am specifically looking for someone with real AmiBroker portfolio backtesting experience, not just indicator scripting. You should understand lookahead bias, delayed execution, ranking systems, and portfolio-level trade logic.
I would like to begin with a small paid pilot milestone before awarding the full project.
If you apply, please briefly explain:
1. your experience running AmiBroker portfolio backtests
2. your experience with delayed execution / next-bar execution
3. whether you have backtested ranking / rotational / breakout systems before
4. how you would handle the S&P 400 historical universe issue in this project