Trading Strategies: Classification Approach Based on Modern Market Realities

What are Trading Strategies

Trading strategies are systematic sets of rules that determine when to enter a trade, when to exit, what position size to use, and how to manage risk. It is the core of any algorithm in trading — whether you trade manually or use an automated trading bot.

Many beginner traders make the mistake of thinking that any of trading strategies is just “buy here, sell there.” In reality, successful trading is an algorithm of actions based on logic, statistics, and risk management.

Trading strategies are not dogmas but tools. They can adapt, change, but having one is critically important. Without a strategy, a trader becomes a gambler relying on luck. And in a market where every tick is measured in money, luck has minimal place.

Primitive Example:
If you trade a strategy based on the crossover of two moving averages (MA50 and MA200), you do not make decisions “by eye” — the strategy clearly dictates: buy when MA50 crosses MA200 from below, and sell on the opposite crossover.

Main Steps in Forming a Trading Strategy

Creating a strategy is essentially developing decision-making logic. Below are the main stages.

Choosing a Market

The first step for any of trading strategies is defining the type of market it will operate on. Each market has its own characteristics: liquidity, volatility, trading hours, response to news, etc. What works perfectly on the stock market (S&P 500) may completely fail in the crypto market, where price can move 10% in a few hours.

Example:
On the stock market, volume- and trend-based strategies (e.g., “trend-following”) work well, while in crypto, trading strategies adapted for 24/7 trading and high volatility are preferred.

Choosing an Instrument for Trading Strategies

Each instrument has its own “character.”
Bitcoin behaves completely differently than low-liquidity altcoins. EUR/USD differs from USD/JPY.

Example:
On BTC, you can successfully use a long-period moving average strategy (MA200), but on low-liquidity tokens it will give many false signals.

Defining the Basis and Method of the Strategy

At this stage, it is decided what the trading logic will be based on:

• Indicators (RSI, MA, MACD, Bollinger Bands)
• Market structure (levels, trends, patterns)
• Behavioral models (reaction to news, market sentiment )
• Volume or order book (Order Flow)
• A combination of factors

Example:
An indicator-based strategy may use an RSI and signal line crossover, while a market structure strategy may enter after a local high breakout confirmed by a global trend.

Determining Entry and Exit Points

Without clear rules for entry and exit, any of trading strategies loses its meaning. The entry point is when the probability of movement in your favor is highest. The exit point is for taking profit or minimizing losses.

Example:
In a breakout strategy, entry occurs when a candle closes above resistance, and exit occurs when the take-profit is reached or the price returns below the level.

Risk and Capital Management

Even the best of trading strategies is doomed without risk management. Risk should be limited in advance — before entering the trade. By the way, you can use the tools for calculating trade risk. The description explains in detail how to do this. There are also other useful calculators available for traders.

Example:
If the risk per trade is 2% of the deposit and the stop is 100 points, the position size is calculated so that a stop loss does not exceed 2%.

Backtesting on Historical Data

Backtesting is checking a strategy on past data.
It allows understanding how the trading strategies behave in different market phases: trend, sideways, volatility.

Example:
An Extremum Range MA crossover strategy tested on 5 years of ADAUSDT history showed 45% profitable trades and and profit factor 2,36.

Real-Time Testing

After successful backtesting, the strategy is launched in real-time but with a small volume. This ensures it works not only “on paper” but on the live market, taking into account spreads, slippage, fees, and delays.

Example:
If trading strategies behave stably with a small volume, real testing results roughly match backtesting results, and volume can gradually be increased.

Main Approaches to Classifying Trading Strategies

Below is an approximate structure of trading strategies in accordance with modern market realities. The classification turned out to be quite extensive. I do not claim to have an academic degree in trading, but this classification is based on my personal experience and the activities of my friends over the past few years. Later, we will take a closer look at each of these trading strategies with practical examples.

Trading Strategies Classification Tree

Trading Strategies
├─ By Basic Principle
│ ├─ Indicator-Based
│ ├─ Market Structure
│ ├─ Order Book Trading
│ ├─ Grid
│ ├─ DCA (Dollar Cost Averaging)
│ ├─ Arbitrage
│ ├─ Spreads
│ ├─ Combined
│ └─ News Trading
│
├─ By Market Type
│ ├─ Stocks
│ ├─ Currency (Forex)
│ ├─ Cryptocurrency
│ ├─ Securities, Bonds, ETFs
│ └─ Commodities & Indices
│
├─ By Instrument Type
│ ├─ Spot
│ ├─ Futures
│ ├─ Options
│ └─ CFDs / ETNs / Derivatives / Perpetuals
│
├─ By Market Analysis Method
│ ├─ Fundamental Analysis
│ ├─ Technical Analysis
│ └─ Combined
│
├─ By Holding Time
│ ├─ Scalping
│ ├─ Intraday
│ ├─ Swing
│ ├─ Positional
│ └─ Long-Term
│
├─ By Degree of Automation
│ ├─ Manual
│ ├─ Semi-Automatic (Signal-Based)
│ └─ Fully Automated
│
├─ By Automation Type
│ ├─ Own Code + Server + Exchange Account
│ ├─ Third-Party Bot + Exchange/Broker Account
│ ├─ Bot + Third-Party Service + Account
│ ├─ Bot Directly on Exchange
│ └─ Combined
│
├─ By Signal Transmission Method
│ ├─ API
│ ├─ Webhook
│ └─ Socket / REST Polling
│
└─ By Code Type
├─ Python
├─ MQL4/5
├─ Pine Script
└─ JS / C# / Rust

By Basic Principle

Indicator-Based

Uses technical indicators: MA, RSI, Stochastic, MACD, volumes, ATR, Bollinger Bands, ADX, etc.
Example: Buy when RSI is below 30, sell when above 70.

Market Structure

Analyzes trends, levels, reversals, candlestick patterns, figures, Elliott waves, Gaps, ORB.
Example: Enter on resistance breakout after consolidation.

Order Book Trading

Reading orders and liquidity (order flow).
Example: Buy when large limit orders appear in the order book.

Grid

Creates a grid of orders within a specific range.
Example: Auto bot buys every 0.5% drop and sells every 0.5% rise. This way, you profit frequently from even the slightest price fluctuations.

DCA (Dollar Cost Averaging)

Gradual averaging of the position over time.
Example: Weekly purchase of BTC for a fixed amount. This way, you gradually build a position at an average price, which can be beneficial in the long run.

Arbitrage

Uses price differences between markets or instruments.
Example: Buy BTC on Binance and sell on Coinbase if the difference is 1%.

Spreads

Works on differences between correlated assets.
Example: Long Brent and short WTI when the spread diverges.

Combined

Combines different principles.
Example: Indicator + market structure: enter on RSI at a level breakout.

News Trading

Uses market reaction to events.
Example: Enter a position 5 minutes before inflation data release.

Additionally:

• AI / Machine Learning algorithms
• Sentiment analysis (social media sentiment)
• Volatility strategies (VIX, ATR)

By Market Type

Stocks

Works with companies and indices. Focus on fundamentals, reports, news.
Example: Buy Apple shares before a report if profit growth is expected.

Currency

Forex — macroeconomic market. Interest rates and central bank news matter.
Example: Buy USD/JPY anticipating a Fed rate hike.

Cryptocurrency

24/7 high-volatility market, ideal for automation.
Example: Trade EMA crossover signals on BTC/USDT with TradingView webhook to Binance.

Securities, Bonds, ETFs

Trading Strategies based on yield and coupons.
Example: Buy bonds when interest rates drop.

Other markets: commodities (gold, oil, gas) and indices — often used for hedging.

By Instrument Type

Spot

Direct purchase of the asset.
Example: Buy Ethereum and hold until target.

Futures

Allows trading with leverage and on decline.
Example: Short BTCUSDT on Bybit at reversal signal.

Options

Works with time and probability.
Example: Buy a call on Tesla expecting a price rise.

Other instruments: CFDs, ETNs, derivatives, perpetuals (in crypto).

By Market Analysis Method

Fundamental Analysis

Based on real company metrics, macroeconomics, events.
Example: Buy Nvidia shares after a 20% profit growth report.

Technical Analysis

Uses charts, levels, indicators.
Example: Enter on MACD and signal line crossover on the daily chart.

Combined

Combines fundamental and technical factors.
Example: Buy gold after a Fed statement + chart confirmation.

By Holding Time

Scalping

Dozens of trades per day, small profit, micro stops.
Example: Buy on a micro-breakout with 0.2% take and 0.1% stop.

Intraday

All trades close by end of day.
Example: Trade on range breakout at US session open (ORB strategy).

Swing

Hold positions for several days.
Example: Buy on price pullback to daily MA20 and exit in 3 days.

Positional

Trades last weeks or months.
Example: Long on oil during a stable uptrend.

Long-Term

Effectively investments, though “buy-sell” principle remains.
Example: Buy Bitcoin for several years.

By Degree of Automation

Manual

Trader makes all decisions.
Example: Enter on chart after a Price Action signal.

Semi-Automatic (Signal-Based)

Trader receives signals from indicators, Telegram bots, or TradingView and decides whether to act.
Example: Signal from “Volume Breakout” indicator — trader opens and closes trade manually.

Fully Automated

Bot does everything according to the algorithm.
Example: Trader launches a ready bot on Trade Santa linked to exchange API.

By Automation Type

• Own code + server + exchange account — maximum control
• Third-party bot + exchange or broker account
  Example: Run CFD strategy on TESLA shares via MT4 using Capital.com broker API
• Bot + third-party service + account
  Example: TradingView signal triggers Wunderbit, which opens trade on Bybit crypto exchange via API
• Bot directly on exchange
  Example: Grid bot on Binance
• Combined — part logic locally, part via cloud

By Signal Transmission Method

• API — standard integration with exchange
• Webhook — used to link TradingView
• Socket or REST polling — for advanced solutions

By Code Type

• Python — universal for trading algorithms
• MQL4/5 — for MT4/MT5
• Pine Script — for TradingView
• JS, C#, Rust — for custom platforms

Conclusion

In recent years, the trading world has changed radically. Previously, traders spent years studying charts, indicators, and risk management. Today, most of these tasks are handled by algorithms. If you liked this article, then I recommend checking out the top 10 strategies for algo trading according to our website.

Modern trading bots can already:

• Analyze the market with dozens of indicators
• Manage risks
• Adapt to changing volatility
• Test on historical data

The trader’s task now is not to “catch signals manually,” but to evaluate the effectiveness of automated trading strategies: its long-term return, result stability, and drawdown risk.

Effectively, a trader’s risk management today is managing allocated capital for the chosen algorithm.
Your deposit is your “stop,” and the strategy’s profit is your “take.”

Yes, risk remains.
Backtesting does not guarantee future profit.
But just as no single trade guarantees success, systematic approach, control, and understanding of statistics matter most.

Trading Strategies without risk do not exist, but strategies with controllable risk and predictable outcomes do.

FAQ