Algorithmic Trading: Build & Backtest Bots Without Coding

In this hands-on lesson, you will learn how professional traders build trading systems step by step — from identifying a market pattern to testing a complete algorithmic strategy. This is your first practical introduction to creating real trading logic using technical indicators, rules, risk management, and no-code automation.

What You’ll Learn in This Lesson

1. What a Trading System Really Is

You’ll understand the core structure behind any profitable trading strategy:

• clear entry rules

• precise exit rules

• risk and capital management

• conditions for trade filtering
  We start with a simple example (such as an EMA crossover) and gradually turn it into a more robust, rules-based system.

2. How Traders Find Patterns and Build Strategies

Professional traders base their systems on repeatable market behaviors, such as:

• seasonality patterns

• fundamental events

• chart patterns (double bottom, trend breakouts, etc.)

• indicator-based signals

You’ll see why indicators like EMA, RSI, MACD, and others are the most commonly used building blocks — and how to turn their signals into actionable rules.

3. Creating Your First Strategy Using EMA 200

You’ll follow a complete walkthrough of building a simple EMA-based strategy:

• how price reacts to EMA 200

• defining expected bounce behavior

• choosing take-profit and stop-loss levels

• creating logic that avoids false signals

• improving accuracy by filtering entries

This lesson shows how even a simple indicator can form the basis of a profitable trading system — when combined with structured rules and testing.

4. Turning Ideas into Automated Rules (No Coding Required)

You’ll learn how to convert trading ideas into clear algorithmic rules using the CDZV Toolkit, including:

• Condition Manager Indicator (setting up conditions, TP/SL, filters)

• State Machine (building the sequence of events and confirmations)

This allows you to automate complex trading logic in minutes, without programming skills.

5. Backtesting Your Strategy the Right Way

This lesson demonstrates how to backtest your system instantly using the CDZV Toolkit:

• run historical simulations

• review all trades

• identify profitable and losing patterns

• find weaknesses and optimize rules

• validate the strategy across multiple timeframes

You’ll see how testing that once required hours (or hiring a programmer) can now be done in seconds.

6. Improving and Optimizing the Strategy

You will learn how to refine your trading logic through real examples:

• limit entries to the first 5 bars after EMA interaction

• move stop-loss to breakeven automatically

• increase take-profit when volatility allows

• add EMA 21 as a filtering condition

• adapt the system for different assets and timeframes

These improvements turn a basic idea into a much safer and more profitable algorithm.

The final result:
- ~21% yearly profit
- 84% winrate
- minimal drawdown
- fully automated
- repeatable on other markets with proper optimization

7. Understanding Robustness and Market Adaptation

You’ll learn why every trading system must be adapted to market conditions:

• volatility differences

• trend vs. range behavior

• instrument-specific characteristics

We also cover key professional metrics like drawdown, losing streaks, and system stability — which you will learn in later lessons.

In this lesson, we explain what trading indicators are, why they continue to work for decades, and how they help traders build clear, logical, and automatable trading systems. The lesson focuses on practical understanding without overpromising information that is not included.

1. Why Indicators Work

We explain the foundation of indicators:

• mathematical models

• analysis of historical market data

• recurring price patterns

You will learn how indicators help reveal patterns that are difficult to see visually, and why groups of traders using similar models often create predictable market reactions.

2. The Core Purpose of Indicators

The lesson covers what indicators are actually used for, including:

• identifying trend direction and strength

• finding support and resistance levels

• measuring market volatility

• detecting buyer and seller activity

• filtering market conditions

• triggering alerts based on predefined events

We emphasize that indicators do not predict future price movements; they help analyze current market conditions.

3. Common Types of Trading Indicators

The lesson reviews major categories of indicators and their purposes:

• Trend indicators (MA, EMA, RSI, MACD, ADX)

• Volatility indicators (ATR, Bollinger Bands, Channel Monster)

• Market structure indicators (ZigZag, Fractals, channels, support/resistance)

• Volume indicators (Volume, Cluster Volume)

Each category is explained in terms of what it measures and how it helps the trader.

4. How Indicators Support Trading System Development

We discuss how traders use indicators to build structured and objective strategies based on measurable patterns. You will learn:

• how indicators help define entries and exits

• how they standardize decision-making

• how they are used in semi-automated and fully automated trading

• why indicators remain the foundation of many institutional trading algorithms

5. Indicators, Discipline, and Automation

The lesson explains how indicator-based rules help traders:

• follow a consistent trading plan

• reduce emotional and impulsive decisions

• maintain objectivity

• avoid inconsistent manual trading

• transition from discretionary trading to rule-based trading

Even partial automation—such as using alerts—helps traders stay disciplined without monitoring charts constantly.

6. Practical Overview

The lesson provides practical examples of popular indicators and demonstrates:

• how they operate

• what market data they evaluate

• how they affect decision-making in real conditions

This establishes the foundation for more advanced and applied lessons later.

Understanding Moving Averages: Core Concepts, Types, and Practical Trading Use

In this lesson, we take a detailed look at one of the most widely used technical indicators — the Moving Average. You will learn what a moving average is, how it works, the main types of MA, and how traders use it to identify trends, detect support and resistance levels, filter market conditions, and generate trading signals.

What a Moving Average Is and Why It Matters

We explain the logic behind moving averages:

• how they smooth out price fluctuations

• how they highlight the market trend

• why they help measure how far price deviates from its recent average

The lesson demonstrates how to add and configure Moving Averages on TradingView, though the concepts apply to any trading platform.

Main Types of Moving Averages

The lesson covers four essential types:

• SMA (Simple Moving Average) — arithmetic mean over a selected period

• EMA (Exponential Moving Average) — gives more weight to recent prices

• WMA (Weighted Moving Average) — assigns diminishing weights as prices get older

• HMA (Hull Moving Average) — a faster, smoother version based on WMA

You will see how each type responds differently to price changes and why traders choose one type over another depending on market conditions.

How to Choose the Right MA Type and Period

We discuss:

• why EMA responds faster than SMA

• why HMA reduces lag in trending markets

• how shorter vs. longer periods affect signal frequency and reliability

• how to adjust MA settings to fit your trading style

The lesson also introduces the concept of backtesting and demonstrates how CDZV Toolkit on TradingView can be used to test MA-based ideas on historical data.

How Traders Use Moving Averages

This lesson provides practical applications, including:

• determining trend direction

• filtering signals and avoiding trades against the trend

• using MA combinations (EMA50/EMA100/EMA200)

• detecting dynamic support and resistance areas

• identifying potential trend reversals through MA crossovers

We also address limitations such as indicator lag, noisy signals during volatility, and the need to adapt parameters to changing market conditions.

By the end of this lesson, students will understand:

• the purpose and logic of Moving Averages

• the differences between SMA, EMA, WMA, and HMA

• how MA periods affect behavior and signals

• how to use moving averages for trend analysis

• how MA-based filters improve trading system stability

• the strengths and weaknesses of MA in real market conditions

This lesson provides a solid foundation for future modules on indicators and strategy design.

In this lesson, we take a detailed look at the MACD indicator (Moving Average Convergence Divergence), one of the most widely used tools in technical analysis. You will learn how MACD works, how its components are constructed, and how traders use it to identify trend dynamics and potential buy or sell signals.

You will learn:

• how the MACD line is calculated and what the difference between the fast and slow EMAs represents;

• the role of the signal line and how its crossovers can highlight potential trend shifts;

• how the MACD histogram works and how it helps assess market momentum;

• the strengths and limitations of MACD, why it may lag, and how to avoid false signals;

• how the default settings (12, 26, 9) affect the indicator’s behavior;

• how to interpret MACD divergences and why they may signal possible reversals;

• why combining MACD with tools like RSI, volume indicators, or moving averages can improve accuracy.

The lesson also includes practical recommendations on applying MACD in trading, managing risks, and refining a strategy through testing and indicator combinations.

This lesson is suitable for traders at any level who want to better understand market dynamics and apply MACD as an analytical tool in their trading systems.

The Relative Strength Index (RSI) is one of the core indicators in technical analysis. This lesson explains how RSI works, what its signals mean, and how the indicator can be used in practical trading systems. The material focuses on realistic application, strengths, limitations, and interpretation without overstating its reliability.

In this lesson, you will learn:

• what RSI values represent and how overbought/oversold zones are identified;

• how RSI helps assess trend strength across different timeframes;

• why the 70/30 levels do not always function literally and how to consider market context;

• how to interpret basic RSI signals and what to watch for when making decisions;

• how bullish and bearish divergences form and why they may indicate weakening momentum;

• the strengths and limitations of RSI, including lagging signals and sensitivity to volatility;

• how to choose appropriate RSI settings (shorter vs. longer periods) depending on your trading style;

• how combining RSI with tools like moving averages, MACD, or volume indicators improves filtering and accuracy;

• why backtesting is essential for evaluating a strategy and how to adjust RSI parameters to changing market conditions.

This lesson is suitable for traders of all experience levels who want to better understand market momentum, identify potential entry and exit points, and use RSI as part of a structured analytical approach.

In this lesson, we take a detailed look at the Channels Monster indicator — a channel-based tool that helps traders identify potential buy and sell zones, understand market structure, and adapt trading decisions to changing volatility. This lesson is suitable for both beginners and advanced traders, as well as anyone planning to build grid bots using channel logic.

What we cover in this lesson:

• how channel indicators work and how price interacts with upper and lower boundaries;

• how the three-level channel structure helps identify possible entry and exit areas;

• using channel boundaries to evaluate trend strength and market momentum;

• an overview of different channel calculation methods (MA+%, MA+ATR, MA+STDEV, Bollinger, MRC) and when each method is useful;

• how moving average type, MA length, and smoothing parameters influence channel behavior;

• how channel width changes depending on volatility and indicator settings;

• an introduction to the advanced MRC (Mean Reversion Channel) mode and its digital filters;

• in which market conditions classic MA-based channels work better, and when MRC provides advantages;

• how channel indicators can be used as a foundation for grid trading strategies;

• strengths and limitations of channel-based indicators, including lag, false signals in ranges, and sharp breakouts;

• practical recommendations on testing the indicator, choosing parameters, and combining channels with other analytical tools.

By the end of the lesson, you will understand how to interpret channel structure, how to select an appropriate calculation method, and how Channels Monster can be integrated into your trading system, including partially or fully automated approaches.

Practical Use of the CDZV Toolkit for Strategy Testing and Performance Evaluation via Backtesting on TradingView

What You'll Learn:

• What backtesting is and why every trader needs it

• How to test a strategy across different market phases (uptrend, downtrend, sideways)

• How to build and test a strategy without coding using Condition Manager and Strategy Tester

• How to configure stop-loss, take-profit, breakeven, and trailing stop

• How the State Machine works for multi-step strategies

Key Concepts:

• Backtesting — testing a strategy on historical data to understand how it would have performed

• Condition Manager — a module for defining entry and exit conditions

• Condition Manager Strategy — a tool for backtesting and statistical analysis

• State Machine — a logic builder for multi-stage workflows (e.g., signal → confirmation → entry)

• Constants — variables used to store stop-loss, take-profit, and coefficient values

Strategy for testing: Since linking is prohibited on the Udemy platform, please write to us in the Q&A section to get the link to the strategy discussed in this video.

How to Analyze Strategy Results: Key Metrics and Their Real-World Interpretation

This is one of the most important lessons in the course.
You’ll learn how market cycles affect strategy performance — and why a strategy that works in one phase may fail in another.
We’ll dive deep into backtesting, explore which metrics truly matter, and how to interpret them for practical decision-making.

Using TradingView as an example, you’ll learn to analyze your system’s results in terms of risk/reward, robustness, and psychological impact.
We’ll break down key parameters like Maximum Drawdown, Profit Factor, Win Rate, Sharpe Ratio, and Sortino Ratio — and show how to use them for objective strategy evaluation.

What You'll Learn:

• What trading cycles are and how they break down into short-, medium-, and long-term phases

• Why every strategy should be tested across at least one full market cycle

• How to determine if your strategy has enough statistical depth (minimum 100 trades per cycle)

• How to run a backtest and avoid common mistakes traders make

• How to assess maximum drawdown and define a safe risk level

• What Profit Factor means, how to calculate it, and what’s considered acceptable

• How Win Rate and Profit Factor relate — and how to balance them

• What Sharpe Ratio and Sortino Ratio are, how they differ, and how to use them for strategy evaluation

• How to factor in commissions and losing streaks during analysis

Essential Trading Metrics You Need to Know:

• Trading Cycles — The repeating market phases—rallies, declines, and ranging periods—that all markets experience.

• Backtesting — Validating your strategy using historical price data.

• Maximum Drawdown — Your biggest capital loss from peak to trough.

• Profit Factor — How your total wins compare to total losses.

• Win Rate — The percentage of winning trades versus all trades.

• Sharpe Ratio — Return per unit of risk (including all price swings).

• Sortino Ratio — Return per unit of downside risk only.

• Max Consecutive Losses — Your longest losing streak (essential for risk control).

Even the Best Strategies Can Fail in Live Markets.
In this lesson, you'll learn how to avoid over-optimization, test your strategy for robustness, and build a resilient portfolio of trading systems.
We'll explore the real risks of algorithmic trading — from technical failures to human errors — and show how automation and discipline can help you overcome them.

What You'll Learn:

• What over-optimization is and how it destroys profitability in live trading

• How to test strategy robustness across timeframes, assets, and slippage scenarios

• How to manage a portfolio of dozens or even hundreds of strategies

• Why controlling asset correlations is critical for risk management

• How to build a “traffic light” system to monitor drawdowns

• How to avoid common psychological pitfalls in trading

• How to automate strategy monitoring and reduce human error

In this lesson, you'll master how margin, leverage, and hedging mode work in crypto trading. We'll break down the difference between isolated and cross margin, explain how liquidation levels are calculated, and show you how to use leverage effectively without excessive risk.

You'll learn to choose the right mode for different strategies and discover how to manage your capital efficiently even when markets are highly volatile.

CONDITION MANAGER — The Core Module for Signal Logic and Strategy Design

CONDITION MANAGER is the central module of the toolkit, allowing you to seamlessly combine various external indicators to generate trading signals or calculate constants.
Thanks to its versatility and power, you can build the majority of your trading strategies using just CONDITION MANAGER — like a Swiss Army knife in the world of trading.

The DATA SOURCE module functions as your strategy's prep kitchen. It lets you extract data from other indicators, combine multiple data streams, and apply mathematical transformations before using them in your CONDITION MANAGER or any other module—even linking DATA SOURCE modules together. Think of it as a flexible memory buffer or calculation layer for your trading logic.

The State Machine is a system that exists in one of several states and transitions between them based on events or input signals.

Think of a washing machine: it has different modes (states)—wash, rinse, spin. Depending on your selection (the event), it moves to the appropriate state and runs the corresponding cycle.

Similarly, a state machine in programming or electronics 'switches' between states according to predefined rules.

SMART GRID Pro — A Comprehensive Module for GRID and DCA Strategies

SMART GRID Pro is an all-in-one module designed for managing and testing Smart GRID and DCA trading strategies. It includes all the essential parameters for execution and backtesting, eliminating the need for multiple external indicators.
Here, you can configure a wide range of grid settings, risk parameters, signal routing to exchanges, and much more.

Condition Manager Strategy — Core Module for Backtesting Trading Strategies

Condition Manager Strategy is a key module for backtesting trading strategies. It handles the calculation of all trades and allows you to define conditions for various trading scenarios. When added to the chart, it appears as the letter “S”.

This module is designed for backtesting signal-based strategies — where trades are opened based on specific indicator signals and positions are not built using a grid.
It is not intended for testing GRID or DCA strategies — for those, we provide the Smart Grid PRO module.

IF-THEN-ELSE Indicator Module — Your Logic-Based Assistant

The IF-THEN-ELSE module is your assistant for building conditional logic. It operates on the principle of “if this, then that, else something else.”
This module serves as a data input source for other modules, allowing you to define branching logic and control flow within your trading system.