DEV Community: Nam

Based on "Building Winning Algorithmic Trading Systems" by Kevin J. Davey, here is the complete strategy development and testing process along with a checklist of key performance metrics

Nam — Wed, 28 Jan 2026 10:07:05 +0000

Strategy Testing Checklist

Nam ・ Jan 28

#discuss #learning #trading #backtest

Strategy Testing Checklist

Nam — Wed, 28 Jan 2026 10:02:10 +0000

Step 1: Simple backtest

Passing criteria: At least 70% of parameter iterations should be profitable.

Total net profit: >20% of initial balance
Profit factor: >1
Number of Trades: 30 - 100 trades
Avg. Trade net profit: >= 1% balance
Maximum drawdown: <20% of initial balance
Slippage & commission must be included

Step 2: Monkey testing

Passing criteria: Your strategy should outperform >90% of random monkey runs.

>90% better than random

Step 3: Walk-Forward Analysis

Passing criteria: At least 70% of parameter iterations should be profitable.

Equity Curve Slope: Must be positive
Curve Stability: No massive flat periods or "cliffs" where performance collapses
Return/DD Ratio (WFA): > 2.0
Total net profit: >30% of initial balance
Profit factor: >1.5
Number of Trades: 100 - 500 trades
Avg. Trade net profit: >= 1% balance
Maximum drawdown: < 30% of initial balance
Slippage & commission must be included

Step 4: Monte Carlo Simulation

Risk of Ruin: < 10%
Median Max Drawdown: < 40%
Return/ Drawdown ratio: > 2.0
Median Annual Return: > 40%
Step 5: Strategy Incubation
Survived for 1 - 2 months
Profit factor: >1.5
Maximum drawdown: < 20% of initial balance
Maximum daily drawdown: < 5% of initial balance
Maximum weekly drawdown: < 10% of initial balance

Step 6: Live Trading and Ongoing Monitoring

Set position size and risk based on Monte Carlo results.
Regularly repeat Monkey Tests and Walk-Forward Analysis to ensure the edge persists.

Most algorithmic trading systems fail even with great backtests. Perfect equity curves hide one thing: overfitting 👉 Read my article to learn the 3-stage framework professionals use to avoid overfitting and survive live markets.

Nam — Mon, 26 Jan 2026 04:13:41 +0000

How to Actually Build an Algorithmic Trading System That Works

Nam ・ Jan 26

#backtesting #algotrading #tutorial #learning

How to Actually Build an Algorithmic Trading System That Works

Nam — Mon, 26 Jan 2026 03:59:20 +0000

(Hint: It's not about finding the perfect strategy, it’s about the process)

Let me save you 10 years of trial and error: your algorithmic trading system will fail. Not because your idea is bad, but because your process is broken.

Here's the exact 3-stage framework that separates profitable systematic traders from perpetual backtesters.

The Broken Process Most Traders Use

You've probably done this:

Get excited about an indicator or pattern
Backtest it on 10 years of data
Optimize until the equity curve looks perfect
Go live
Watch it fail
Blame the markets

The problem isn't your idea. It's that you curve-fitted to every piece of historical data you had. Your beautiful backtest was an illusion of hindsight.

The Professional 3-Stage Framework

Stage 1: The Filter Backtest (Kill 80% of Ideas in 24 Hours)

Purpose: Quick elimination, not validation.

The brutal numbers:

Test on only 1-2 years of data (not your whole dataset)
Run 100-200 parameter combinations
Require >70% of iterations to be profitable
Must beat >90% of random "monkey" strategies
Minimum $5,000/year/contract profit (before costs)

Why this works: You're not looking for the best system. You're filtering out the worst ideas. If your strategy can't pass these simple tests on a small data sample, it has zero chance in live markets.

Action: This week, take your best idea. Test it. If it doesn't pass ALL these criteria, discard it. You just saved yourself 40 hours of wasted effort.

Stage 2: The Walk-Forward Backtest (Where Real Validation Happens)

Purpose: Pressure-test across different market environments.

The exact setup:

Use 5-10 years of total data
Optimize on 3-5 years (in-sample)
Test on the next 1 year (out-sample)
Slide forward and repeat
Your out-sample performance should be ≥50% of your in-sample

Non-negotiable metrics:

Total Net Profit: ≥ $10,000/year/contract
Profit Factor: ≥ 1.5 (ideally > 2.0)
Average Trade: ≥ $50 after commissions
Maximum Drawdown: ≤ 50% of total profit
Risk of Ruin: < 10% (Monte Carlo)

The reality check: Most strategies that pass Stage 1 will FAIL Stage 2. That's the point! You're pressure-testing your system against reality, not optimizing it to historical perfection.

Stage 3: Incubation (The Step Everyone Skips and Regrets)

Purpose: Verify with real-time data and psychological fit.

The rule: Paper trade for at least 3-6 months before risking real money.

Why this is non-negotiable:

Removes emotional attachment to your "brilliant" creation
Catches fill/slippage issues you missed in backtests
Let's see if you can actually stomach trading it
Most strategies that passed Stage 2 show cracks here

The psychological test: If you can't watch your strategy for 3 months without itching to change it, you'll never hold it through a 6-month drawdown.

The Psychological Shift Required

Stop thinking: "How can I make this backtest look better?"
Start thinking: "How can I prove this won't lose money?"

Your goal isn't impressive historical results. Your goal is predictable future performance.

The most successful algorithmic traders I know aren't geniuses. They're just brutally disciplined about their process. They kill their darlings without hesitation. They'd rather have no system than a questionable one.

The Uncomfortable Truth
If you're reading this and thinking:

"This is too much work"
"My system is different"
"I'll just skip to the good part"

...then algorithmic trading isn't for you. This is a profession of discipline, not shortcuts.

But if you're willing to:

Test 100 ideas to find 1 gem
Spend 500 hours before your first live trade
Follow exacting criteria without exception

...then you might have what it takes.

Final Thought

The markets don't care how brilliant your algorithm is. They only care if it's:

Statistically valid (not curve-fitted)
Properly tested (across multiple regimes)
Psychologically tradeable (you can stick with it)
Risk-managed (won't blow up your account)

Most traders fail at #1 because they don't have a proper testing process.

You now have the exact framework professionals use. The question is: Will you use it?