Notes for Those Who Want to Develop Algorithmic Trading

As someone who is in the kitchen of this business, I want to tell you a few things. Because we are not just creating a system that produces signals, we are building a thinking structure. And in this business, we progress not by memorizing, but by understanding.

When developing algorithmic trading systems, our goal is not only to create a code block that sends automatic orders; but to systematically define specific market behaviors, test them, and transform them into a sustainable structure.

Your codes are tools that describe your ideas.
But if your idea is incomplete, your algorithm will never yield the results you expect.

1️⃣ Strategy Design: Fundamental Algorithmic Logic

The thing that needs to be clarified before writing an algorithm:
"Which behavior of the market do you see as an opportunity and how do you detect it?"

The example thought chain should be as follows:

Liquidity sweep + order flow divergence → zone test → low momentum pullback → entry into trade

What is inside this structure?

-A structural trigger (sweep)
-Approval data (CVD divergence / Delta burst)
-A technical field (zone / order block)
-Timing filter (volatility contraction / session opening)

Each structure defines "when the system should operate." Those that do not develop a strategy only generate random signals.

2️⃣ Data Usage and Advanced Indicators

Classic indicators (RSI, MACD etc. ) are now insufficient for many algorithmic systems. To be able to define the structural and real-time behaviors of the market, you need to turn to the following data types:

a) Order Flow and Derivatives

CVD (Cumulative Volume Delta)
Analyzes the real buyer-seller balance. If CVD rises while the price is falling, there may be latent demand.

Delta (Aggressive Buy/Sell Volume Difference)
Measures the short-term aggressive trading balance. An in-zone delta explosion indicates that the zone has been accepted.

Open Interest (OI)
Shows whether a new position has been opened or not. OI increase + price rise → trend confirmation. OI decrease + price movement → short squeeze / trap possibility.

b) Liquidity Data

-Heatmap ( example: TradingLite / Tensor )
-Spot order book densities
-Sweep analyses

Able to analyze data, can read the market. It is not enough to just use data; it is necessary to create a data scenario.

3️⃣ Backtest Discipline and Statistical Foundations

The code does not mean anything.
If you do not know how the code works on historical data, the result you will get in the real market is just a guess.

Metrics that you must measure while backtesting:

Win Rate - Winning Percentage
Average R - Average Risk:Reward ratio
Expectancy - Expected value per trade → (Avg Win * WinRate) - (Avg Loss * LossRate)
Max Drawdown - The worst drawdown period
Time-based - Filtering Hour, day, weekly filtering
Distribution - CurveTrade results distribution chart

Additionally:

Test each strategy separately on an hourly basis. It may only work between 10:00 and 13:00.

Apply Monte Carlo simulation. Is the system still positive even in random variations?

Perform an out-of-sample test. Test the algorithm you developed on data it has not seen before.

Note: Optimized systems do not win. Adaptive and robust systems win.

4️⃣ Live Testing Process and System Development

A successful system in backtesting may fail in live trading. The reasons for this are mostly as follows:

-Data latency / slippage / spread widening
-Change in real-time liquidity conditions
-The user's intervention outside the system (is the critical factor)

That's why in the live testing process:

-Test with real orders using small capital.
-Keep a transaction log: Write down the reason and outcome after each transaction.
-Set up a log system: When did which signal occur, how many seconds did it last, how much did the price drop?

The moment a system starts to operate in real life, that system is truly said to be "working."

Closing: Turning Thought into Code

Writing an algorithm is not a software job; it is a discipline of thinking. The strongest code reflects the strategy that is the simplest and clearest in thought.

So first:

Which market behavior presents an opportunity for me?
How do I measure this behavior?
What triggers this measurement?
When do I consider it invalid?
Translating a structure whose answers you do not know into code is just a waste of time. Don't forget that time has a cost too. :)

If you want to proceed on this path, define your strategy.
Read the data.
Calculate your statistics.
Test in the real world.
And repeat everything.

#AlgoTrade # AlgoZone