How a Trading Idea Becomes a Set of Rules
A trading idea often starts with something very simple:
“Maybe the price tends to keep rising after a strong upward move.”
That may be an interesting observation, but it is not yet something a computer can trade.
A computer needs clear instructions. It needs to know what counts as a “strong” move, what should happen before entering a trade, when the trade should end, and what to do when the market does something unexpected.
That is the interesting part of developing an algorithmic trading strategy: turning a human idea into precise, measurable rules.
Start With a Question, Not a Strategy
People developing trading strategies do not always begin with a finished set of rules. Often, they begin with an observation or a question.
For example:
“What if Bitcoin tends to continue rising after a strong upward move?”
The next step is to make the idea measurable.
- What exactly is a “strong” move?
- How long should that move last?
- How do we decide whether the market is trending upward?
- When should an entry happen?
- When should the trade be closed?
- How much should be traded?
This is where an algorithmic trading idea starts becoming a set of rules.
A human can look at a chart and say, “That looks like a strong move.” A computer cannot. The trader has to define what strong means using numbers, conditions, or other measurable information.
Learn What You Can Measure
One of the ways traders turn ideas into measurable conditions is by learning about technical indicators.
A technical indicator is a calculation based on market information such as price or volume. Different indicators are designed to help traders examine things such as trend, momentum, volatility, or the size of recent price movements.
You do not need dozens of indicators to begin experimenting. A few simple ones can already turn vague ideas into measurable conditions.
Moving Averages
A moving average calculates an average price over a selected number of periods. Traders often use it to make the general direction of price easier to see.
A vague observation such as:
“The market seems to be moving upward.”
could become something more precise, such as:
“Only consider an entry when the closing price is above a 20-period moving average.”
The number 20 is not a magic number here. It is simply an example of how a visual observation can be converted into a measurable condition.
RSI — Relative Strength Index
RSI (Relative Strength Index) is an indicator commonly used to measure the strength of recent price movements. It is often used to identify whether price momentum appears relatively strong or weak.
Instead of saying:
“I only want to trade when momentum looks strong,”
a trader could investigate a rule involving an RSI level.
Again, the important part is not the particular number chosen. The important part is that “strong momentum” has been turned into something that can be measured and tested.
Some More Advanced Indicators
As someone learns more, they may come across indicators that answer different questions:
- MACD — Moving Average Convergence Divergence: commonly used to study momentum and changes in trend by comparing moving averages.
- Bollinger Bands: bands built around a moving average that help show how widely price is moving relative to its recent average.
- ATR — Average True Range: a measure of recent price movement that is commonly used to understand market volatility and the typical size of price moves.
- Volatility measures: measurements that describe how much and how quickly prices are moving. Higher volatility generally means larger price movements, while lower volatility means smaller movements.
At this stage, a trader is not necessarily looking for a magical indicator that predicts the future. They are learning what information can be measured and whether that information can help describe the idea they are investigating.
Where Do People Learn All This?
This is where the path can split.
Someone interested in algorithmic trading can go surprisingly deep into the technical side. They can learn technical analysis, statistics, programming, historical market data, data analysis and eventually build their own research tools.
Someone else may simply want to understand trading rules and test an idea without becoming a programmer.
Both are valid paths.
People commonly learn about indicators and trading concepts through books, courses, educational websites, documentation, communities and practical experimentation.
As they become more technical, they may start learning programming languages and tools used for working with data.
When Coding Enters the Picture
Once someone wants to investigate ideas using large amounts of historical data, coding becomes very useful.
Python is one popular choice because it can be used to load historical market data, perform calculations, create charts and run repeated experiments.
Tools such as pandas can help work with tables of data, while NumPy is commonly used for numerical calculations. Matplotlib can be used to create charts, and Jupyter Notebooks let people keep code, calculations, charts and notes together while experimenting.
The goal is not necessarily to build a trading bot immediately.
It might simply be to answer a question.
Then They Start Playing With Historical Data
Imagine the original question is:
A basic research experiment could look like this:
- Download historical Bitcoin price data.
- Calculate the 20-day moving average.
- Find the points where price crosses above it.
- Measure what happened during the following 1, 5, or 10 days.
- Plot the results on a chart.
- Compare the results across different periods.
That changes the process from:
“I think this might work.”
to:
“I have historical data that I can use to investigate whether this observation actually appears in the market.”
↓
Historical data
↓
Calculate a measurement
↓
Find the situations that match
↓
Measure what happened next
↓
Look for evidence
Why Charts Still Matter
Numbers are useful, but charts can reveal things that are difficult to notice in a table.
A trader might plot price together with a moving average and mark every crossover. The chart might reveal that many useful-looking signals happened during strong trends, while sideways markets produced many false signals.
This is why strategy research often combines calculation and visual inspection.
The computer can process thousands of observations. The human can then look at the results and ask whether the pattern actually makes sense.
What About GitHub and Other Code Repositories?
People who become more technical do not always build everything from zero.
GitHub is a platform where developers publish and collaborate on software projects. For someone learning algorithmic trading, it can be useful for finding examples of indicator implementations, data-processing code, backtesting projects, research notebooks and other experiments.
For example, someone learning Python might search for something like:
Python RSI backtest
They may find projects showing how another developer calculated RSI, prepared historical data or structured a basic backtest.
That can shorten the learning curve considerably.
This is also where algorithmic trading can become a much larger technical field. Someone who enjoys coding can eventually explore data pipelines, research repositories, statistical models, backtesting frameworks and their own strategy-development tools.
But you do not have to follow that path to start.
The Simpler Path: Turn the Rules Into a TradingView Strategy
If the technical side sounds interesting but building a complete Python research environment feels like too much, there is another route.
Once an idea has been expressed as clear conditions, those conditions can be represented in TradingView using Pine Script, TradingView's scripting language.
For example, our earlier idea could eventually be expressed conceptually as:
IF
price is above the moving average
AND
momentum meets the chosen condition
THEN
generate an entry signalThe exact rules, values and logic would need to be defined by the person developing the strategy. The important point is that the original idea has now become a collection of conditions a computer can evaluate.
TradingView can then apply those rules to historical candles through a strategy and show how the rules would have behaved in the past.
Backtesting simply means applying a set of trading rules to historical market data to see what would have happened.
This makes TradingView a useful starting point for someone who wants to explore algorithmic trading without first building an entire programming and data-analysis environment.
From Rules to Something You Can Test
Notice what has happened to our original thought:
“I want to trade when an upward trend gains momentum.”
↓
Possible measurable components:
- A way to define the trend
- A way to measure momentum
- A precise entry condition
- A precise exit condition
- A position-size rule
- Rules for managing risk
Only after those things are defined does it make sense to ask whether the idea can be tested.
And testing may lead to another round of questions:
- Does the idea work across different market conditions?
- Does it only work during one particular period?
- Does changing one parameter completely change the result?
- Would trading costs affect the result?
A Set of Rules Does Not Automatically Make a Good Strategy
This distinction is important.
You can turn an idea into perfectly precise rules and still end up with a strategy that does not work well.
You can also create rules that look excellent when tested against historical data but perform poorly later.
One reason is overfitting. Overfitting happens when rules are adjusted so closely to historical data that they perform well on the past but fail to generalize to new market conditions.
That is why turning an idea into rules is only one part of developing an algorithmic trading strategy. Research, testing and validation still matter before considering live automation.
The Journey From Idea to Algorithm
There is no single way to develop a trading strategy, but the process often looks something like this:
↓
Define the question
↓
Decide what can be measured
↓
Learn indicators and market concepts
↓
Collect historical data
↓
Experiment with Python, charts or other tools
↓
Turn observations into precise rules
↓
Backtest
↓
Refine and validate
↓
Consider automation
And this is where the two paths can eventually meet.
A technically minded trader might build much of the research process themselves using code, data and repositories. Someone who wants a simpler route can work with TradingView, Pine Script and its built-in strategy testing tools.
Once a TradingView strategy can generate defined trading signals, those signals can also become the starting point for an automated execution workflow. Webhooks can carry those signals to an execution service, and TurboBridge can connect the TradingView signal to supported exchange execution.
Algorithmic trading does not begin with a bot. It begins with an idea that can be made precise enough to test.
In the next part of this series, we can take the next step: how a computer actually takes those rules and turns them into trading decisions.