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Why Don’t You Just Trade Fully Automated Trading Systems?

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In the modern trading landscape, the allure of fully automated trading systems is strong. With promises of hands-free profits and sophisticated algorithms, it’s easy to understand why traders might gravitate toward this approach. However, the reality of fully automated trading systems (ATS) is more complex. Let’s explore why traders often favor hybrid automated trading systems, such as those leveraging AFT (Algo Futures Trading) and AWT (AlphaWebTrader), for a more robust, adaptable trading strategy.

The Reality of Fully Automated Trading Systems

Lack of Market Adaptability

Fully automated systems rely on preprogrammed rules and historical data. While these rules can be effective in specific market conditions, they often fail to adapt to unforeseen changes, such as major economic events, news catalysts, or shifts in market sentiment. Markets are dynamic, and rigid systems can struggle to keep up.

Prop Firm Constraints

Prop firms, which offer traders access to funded accounts, typically prohibit 100% automated trading. This restriction exists because fully automated systems can quickly breach drawdown limits due to their inability to pause or recalibrate during volatile phases. Hybrid systems provide a manual override capability, aligning better with these rules.

The Drawback of Black Box Systems

Many fully automated systems are “black boxes,” offering no transparency into their logic. Traders using these systems are left to trust algorithms they don’t understand, making it challenging to diagnose issues or improve performance during losing streaks.

Risk of Over-Optimization

Fully automated systems often fall victim to over-optimization, tailoring their strategies to fit historical data too precisely. This can lead to poor performance in live markets where conditions deviate from the backtested scenarios.

Why Hybrid Automated Trading Systems Are Superior

Dynamic Decision-Making

Hybrid systems empower traders to leverage automated signals for consistency while retaining the ability to intervene based on market context. For instance, with AFT and AWT, traders can follow algo signals while using their judgment to adjust trades during pivotal moments.

Enhanced Control

Hybrid trading blends the precision of automation with the control of manual trading. Systems like AFT + AWT allow traders to configure algo signals, filters, and exits or even inject their own logic into the framework. This flexibility makes hybrid systems adaptable to diverse trading styles and market conditions.

Compliance with Prop Firm Rules

Hybrid trading aligns well with prop firm guidelines. A trader can combine systematic signals with manual execution, reducing the risk of breaching drawdown limits and ensuring compliance with firm regulations.

Improved Risk Management

Hybrid systems enable traders to manage risks more effectively by manually overriding or modifying trades in response to unforeseen events. Automation handles repetitive tasks, while traders focus on high-level decision-making.

Scalability and Growth

Hybrid systems, like those powered by AFT and AWT, provide a pathway for traders to evolve. Beginners can start with semi-automated setups, manually taking systematic signals, and progressively transition to fully automated strategies for specific markets or sessions.

How AFT + AWT Combine the Best of Both Worlds

  • Algo Signals + Human Insight: AFT provides systematic trading signals, while AWT offers tools like TradeViewPoint and Trendo to monitor market conditions and correlations.
  • Customizable Automation: Traders can build and refine their strategies by adding filters, adjusting parameters, or incorporating custom logic into the baseline settings.
  • Learning Through Execution: Hybrid trading encourages traders to learn market behavior through hands-on experience, fostering a deeper understanding of when to trade and when to step aside.

Conclusion

While fully automated trading systems may seem like the ultimate solution, their limitations in adaptability, transparency, and risk management often make them unsuitable for long-term success. 80% of traders lose at day trading manually, and up to 95% of day traders lose at fully automated trading!

Hybrid automated trading systems, such as those utilizing AFT + AWT, provide the perfect balance of automation and human intelligence, offering flexibility, control, and scalability. Our VIP Trading group can surpass 66% winners versus losers, compared to the standard retail world of 20% wins. Need we say more? AFT + AWT were purpose-built for hybrid trading specifically as we want the maximum profit with the least drawdown systems.

By combining the best of both worlds, traders can harness the power of automation while retaining the discretion to navigate the complexities of modern markets. Whether you’re seeking consistency, compliance, or growth, a hybrid approach paves the way for sustainable trading success.

Book list reading list on trading systems performance

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Books on Trading System Analysis and Improvement


Books on Trading System Analysis and Improvement

Here’s a curated list of books on system expectancy, trading performance measurement, and related concepts to deepen your understanding of trading system analysis and improvement:

Trading System Development and Expectancy

  1. “Trade Your Way to Financial Freedom” by Van K. Tharp
    • A comprehensive guide to system development, expectancy, and position sizing.
    • Focuses on creating systems tailored to your trading psychology and goals.
  2. “Building Winning Algorithmic Trading Systems” by Kevin Davey
    • Offers practical advice on designing, testing, and optimizing trading systems.
    • Emphasizes risk management and long-term profitability.
  3. “Design, Testing, and Optimization of Trading Systems” by Robert Pardo
    • A detailed guide on developing and validating trading systems.
    • Introduces robust testing techniques and optimization strategies.

Risk and Money Management

  1. “The Mathematics of Money Management” by Ralph Vince
    • A deep dive into money management strategies and their impact on trading expectancy.
    • Explores concepts like optimal bet sizing and drawdown control.
  2. “The New Trading for a Living” by Dr. Alexander Elder
    • Covers trading psychology, system design, and risk management.
    • Includes practical tools for measuring and improving system performance.
  3. “Position Sizing: The Key to Maximum Returns” by Van K. Tharp
    • Explains how position sizing impacts trading system expectancy and overall results.

Performance Measurement and Trading Metrics

  1. “Beyond Technical Analysis” by Tushar S. Chande
    • A focus on system development and performance measurement metrics.
    • Introduces innovative methods for evaluating trading systems.
  2. “Systematic Trading” by Robert Carver
    • Covers quantitative approaches to trading and performance evaluation.
    • Explains how to test, measure, and refine trading systems.
  3. “Quantitative Trading Systems” by Howard B. Bandy
    • Discusses the mathematics and analysis behind trading systems.
    • Provides actionable advice on evaluating system performance.

General Trading and Market Analysis

  1. “The Art of Trading: A Complete Guide to Trading the Markets” by Christopher Tate
    • Combines trading psychology, technical analysis, and system evaluation.
    • Great for beginners looking to understand expectancy and performance metrics.
  2. “Trading Systems and Methods” by Perry J. Kaufman
    • A classic, comprehensive resource on trading system development and analysis.
    • Covers tools, metrics, and strategies for system evaluation.
  3. “Thinking in Bets” by Annie Duke
    • Focuses on decision-making under uncertainty, relevant to trading expectancy.
    • Explains how to approach probability and outcomes objectively.

Trading Psychology and Long-Term Performance

  1. “The Disciplined Trader” by Mark Douglas
    • Explores the psychological aspects of trading and their impact on performance.
    • Includes methods to build consistency and discipline in trading.
  2. “Trading in the Zone” by Mark Douglas
    • Focuses on mindset and emotional control to enhance system performance.
    • Complements expectancy by addressing trader behavior.
  3. “The Mental Game of Trading” by Jared Tendler
    • Explores the psychological challenges of trading and how to overcome them.
    • Offers tools for improving discipline and maintaining focus.

These books cover a mix of technical, quantitative, and psychological aspects of trading, offering a well-rounded approach to improving your systems and understanding expectancy.


Trading System Win Rates: Ranges, Realities, and Refinements

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Algo Trading Insights


I was recently asked, “What win percentage do your algos achieve?” This is a broad and open-ended question—and a good one but not a great one. It would not be a question a pro trader or a fund manager would ask me. They would ask about NAV, % profit, drawdown, etc., or better, what expectancy your systems have, then dive into preferences of win ratio and RR, etc. Let’s take a moment to put it into perspective.

Note to self: I need to get the baseline algo stats website online again, as they’re a helpful guide. However, that’s just the starting point—there’s much more to the story. While baselines are useful to understand, let’s dive deeper into the possibilities.

AFT (Algo Trading Framework) is a comprehensive tool designed with preconfigured turnkey systems, giving traders a ready-made solution to start trading effectively. However, it also offers the flexibility for traders to invent, configure, or even code their own algo rules. With its vast range of off-the-shelf features, there isn’t just one answer to what a system can achieve—it’s simply too expansive. The permutations are nearly endless, allowing traders to experiment and refine their setups for years if they choose.

For many, the journey begins with our turnkey systems, offering a solid foundation to start trading. As traders master the basics, they can then optimize, experiment, and create unique combinations to suit their style. Some prefer to stick with the turnkey setups, live trading, and fine-tuning them to achieve consistent results. Whatever the approach, AFT adapts to the trader’s needs and skill level.

Trading system win rates can vary widely, ranging from 35% to 95%, depending on the trader’s preference, the type of trading system, and their style. While high win rates may seem appealing, it’s important to understand what truly defines a successful trading system.

Baselines and Real-World Performance

At Algo Trading Systems (ATS), baseline systems typically achieve win rates between 50% and 65%. However, live traders in the VIP Group often average 66% to 80% win rates by leveraging a combination of strategies and tools, such as the Hybrid Trading Approach.

Why Win Percentage Alone Is Misleading

While win percentage is an essential metric, it cannot determine profitability on its own. For instance, a trading system with a 95% win rate could still lose money after factoring in commissions and costs if its losses are disproportionately large compared to its wins.

A more meaningful metric combines win ratio and risk-reward into a measure of expectancy. For example:

  • A 65% win rate with a 1:1 risk-reward ratio is excellent.
  • A 50% win rate with a 1:2 risk-reward ratio would be exceptional.
  • A 50% win rate with a 1:1.25 risk-reward ratio is more typical and sustainable.

Example: High Win Ratio Scalper System (ES Futures)

Consider a scalping system with the following parameters:

  • Stop: 24 ticks
  • Target: 8 ticks

If we assume random price movement without any edge or strategy, the likelihood of hitting the target or stop is inversely proportional to their respective distances from the entry point. The shorter distance (the target) has a higher probability of being hit.

Probabilities:

  • Target Being Hit: 75%
  • Stop Being Hit: 25%

This means the win percentage could be 75% based purely on chance, assuming equal price movement probabilities.

Adjusted Example: Smaller Target

Now, consider a system with:

  • Stop: 28 ticks
  • Target: 6 ticks
  • Total Distance: 34 ticks

Probabilities:

  • Target Being Hit: 82.35%
  • Stop Being Hit: 17.65%

The target is 82.35% likely to be hit because it is much closer to the entry point than the stop. The stop, on the other hand, has a 17.65% probability of being hit.

Caveats of High Win Ratios

While such systems heavily favor hitting the target, they require a very high win rate (>82.35%) to overcome their poor risk-reward ratio (approximately 4.6:1). For every 1 loss, you would need at least 5 wins to break even.

Is This Tradeable?

For me, no. I prefer baseline systems designed around a 65% average win ratio, with targets that start at 1:1, 1:2, or even 1:5 risk-reward ratios, and potentially 1:10 for runners.

Improving the System

To improve such systems, align trading with high-probability times to increase the win ratio without compromising the risk-reward ratio. For example:

  • Stop: 20 ticks
  • Targets: 20, 40, 100 far runner

With trading skill and careful selection of high-probability conditions, it’s possible to move the win ratio from 50% to 65%, or even 85%, over time.

Introduction to Expectancy: Combining Win Percentage and Risk-Reward

Expectancy is the cornerstone of any successful trading system. It combines two critical metrics—Win Percentage and Risk-Reward Ratio—to provide a complete picture of a strategy’s profitability over time. While Win Percentage reflects how often a trade is successful, it doesn’t account for the size of wins versus losses. Similarly, Risk-Reward Ratio quantifies the relationship between potential profits and losses but says nothing about how frequently those profits occur. Expectancy bridges this gap by calculating the average profit or loss per trade, considering both the probability of winning and the magnitude of gains and losses. A balanced approach, such as a moderate Win Percentage paired with a favorable Risk-Reward Ratio, often leads to consistent and sustainable trading outcomes. This metric allows traders to evaluate whether their system can generate profits over the long term, regardless of occasional losing streaks.

Example of Expectancy for a System Trading ES E-mini Futures with the Given Parameters:

System Parameters:

  • Average Loss (Stop): 24 ticks
  • Average Reward (Target): 12 ticks
  • Win Percentage (Win %): 66% (0.66)
  • Loss Percentage (Loss %): 34% (0.34)

Step-by-Step Calculation:

  1. Win Component: 0.66 × 12 = 7.92
  2. Loss Component: 0.34 × 24 = 8.16
  3. Expectancy: 7.92 – 8.16 = -0.24

Expectancy Result:

The system has an expectancy of -0.24 ticks per trade, meaning that over time, you lose 0.24 ticks on average for each trade.

Interpretation:

This system, with a 66% win rate and a risk-reward ratio of 2:1 (24 ticks risk vs. 12 ticks reward), is not profitable over the long term because the losses outweigh the gains despite a high win percentage.

To make this system profitable:

  1. Increase the Reward: Aim for a reward of at least 16 ticks (closer to a 1:1.5 risk-reward ratio).
  2. Increase the Win Rate: Improve the win rate beyond 66% to compensate for the current risk-reward imbalance.
  3. Reduce the Stop Loss: Tighten the stop loss to 18 ticks to align better with the 12-tick reward.

System 2 Parameters:

  • Average Loss (Stop): 24 ticks
  • Average Reward (Target): 20 ticks
  • Win Percentage (Win %): 66% (0.66)
  • Loss Percentage (Loss %): 34% (0.34)

Expectancy = (0.66 × 20) − (0.34 × 24)

Expectancy Result:

The system has an expectancy of +5.04 ticks per trade, meaning that over time, you gain 5.04 ticks on average for each trade.

Interpretation:

This system is profitable over the long term. Despite risking slightly more (24 ticks) than the potential reward (20 ticks), the 66% win rate compensates for the slightly unfavorable risk-reward ratio.

Why It Works:

  • The win percentage (66%) is high enough to offset the imbalance in the risk-reward ratio.
  • The average reward (20 ticks) is close to the stop loss (24 ticks), meaning the system doesn’t rely too heavily on an extreme win rate to stay profitable.

Performance Insights:

This system has a positive expectancy, which makes it viable for consistent trading. To enhance it further:

  • Improve the Reward: Target closer to 24 ticks for a 1:1 risk-reward ratio.
  • Optimize the Win Rate: A slight increase in win percentage (e.g., 70%) would boost profitability significantly.

Combining Systems for Success

Using AWT alongside AFT has allowed traders to achieve win rates of 85% to 100% during certain trading periods, even when the system itself averages around 65%. Some traders have consistently averaged 87% win rates over extended periods, but this level of performance requires skill, practice, and time.

Achievably, realistically for day trading if you are hauling in 66% on average and your RR is around 1:1 and find that to be sustainable you are definitely on the right track to excellence—way above 80% to 90%+ of the rest of the herd!

By combining classic trading systems theory with the hybrid edge, traders can achieve maximum profit, minimum drawdown, and reduced emotional strain. This is the approach we teach, coach, and provide the tools and systems to implement.

Conclusion

With Hybrid—high probability trading methods and tools. Why do we beat traditional algo traders into dust? We can do what classic trading automated trading and systematic traders are unable to do—increase the win % without changing the system profile. For example, they would change the RR to increase the Win % or keep looking for the next holy grail settings and optimizing to get them through the next phase never settling, etc. But we use adaptive systems and one setting and instead nail the bullseye with far more consistency than any other method—with time, skill, and commitment. “Bruce Lee” trading if you will—economy of thought and action practicing the same way 10,000 times, not practicing 1,000 ways.

A Simple Analogy

Think of it as a Tesla driving around a racetrack on autopilot, achieving an okay lap time. With man and machine mode, a skilled driver at the wheel, navigating the nuances of S-bends and traffic, the performance could be far superior. Trading isn’t a Route 66 cruise; it’s more like a Swiss Alps rally. Anyone who believes a robot alone can outtrade a hybrid trader has yet to experience the real rewards and success that VIP traders achieve. To get funded and to trade, our approach allows a trader the maximum control on the smallest targets so as not to fall foul of the prop firm trading rules. ATS Traders share their stories and stats, straight from the horse’s mouth in the VIP Trading Group.


Winning Attitudes vs Losing Aptitudes

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Winning Attitudes:

Minds wide open! Start a systematic winning approach, get a routine, and make it a habit—the habit will make you or break you!

  • Receptiveness and Discipline: Successful traders are like sponges, absorbing knowledge and following a process. They understand the value of step-by-step learning and repetition, which builds a solid foundation for trading.
  • Documentation and Reflection: Keeping records (screenshots, market context, higher time frames, intra-day cycles, entry/exits, trade notes, journals) is vital. This allows for review, analysis, and improvement over time. It’s like having a feedback loop for your trading.
  • Action-Oriented: They emphasize taking action, even in a simulated environment. This builds confidence and experience. Analysis paralysis is a common pitfall, and these traders avoid it.
  • Progressive Learning: They understand that trading is a journey, not a destination. They focus on continuous improvement and expanding their knowledge gradually.
  • Openness to New Approaches: They are willing to explore hybrid methods and incorporate techniques like our Zero to Hero Trader Program and utilize NLP to enhance their trading performance.

Losing Aptitudes:

System jumping, grail seeking, closed mind, lost wanting help to get help, self-entitlement or inherent laziness and impatience “needing”

  • Impatience and Resistance: Traders who struggle often resist structure and process. They want shortcuts and instant gratification.
  • Information Overload: Seeking the “magic formula” or demanding PDFs of all settings can signify wanting answers without putting in the work.
  • Lack of Foundation: Jumping into live trading without understanding the basics is a recipe for disaster. It’s like building a house on sand.
  • Emotional Trading: Fear, greed, and recency bias (overemphasizing recent events) drive impulsive decisions, leading to losses.
  • Closed-Mindedness: Dismissing new ideas or approaches can limit their growth potential.
  • Inability to Self-Assist: Self-entitlement or sheer laziness leads to questions like, “What is the news? How do I find an answer?” instead of seeking solutions independently.

Your Call to Action:

“If you want to master trading for free with hybrid methods—and experience the life-changing and trader-changing success that trading and NLP can bring—attend our Zero to Hero Trader Program!”

We throw you the rope, but you must grab it…

  • This is exclusively for winning-mindset traders or those who want to transition from losing to winning with an open mind!
  • There is no secret; the cost of success is time, effort, and focus.
  • Leverage our trading tools for minimum drawdown, least emotion, and maximum profit.

Manual Backtesting and NLP: A Synergistic Approach

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Diving Deep into Manual Backtesting: A Step-by-Step Guide for Traders

In the world of trading, where uncertainty reigns supreme, backtesting offers a beacon of clarity. It allows traders to test their strategies against historical data, providing valuable insights into their potential profitability and risk. While automated backtesting tools are readily available, manual backtesting offers a deeper understanding of your system’s mechanics and nuances. This article serves as a comprehensive guide to manual backtesting, focusing on systematic chart signals for long and short trades, entry orders, and exit orders.

What is Manual Backtesting?

Manual backtesting involves manually analyzing historical price charts to determine how a trading strategy would have performed in the past. This hands-on approach allows traders to meticulously evaluate each trade, identify patterns, and understand the reasoning behind wins and losses.

Why Choose Manual Backtesting?

While automated backtesting offers convenience, manual backtesting provides:

  • Deeper Understanding: Manually analyzing charts fosters a profound understanding of your strategy’s strengths and weaknesses in different market conditions.
  • Pattern Recognition: By visually inspecting charts, you can identify recurring patterns and refine your strategy accordingly.
  • Flexibility: Manual backtesting allows for subjective judgment, enabling you to account for nuances that automated systems might miss.
  • Enhanced Intuition: The process hones your trading intuition and helps you develop a feel for the market.

Manual Backtesting and NLP: A Synergistic Approach

Combining the strengths of manual backtesting with the power of NLP (Neuro-Linguistic Programming) can indeed elevate your trading game to new heights. Let’s explore how these elements intertwine: Manual backtesting provides a deep, intuitive understanding of your trading system. NLP, on the other hand, offers tools to enhance your mental processes and program your mind for success. Here’s how they complement each other:

  • Sharpened Sensory Acuity (See, Speak, Hear): NLP emphasizes the importance of sensory awareness. By consciously engaging your senses (sight, sound, even kinesthetic feeling) during manual backtesting, you become more attuned to subtle nuances in the charts.
    • See: Observe chart patterns with heightened focus, noticing subtle breaks in trends, volume shifts, and candlestick formations.
    • Speak: Verbalize your observations as you analyze the charts, green long, red short! This reinforces pattern recognition and strengthens neural pathways associated with successful trading.
    • Hear: Imagine the “sound” of the market with signals indicators alerts, while a consolidation might feel “quiet.”
  • Programming the Mind for Expertise: NLP techniques like visualization, anchoring, and affirmations can be integrated into your backtesting routine.
    • Visualization: Imagine yourself executing trades flawlessly, adhering to your system with discipline and confidence.
    • Anchoring: Associate a specific touch or gesture with a successful trade or a state of focused trading. Trigger this anchor to instantly access that resourceful state.
    • Affirmations: Use positive affirmations like, “I am a disciplined trader,” or “I consistently follow my system,” to reinforce positive trading beliefs.
  • Accelerated Learning: NLP can help you learn from your backtesting experience more effectively.
    • Modeling: Study the charts of VIP Trading Playbook in the ATS VIP Group, and identify their patterns of decision-making. Integrate these patterns into your trading.
    • Meta-Modeling: Ask yourself powerful questions about your trading system, such as, “What are the key distinctions that make this signal successful?” or “How can I refine my entry to improve profitability?”
  • Emotional Mastery: Trading psychology is crucial. NLP provides techniques to manage emotions like fear and greed.
    • Reframing: Reframe negative thoughts or experiences during backtesting. For example, view a losing trade as a learning opportunity rather than a failure.
    • Dissociation: If you find yourself emotionally attached to a particular trade during backtesting, dissociate from the experience by viewing it as if it were happening to someone else. This helps you maintain objectivity.

Example: Integrating NLP into the USAR Trend Trading Strategy

Let’s revisit our previous example and incorporate NLP:

  1. Visual Acuity: As you scan the chart for long green signals, pay close attention to the state of the price action, indicators, volume accompanying, and the overall market context.
  2. Auditory Reinforcement Buy: When you spot a valid entry buy signal, say aloud, “Green Signal! long.” When exiting, say, “Reversal! Trade Exit long.”
  3. Auditory Reinforcement Sell: When you spot a valid entry sell signal, say aloud, “Red Signal! short.” When exiting, say, “Reversal! Trade Exit Short.”
  4. Kinesthetic Anchoring: As you record a winning trade in your spreadsheet, make a fist and say, “Winning trade!” Associate this gesture with the feeling of success.
  5. Visualization: Before each trading day (in your backtest), visualize yourself calmly analyzing the chart, identifying signals, and executing trades with precision.
  6. Affirmations: Repeat affirmations like, “I am a skilled trader who identifies high-probability setups.”

Tools for Manual Backtesting:

  • Historical Price Charts: Access to high-quality charts with various timeframes is essential.
  • Spreadsheet Software: A spreadsheet (e.g., Excel, Google Sheets) helps record trade details, calculate performance metrics, and analyze results.
  • Trading Journal: A journal to document observations, insights, and emotional responses during the backtesting process.

Steps to Manual Backtesting:

  1. Define Your Trading Strategy:
    • Systematic Chart Signals: Clearly define the chart patterns or indicators that trigger your entry and exit signals. Examples include:
      • Long Entry: Bullish engulfing pattern, breakout above resistance, signals indicators green signal long
      • Short Entry: Bearish engulfing pattern, breakdown below support, signals indicators red signal short
    • Trade Direction: Specify whether your strategy focuses on long trades (buy low, sell high), short trades (sell high, buy low), or both.
    • Entry Orders: Determine your order types for entering trades. Common types include:
      • Market Orders: Execute immediately at the current market price.
      • Limit Orders: Buy at a specified price or lower, or sell at a specified price or higher.
      • Stop Orders: Buy at a specified price or higher (stop-loss buy), or sell at a specified price or lower (stop-loss sell).
    • Exit Orders: Define your rules for exiting trades, including:
      • Profit Targets: Predetermined price levels at which to take profits.
      • Stop-Loss Orders: Orders to limit potential losses by exiting a trade at a predetermined price level.
      • Trailing Stops: Dynamic stop-loss orders that trail the price as the trade moves in your favor.
  2. Select Your Backtesting Period:
    • Choose a historical period that reflects the market conditions against which you want to test your strategy. Consider including periods of high volatility, low volatility, bull markets, and bear markets.
    • Ensure sufficient data points for statistically significant results. Aim for at least several months or years of data, depending on your trading frequency.
  3. Analyze Historical Charts:
    • Start by examining price charts for your chosen period.
    • Meticulously identify instances where your entry signals occur.
    • Mark your entry point on the chart based on your chosen entry order type.
    • Apply your exit strategy rules to determine your exit points and mark them on the chart.
    • Record the details of each trade (entry date, entry price, exit date, exit price) in your spreadsheet.
  4. Calculate Performance Metrics:
    • Winning Trades: Calculate the percentage of trades that resulted in a profit.
    • Losing Trades: Calculate the percentage of trades that resulted in a loss.
    • Average Win: Determine the average profit per winning trade.
    • Average Loss: Determine the average loss per losing trade.
    • Risk-Reward Ratio: Calculate the ratio of average win to average loss.
    • Maximum Drawdown: Calculate the largest percentage decline from a peak to a trough in your trading capital during the backtesting period.
    • Profit Factor: Calculate the ratio of gross profit to gross loss.
    • Sharpe Ratio: Measure risk-adjusted return, considering the strategy’s volatility.
  5. Analyze and Refine Your Strategy:
    • Identify Patterns: Analyze your trading journal and spreadsheet data to identify recurring patterns in winning and losing trades.
    • Refine Entry and Exit Rules: Adjust your entry and exit signals based on your analysis to improve performance.
    • Optimize Risk Management: Refine your stop-loss and profit target strategies to manage risk effectively.
    • Consider Market Conditions: Evaluate how your strategy performs in different market conditions and adjust accordingly.

Example: Backtesting an AFT USAR Signals Indicator

Let’s illustrate manual backtesting with a basic example:

  • Strategy: Long and short trades – Close and Reverse (CAR) – for each short or long, close out and enter the new trade.
  • Entry Signal Long: Green Signal buy
  • Exit Order: If the position was short, exit the short at the close of the signal bar.
  • Entry Order: Market order at the close of the signal bar.
  • Entry Signal Short: Red Signal Sell
  • Exit Order: If the position was long, exit the long at the close of the signal bar.
  • Entry Order: Market order at the close of the signal bar.

Backtesting Process using the AFT USAR:

  1. Select a stock and timeframe: E.g., Nasdaq Emini Futures (NQ) 120min chart from 2020 to 2023.
  2. Plot the USAR: Add the USAR to the chart and set the signal mode to 1.
  3. Rewind the chart: Scroll all the way to the left, then move forward 7 days or more to the next new whole week to stabilize the plots.
  4. Identify Signals Long: View each instance of a Green Signal on the chart via the cursor crosshair.
  5. NLP: SPEAK, POINT, HEAR, WRITE, UNDERSTAND the signal concept.
  6. Enter Long Trades: Record the entry date and price of the signal bar.
  7. Identify Signals Short: View each instance of a Red Signal on the chart via the cursor crosshair.
  8. NLP: SPEAK, POINT, HEAR, WRITE, UNDERSTAND the signal concept.
  9. Exit Long Trades: Record the exit date and price of the signal bar.
  10. Calculate Performance Metrics: Use the recorded trade data to calculate metrics like winning percentage, average win/loss, maximum drawdown, etc.

Tips for Effective Manual Backtesting:

  • Be Objective: Avoid biases and emotional attachments to your strategy.
  • Maintain Discipline: Strictly adhere to your defined rules throughout the process.
  • Document Everything: Keep detailed records of your trades and observations in your trading journal.
  • Start Simple: Begin with a basic strategy and gradually add complexity as you gain experience.
  • Practice Patience: Manual backtesting can be time-consuming, but the insights gained are invaluable.
  • NLP: SPEAK, POINT, HEAR, WRITE, UNDERSTAND, PROGRAM THE SUBCONSCIOUS WITH COMPETENCE!

Conclusion:

Manual backtesting provides a powerful framework for evaluating trading strategies and refining your approach to the markets. By diligently following the steps outlined in this guide, you can gain a deeper understanding of your system’s strengths and weaknesses, ultimately improving your trading performance and increasing your chances of success. While the process requires time and effort, the rewards in terms of knowledge and confidence are well worth the investment.

By integrating NLP techniques into your manual backtesting process, you create a powerful synergy that accelerates learning, enhances intuition, and improves emotional control. This holistic approach transforms backtesting from a mere analytical exercise into a transformative experience that programs your mind for trading success.

Within our Zero To Hero Program – Enter the Trading Matrix

  • Action Over Inaction: Program your trading mind through action, not endless questions, and procrastination.
  • No Need for Overanalysis: Don’t get bogged down in the “why” behind everything. Focus on what works.
  • The Holy Grail is a Myth: Forget searching for the perfect settings. Consistent profitability comes from mastering a system, not chasing magic formulas.
  • Chart Time is King: Ditch the endless books and internet theories. Real learning happens on the charts. Get screen time!
  • Embrace NLP: Sharpen your senses with NLP techniques. See, hear, draw, analyze, and document your observations.
  • Slow and Steady Wins the Race: Gradually add nuances and filters to your system. Don’t rush. Less is often more. Slow down to go faster.
  • Earn Your Optimization Stripes: Fine-tune settings only after you’ve developed a deep understanding of your system.
  • Market Mastery: Learn to read the market and identify high-probability trading opportunities. Sometimes, the best trade is no trade.
  • The Professional Mindset: Approach trading as a profession, not a gamble. Focus on mastery, not just money.
  • Success is Multifaceted: True success in trading goes beyond financial gains. It’s about discipline, skill, and continuous growth.
  • Rise Above the Average: Leave behind the losing mentality of the masses. Become an elite ATS Trader.

Why This Works:

  • Emphasis on Action: Trading is a skill that requires practice. This program encourages immediate action, which is crucial for developing competence.
  • Focus on Practicality: It cuts through the fluff and focuses on what truly matters: chart analysis, pattern recognition, and consistent execution.
  • NLP Integration: NLP techniques can significantly enhance a trader’s focus, emotional control, and learning process.
  • Long-Term Vision: It emphasizes the importance of patience, discipline, and continuous learning, which are essential for long-term success in trading.

This framework provides a solid foundation for aspiring traders to develop the skills and mindset needed to thrive in the markets.

Trader Failings and Trader Success

The main failing in trading is trader psychology. With NLP techniques, this can be reset and programmed for success over time with discipline and routine. Clear the mind—as funny as it might sound—try to become like the Bruce Lee of trading: Zen-like, with economy of mind and action! Clear the clutter and rubbish from the mind and make a straight line—the fastest, easiest way to the target. Reset and start at stage 1 and work to stage 4 onwards…with a plan, you can recite and see in your mind—your trading day and actions. Remove the human element from trading—become a stoic machine, ambivalent to lose or win. Focus on the process and being a pro! Get good at the game, and success will follow. By leveraging manual processes and routines, you can erase self-sabotage and negative trader psychology and replace it with a winning, machine-like trader mind! Our tools and methods fit with this ethos and are purpose-built for minimum drawdown, maximum profit—full control and success.

How to perform Network testing for a trading server?

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Network testing for a trading server requires a rigorous approach to ensure the lowest latency and highest reliability. Here’s a breakdown of the gold standard in testing methodologies and tools:

1. Key Performance Indicators (KPIs)

  • Latency: This is the most critical metric. Measure round-trip time (RTT) in microseconds (”s) for order submission and market data updates. Aim for consistently low latency with minimal jitter (variation in latency).
  • Jitter: Variations in latency can disrupt order execution timing. Measure jitter to identify inconsistencies in network performance.
  • Packet Loss: Lost packets can lead to missed orders or inaccurate market data. Measure packet loss as a percentage of total packets sent.
  • Throughput: Ensure sufficient bandwidth for peak trading volumes. Measure throughput in megabits or gigabits per second (Mbps or Gbps).
  • Error Rate: Monitor for network errors that can disrupt trading activity. Measure errors per second or as a percentage of total traffic.

2. Testing Tools & Techniques

  • Network Simulation Tools:
    • Hardware Simulators: Expensive but offer the most realistic testing environment. Examples include Spirent TestCenter and Ixia IxNetwork.
    • Software Simulators: More affordable and flexible. Examples include Packet Tracer, GNS3, and OPNET Modeler.
  • Dedicated Testing Environment: Replicate your production environment as closely as possible for accurate results. This includes network hardware, server configurations, and market data feeds.
  • Load Testing: Simulate peak trading conditions with high message rates and order volumes. Tools like LoadRunner or JMeter can be used.
  • Stress Testing: Push the network beyond its limits to identify breaking points and bottlenecks.
  • Failover Testing: Simulate network failures (e.g., link or switch failure) to verify redundancy and failover mechanisms.
  • Monitoring Tools: Use real-time network monitoring tools to identify performance issues during testing. Examples include SolarWinds Network Performance Monitor and PRTG Network Monitor.

3. Best Practices

  • Baseline Testing: Establish a performance baseline in a controlled environment.
  • Continuous Monitoring: Continuously monitor network performance during testing and in production.
  • Automated Testing: Automate tests to ensure consistency and repeatability.
  • Documentation: Thoroughly document test procedures, results, and analysis.

4. Specialized Considerations for Trading Servers

  • Co-location: If your server is co-located with an exchange, focus on testing connectivity to the exchange’s network.
  • Market Data Feeds: Test the performance of your market data feeds with tools like those from Vela Trading Technologies or Refinitiv.
  • Order Routing: Test the entire order routing path, from your trading application to the exchange and back.
  • Time Synchronization: Ensure accurate time synchronization using NTP (Network Time Protocol) servers.

5. Example Test Scenarios:

  • Latency under peak load: Simulate high message rates and order volumes to measure latency under stress.
  • Packet loss during market volatility: Simulate a period of high market volatility with rapidly changing prices to measure packet loss.
  • Failover to backup network link: Simulate a primary network link failure to verify failover to a backup link.

By following these guidelines, you can establish a gold standard for network testing your trading server, ensuring optimal performance, reliability, and successful trading outcomes.

Note: We performed many of the above tests when selecting QuantVPS as our preferred Trading Server providers.

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