Every experienced trader wants to execute the plan they see on the chart. You know your key levels, your risk, and how you’d scale in if the market moves your way. But traditional tools make that hard. DCA bots average down in fixed steps, GRID bots work only in ranges, and manual trading keeps you glued to the screen.

Custom Price Ladder gives you total control. You can pre-set exact entry prices, order sizes, and conditions.

A flexible way to predefine price levels and order sizes for scaling into a position. Unlike classic DCA (fixed spacing & multipliers), you choose where each order sits and how big it is—then adjust it visually on the chart. As orders fill, your WAP (weighted average price) updates in real time, and any %-based TP/SL tied to WAP adapts automatically.

Place levels below the base order to buy dips (mean reversion, range edges, demand zones).

Place levels above the base order to add into strength (averaging up on breakouts, higher-low retests, momentum continuations).

Mix both to handle range-to-trend transitions (pullback entries plus follow-through adds).

Size each level independently (equal, increasing, or decreasing) to control exposure and how quickly WAP moves.

Edit on the chart (drag & drop), type exact deviations, or use presets for fast setup; reorder levels and see live TP/SL/WAP readouts.

Combine with rules—e.g., require indicator confirmation, set min distance to TP/SL, and cap Max amount for bot usage.

Risk note: Custom ladders don’t remove risk, they let you shape it. Use a capital cap, define TP/SL/TSL/SLBE, respect exchange min order sizes, and backtest before scaling. In fast trends, averaging down can over-allocate; in choppy markets, averaging up can chase. Start simple, review fill rates and drawdowns, then iterate.

Best for mean-reversion or range conditions:

RSI oversold, volatility spike into a level

Goal: improve your average entry so you don’t need a full reversal to get green.

Higher highs / higher lows, strong momentum

Goal: compound winners—larger position as the trade proves itself.

Pro tip: Many traders overfund losers and underfund winners. Averaging up flips that—pay the market when it’s right.

Use when: price is oscillating inside a clear range.

TP: 1.3–2.0% with Multiple TPs (e.g., 40/40/20)

SL/TSL: SL under range low or use TSL after first TP

Why it works: improves WAP on dips and takes profits on revert-to-mean pops.

Example math: Base 100, add 1× at 98, 1.5× at 95.5, 2× at 93 → WAP drops fast; you need a smaller bounce to exit profitably.

2) Breakout + Retest Ladder (Averaging Up)

Use when: clear resistance breaks with volume and you want to join the trend after confirmation.

AOs (above): +0.8%, +1.6%, +2.5% (place at prior highs / intraday retests)

Sizes: 1×, 1×, 1.2× (keep risk contained)

TP: Trailing Take Profit on final tranche; partial TPs on earlier adds

SL/TSL: Initial protective SL under breakout level; switch to break-even / TSL after second fill

Why it works: avoids catching fakeouts, rewards trends and compounds strength.

3) Trend Pullback Ladder (Blend Down & Up)

Use when: strong uptrend, you buy pullbacks and also want to add if momentum resumes.

Base: at pullback into 20–50 EMA or trendline

AOs (below & above): −1.5%, −3.5%, +1%

TP: Multi-TP (e.g., 30/40/30) or TTP on last tranche

Why it works: you’re paid for both scenarios—deeper pullback or immediate continuation.

Equal size: simple, predictable drawdown profile.

Martingale (bigger deeper): fastest WAP improvement, highest max usage—cap your “Max funds used by bot.”

Anti-Martingale (bigger higher): ideal for averaging up, favors trends, limits exposure in chop.

Start with equal or light progressive sizing. Let your backtests tell you when to be more aggressive.

Max amount for bot usage: hard cap your downside.

Stop Loss vs. Trailing SL: SL for invalidation; TSL to protect winners.

SL Break-even (SLBE): after X profit/TPs, move risk off the table.

Conditional AOs: skip adds during sudden crashes; require indicator confirmation (e.g., RSI reclaim, MA cross).

TP: 1.6% (40%), 2.4% (40%), TSL on last 20%

TP: partials at +1.5% / +2.5%; TTP for runner

SL: under breakout line; SLBE after first TP

% of AOs filled (are you over- or under-stacked?)

Drawdown vs. Max funds used (is size safe?)

Profit factor & win rate (consistency vs. fat-tail wins)

ROI vs. PnL tradeoff (bigger capital can lift PnL while lowering ROI — normal)

It risks chasing if misused. Keep adds small & spaced, anchor risk with SL/TSL, and only add after confirmation (retest holds, higher low forms, volume supports).

Fewer, better-placed levels beat many random ones. 3–6 is a good working range.

Spot is simpler for learning. On futures, manage leverage, liquidation buffers, and margin usage tightly.

Classic DCA helps you survive. Custom Price Ladder helps you express your edge. Buy the dips you actually want and fund the trends that deserve it. Backtest, start small, and let the data shape your ladder.

Turn Price Levels on Your Chart into a Profit-Making Bot Setup

Please note that this blog aims to explain the theory and fundamentals behind margin trading and should not be considered financial or trading advice. All examples are hypothetical and do not promise or guarantee a positive return on investment. Margin trading can be very risky, and should be approached with caution. Professional traders typically recommend that newcomers start with small leverage to reduce risk while they learn. 

Margin trading can significantly boost your trading capacity by allowing you to borrow funds for investment, thus increasing potential returns. But what exactly is the margin? It's a security deposit that investors must provide to cover the credit risks posed when they engage in leveraged trading, such as borrowing cash for buying securities, selling stocks short, or entering derivative contracts.

Imagine you spot a trading opportunity where an asset priced at $1,000 per LOT is expected to soar to $1,500 tomorrow. With your $1,000, you could buy one LOT and make a $500 profit if your prediction is correct. But what if you could amplify that profit?

This is where margin trading shines. By opening a margin account and using your $1,000 as an initial margin, you could leverage to buy two LOTS worth $2,000, essentially doubling your potential profit to $1,000 (minus any applicable commissions). This approach not only maximizes your gains but also enhances your market impact.

To engage in margin trading, you'll need to open a margin account with your broker, which typically requires a minimum upfront payment. Once the account is active, you must maintain an initial margin—a percentage of the total trade value. For example, a 50% margin requirement on a $10,000 position means you need $5,000 in your account, borrowing the rest.

Additionally, there’s a maintenance margin to be aware of. This is the minimum balance you must keep to avoid a margin call, where you'd need to deposit more funds or sell some assets to cover the potential loan loss.

Margin accounts are also crucial for short selling—selling borrowed securities with the expectation of buying them back at a lower price. If you predict an asset's price drop from $1,000 to $500, you could short sell it by borrowing and immediately selling at the current higher price, then repurchase at the lower price, netting a profit after returning the borrowed shares.

Futures and options represent the backbone of derivative trading in margin accounts. For instance, buying a futures contract requires an initial margin, say $3,000, to cover potential losses, with a maintenance margin to ensure you can sustain the trade. These financial instruments allow for speculation on asset price movements and hedging against price volatility.

Financial derivatives, including futures, options, and swaps, derive their value from underlying assets like commodities or stocks. They can be traded on regulated exchanges or over-the-counter (OTC), offering flexibility and opportunities for both speculation and risk management.

Futures: Obligate you to buy or sell an asset at a set price on a future date, allowing speculation or hedging against price changes.

Options: Provide the right, but not the obligation, to buy (call option) or sell (put option) an asset at a predetermined price, offering more strategies for traders.

Swaps: Involve exchanging cash flows or other financial instruments to manage various financial risks.

Margin trading is not just about leveraging for higher profits; it’s also about efficiency and flexibility in managing your investments. Whether you're speculating on price movements or hedging to protect against losses, margin trading can be a powerful tool in your trading arsenal.

For a deeper dive into the mechanics and strategies of margin trading, including helpful visual explanations, check out this detailed video: Understanding Margin Trading.

Margin trading offers a pathway to enhance potential returns, manage risks, and capitalize on market opportunities. By understanding and leveraging the tools available, such as margin accounts and derivatives, traders at 3Commas can potentially navigate the markets more effectively and make more informed trading decisions. Whether you’re looking to amplify your gains or protect against potential losses, margin trading can be a critical component of your trading strategy.

Navigating Margin Trading in 2025: Key Developments for Professional Traders

As the crypto trading landscape evolves, professional traders and asset managers must adapt to new developments in margin trading. Here are several critical updates for 2025:

Regulatory Landscape
 Regulations have become more stringent, with authorities such as the UK's Financial Conduct Authority proposing a ban on retail investors borrowing funds to invest in digital assets. This underscores a broader global shift toward enhancing consumer protection and controlling systemic risk.

Technological Integration
 The widespread adoption of AI-powered trading tools has reshaped how margin strategies are implemented. Today, an AI crypto trading bot or an automated crypto trading bot can analyze market conditions and adjust leverage or stop-loss levels in real time. 3Commas, as a software provider, now offers advanced AI bot trading configurations that support both risk mitigation and strategy optimization.

Market Trends
 The approval of cryptocurrency ETFs has driven a surge in institutional interest. At the same time, the rise of ai based crypto trading bots and auto trading cryptocurrency software reflects a shift toward intelligent automation, where bots continuously adapt based on volatility and volume dynamics.

Risk Management Practices
 Managing downside risk is paramount. Traders are placing greater emphasis on features like trailing stop-losses, margin call alerts, and bot-based strategy simulations. Tools such as a crypto trading bot or ai crypto bots—particularly when integrated into automated crypto trading platforms—offer structured ways to test, execute, and refine margin strategies without emotion-driven decisions.

By aligning with regulatory developments and leveraging tools like ai bot crypto trading systems, professionals can manage exposure more effectively while maintaining agility in volatile markets. For those using 3Commas, access to advanced configuration of auto trade bot and altcoin trading bot features enhances margin trading workflows in 2025’s increasingly algorithm-driven ecosystem.

Explore the power of margin trading in 2025 with our comprehensive guide for 3Commas traders. Learn how to amplify your investments, manage risks, and maximize profits through leveraging, short selling, and understanding derivatives like futures and options. Perfect for both beginners and seasoned traders looking to expand their trading strategies.

Unleashing Potential with Margin Trading: A 2025 Primer for 3Commas Traders

Learn how you can potentially improve your decision-making

Unleashing Potential with Margin Trading: A Primer for 3Commas Traders

The rise of artificial intelligence in financial markets has reshaped the way trades are made, strategies are formed, and portfolios are managed. No longer reserved for hedge funds and institutional giants, AI-powered tools are now accessible to retail investors, crypto traders, and data-driven day traders alike. Among the most influential technologies leading this transformation is machine learning (ML)—a branch of AI designed to learn from historical data and uncover patterns that inform better trading decisions.

Machine learning doesn’t rely on rigid rules. Instead, it dynamically adapts to market changes, learning from both historical trends and real-time inputs. This has made ML an essential tool for developing stock trading bots that can make informed, real-time trading decisions, automate strategies, and help mitigate emotional bias in volatile markets.

In this complete guide, we’ll break down how machine learning is applied to stock and crypto trading, explore the most widely used prediction models, and explain how you can integrate AI-powered tools like 3Commas into your trading strategy. Whether you're building a custom bot or using existing automated trading software, understanding ML fundamentals will give you a competitive advantage in today’s fast-moving markets.

Understanding Machine Learning in Stock Trading

At its core, machine learning (ML) is a form of artificial intelligence that enables systems to learn patterns from data and make decisions without being explicitly programmed. In the context of stock and crypto trading, ML replaces rigid, rule-based logic with adaptive algorithms that improve with experience and data exposure.

There are three primary types of machine learning widely used in trading:

Supervised learning: Trained on historical, labeled data (e.g., price going up or down) to predict future outcomes. It's commonly used in binary classification tasks like forecasting market direction or identifying trade entry points.


Unsupervised learning: Finds patterns in data without labeled outcomes, useful for asset clustering, correlation analysis, and anomaly detection. This is particularly valuable in portfolio diversification strategies.


Reinforcement learning: Uses feedback from simulated or real trades to optimize strategies through trial and error. The model receives rewards or penalties and learns to maximize long-term returns. This is especially useful in high-frequency or algorithmic trading environments.


These learning types form the foundation of trading algorithms capable of adapting to evolving market conditions, identifying profitable opportunities, and optimizing execution timing.

Machine learning offers three major benefits for stock and crypto traders:

Superior Pattern Recognition: ML models can process massive volumes of historical and real-time data, uncovering insights far beyond the scope of human analysis. This allows traders to detect non-obvious patterns that could indicate emerging trends or potential reversals.


Dynamic Strategy Adaptation: As market conditions change, ML models update their predictions accordingly—ideal for volatile environments like crypto and equities. Traditional rule-based strategies often fall short in adapting to unforeseen scenarios, while ML models can recalibrate quickly based on new data.


Automated Execution at Scale: ML-powered trading bots can analyze, act, and execute trades automatically across multiple exchanges or asset classes. This dramatically reduces missed opportunities and removes the delays associated with manual execution.


Software providers like 3Commas further empower traders by combining AI-driven signals with flexible automation tools. This hybrid model ensures that both novice and experienced users can harness the power of machine learning without needing to build their own infrastructure.

AI vs. Machine Learning vs. Deep Learning

These terms often overlap in discussion, but they refer to different levels of capability:

Artificial Intelligence (AI) refers to the broader concept of machines simulating human intelligence, including reasoning, problem-solving, and decision-making.


Machine Learning (ML) is a subset of AI focused on learning from data and making predictions based on those patterns. ML models can evolve as they are exposed to more data, improving accuracy over time.


Deep Learning is a specialized form of ML that uses layered neural networks to recognize complex patterns and relationships, especially useful in high-frequency and sentiment-driven trading. Deep learning is particularly suited for interpreting large-scale unstructured data such as news articles, social media sentiment, or even candlestick chart images.


Understanding these distinctions is important when evaluating trading bots and software. While most stock trading bots use some form of ML, only a subset incorporates deep learning, which may offer advantages for specific strategies like sentiment analysis or real-time pattern recognition.

How Machine Learning Models Are Applied in Trading

Machine learning enables traders to build predictive models that analyze historical data and forecast potential market outcomes. These models vary in complexity and purpose, but most fall into four broad categories:

Classification models: These are used to determine categories, such as whether a stock’s price will rise or fall. Traders use classification models for directional bets and market sentiment decisions.


Regression models: These estimate numerical outcomes—like predicting the exact price of a stock or its expected return over a specific period.


Clustering models: These group assets that behave similarly based on historical correlations, helping traders identify diversification opportunities or isolate patterns.


Reinforcement learning models: These adapt strategies by receiving feedback in the form of rewards and penalties, enabling bots to self-optimize their trading behavior over time.


Each model type has distinct use cases, and experienced traders often combine several to form a more holistic strategy.

A variety of ML algorithms are used to power AI trading bots, each with strengths and ideal use cases:

Decision Trees and Random Forests: Great for non-linear decision-making. Random Forests aggregate the outputs of many trees, improving generalization and robustness against overfitting.


Support Vector Machines (SVM): Particularly effective in binary classification problems, SVMs are used when high-dimensional data must be separated into decision zones, such as trend/no trend.


Gradient Boosting Machines (GBM): XGBoost and LightGBM are popular due to their high accuracy in structured data. They work well in ranking trade opportunities or assigning probabilities to price movement.


Neural Networks and LSTM (Long Short-Term Memory): Essential for processing time-series data. LSTM networks, which retain memory across sequential data, are used to predict momentum shifts and volatility spikes in both stock and crypto markets.


Choosing the right algorithm depends on the type of data, the frequency of trading, and whether interpretability or predictive power is the priority.

Real-World Example: LSTM Models in Crypto Forecasting

A crypto trader focusing on high-frequency trading might use an LSTM model trained on 1-minute BTC/USDT price data and volume profiles. By identifying acceleration in momentum and unusual volume surges, the model can signal potential breakouts. Combined with automated execution via 3Commas bots, this strategy can capitalize on micro-trends before they appear on standard indicators.

Key Benefits of Machine Learning for Stock Traders

Machine learning helps traders refine their existing strategies or develop entirely new ones. Instead of relying solely on fixed technical indicators, ML systems continuously ingest new data and adjust execution plans in real time. This enables:

More adaptive scalping or day trading strategies


Identification of low-risk, high-reward setups based on thousands of variables

Machine learning doesn’t just replace human intuition—it complements it by validating hypotheses with statistical rigor and reducing reliance on subjective decision-making.

AI models can continuously analyze streaming market data, identify pricing anomalies, and generate trade alerts in milliseconds. Real-time analysis is critical when trading fast-moving assets such as small-cap stocks or volatile crypto pairs. With machine learning, traders can:

Detect liquidity imbalances across order books


Adjust dynamically to shifts in bid/ask spreads

React faster to news or sentiment-driven volatility


By integrating real-time data feeds with machine learning engines, traders unlock a powerful toolset for market responsiveness that traditional technical tools simply can’t match.

Automated Trading and Reduced Emotional Bias

Emotion often leads to poor trading decisions. AI stock trading bots operate without fear, greed, or hesitation. They:

Maintain consistency across volatile markets


3Commas allows traders to deploy bots with machine learning logic while setting strict risk rules, such as max drawdowns or dynamic stop-losses, ensuring trades are executed systematically even during chaotic conditions.


A fully functional AI trading bot consists of three major components:

Data ingestion: Collects real-time and historical market data


Model prediction engine: Processes data through machine learning models to generate trading signals


Execution system: Executes trades via exchange APIs or brokerage accounts

Advanced bots also include:

Risk management modules for dynamic lot sizing and drawdown protection


Feedback loops for ongoing learning and strategy refinement


Logging systems for performance review and auditing


Traders validate their ML models through backtesting, which simulates how the model would have performed using historical data. But backtesting is only a start. To ensure robustness:

Use walk-forward testing to avoid overfitting


Transition to paper trading in live markets to simulate performance without financial risk


3Commas supports paper trading with full bot functionality, allowing users to evaluate strategies before real capital is involved.

3Commas offers a bot marketplace where users can:

Discover pre-built trading bots powered by ML or traditional logic


Clone successful bots and tweak inputs like capital allocation or signal thresholds


Combine strategies across asset classes (e.g., DCA bots on equities, grid bots on altcoins)


Traders can also deploy custom bots using 3Commas' API, integrating their own ML models from Python or cloud environments. This flexibility makes it easier to scale up or personalize automated trading workflows.


Effective machine learning depends on diverse, high-quality data. Common sources include:

OHLCV (Open/High/Low/Close/Volume) pricing data


Technical indicators (RSI, MACD, Bollinger Bands)


Social media sentiment and news headlines


The more varied the data sources, the more robust the model becomes in identifying trading signals that span multiple market conditions.

Data Preprocessing and Feature Engineering

Before feeding data into an ML model, it must be cleaned and structured. This includes:

Creating new features (e.g., moving averages, volatility bands, price ratios)


Normalizing or standardizing feature scales


Effective feature engineering can dramatically improve model accuracy. For example, instead of feeding raw prices, feeding percent change over multiple timeframes can help models learn trend persistence.

Example: Feature Engineering for Crypto Bots

A trader building an ML bot for ETH/USDT might engineer features like:

24-hour sentiment score from Reddit and Twitter


Master machine learning in stock trading with our comprehensive 2025 guide. Learn about AI prediction models, algorithms, and how to use them for smarter trading decisions and automation.

Machine Learning in Stock Trading: AI Prediction Models Guide 2025

Explore the complete guide to machine learning in stock trading. Learn about AI prediction models, how they work, and how to integrate them into your trading strategy for enhanced decision-making and automated execution.

Machine Learning in Stock Trading: Complete Guide to AI Prediction Models

The following blog is based on a recent interview with one of our top traders, an Indonesian gentleman who goes by @hunterwhalehunter. Some of the answers are edited slightly for clarity. 

Although my background in trading isn't extensive and I don't identify as a technical trader (TA), my journey in stocks and crypto has been quite fascinating. My initial foray into the market involved holding positions rather than active trading. I was just a regular worker investing my monthly salary into the market. However, a friend of mine, who quit his job to trade full-time, sparked my interest in trading.

When I began learning about trading, it felt like gambling, which clashed with my primary principle of investing for retirement savings. Traditional TA trading, where you buy on an upward breakout and sell on a downward breakout, was completely different from my approach. Despite this, I learned valuable concepts like stop-losses and trading signals from my friend. However, my ambition was to profit from trading using my own methods.

I realized that prices generally move sideways within a historical range. This led me to the idea of spreading my money across all price points. Initially, managing numerous limit orders manually was overwhelming, but the results were satisfying. This experience inspired me to explore trading bots. Having used bots for gaming in school, I wondered if they could be used for trading. After some research, I found trading bots and have been using them ever since.

Before entering the crypto market, I traded stocks. Stocks offered more stable gains compared to the rapid rise of Bitcoin. My stock portfolio included Visa, Mastercard, Honda, and Samsung. Over time, I realized that stocks like Honda and Samsung, with their price fluctuations, were better suited for passive income trading rather than retirement savings. This led me to sell my stocks and focus on Bitcoin, which I believed was better for long-term investment.

I began using trading bots in 2015, initially with Haasonline and later with 3Commas. The decision to switch to 3Commas was due to its affordability compared to Haasonline. Despite my extensive market experience since 2007 in stocks and 2012 in crypto, my hands-on trading experience with TA was limited. My approach to using bots is unique because it aligns with my principle of generating passive income rather than gambling.

I develop and test my strategies using real capital instead of demo accounts. Observing the outcomes over three months, I learned that patience and time are crucial for passive income. My strategy is simple yet effective: setting up a system that ensures consistent profits.

I monitor my trading bots through direct exchange checks and Telegram notifications. If a bot underperforms, I simply delete it. This routine has become second nature over the years, ensuring my strategy remains effective without frequent adjustments.

Using trading bots has significantly changed my trading routine. Bots save time and monitor the market 24/7, unlike manual trading, which is time-consuming. Bots help manage emotional risks, keeping fear and greed in check, allowing me to stick to my strategy without emotional interference.


I utilize the 3Commas Dashboard to manage my trading activities, check unrealized profits and losses, and make bulk edits to my bots. This tool enhances my trading efficiency and effectiveness.

I hold Bitcoin indefinitely and continue to accumulate more, believing in its unmatched potential as an asset.

For those new to trading bots, I recommend setting long and short bots with a wide range and playing all pairs. Avoid frequently editing or deleting bots to maintain accurate bookkeeping.

Despite my success, the drive to keep trading stems from the belief that there's always more to achieve financially.

Berikut adalah blog berdasarkan wawancara terbaru dengan salah satu trader terbaik kami, seorang pria Indonesia yang dikenal dengan nama @hunterwhalehunter. Beberapa jawabannya telah diedit sedikit untuk kejelasan.

Meskipun latar belakang saya dalam trading tidak begitu luas dan saya tidak mengidentifikasi diri sebagai seorang trader teknikal (TA), perjalanan saya di dunia saham dan kripto cukup menarik. Awalnya, saya lebih banyak memegang posisi daripada melakukan trading aktif. Saya hanyalah pekerja biasa yang menginvestasikan gaji bulanan ke market. Namun, seorang teman yang berhenti bekerja untuk trading penuh waktu, memicu minat saya untuk trading.

Ketika saya mulai belajar tentang trading, rasanya seperti berjudi, yang bertentangan dengan prinsip utama saya dalam berinvestasi untuk tabungan pensiun. Trading TA tradisional, di mana Anda membeli saat breakout naik dan menjual saat breakout turun, sangat berbeda dengan pendekatan saya. Meskipun demikian, saya belajar konsep-konsep berharga seperti stop-loss dan sinyal trading dari teman saya. Namun, ambisi saya adalah mendapatkan keuntungan dari trading dengan metode saya sendiri.

Saya menyadari bahwa harga umumnya bergerak sideways dalam rentang historis. Ini membawa saya pada ide untuk menyebarkan uang saya di semua titik harga. Awalnya, mengelola banyak order limit secara manual sangat melelahkan, tetapi hasilnya memuaskan. Pengalaman ini menginspirasi saya untuk menjelajahi trading bot. Setelah menggunakan bot untuk bermain game di sekolah, saya bertanya-tanya apakah mereka bisa digunakan untuk trading. Setelah melakukan riset, saya menemukan trading bot dan telah menggunakannya sejak saat itu.

Sebelum masuk ke market kripto, saya trading saham. Saham menawarkan keuntungan yang lebih stabil dibandingkan dengan kenaikan cepat Bitcoin. Portofolio saham saya termasuk Visa, Mastercard, Honda, dan Samsung. Seiring waktu, saya menyadari bahwa saham seperti Honda dan Samsung, dengan fluktuasi harganya, lebih cocok untuk trading pendapatan pasif daripada tabungan pensiun. Ini mendorong saya untuk menjual saham saya dan fokus pada Bitcoin, yang saya percaya lebih baik untuk investasi jangka panjang.


Saya mulai menggunakan trading bot pada tahun 2015, awalnya dengan Haasonline dan kemudian dengan 3Commas. Keputusan untuk beralih ke 3Commas disebabkan oleh biayanya yang lebih terjangkau dibandingkan Haasonline. Meskipun pengalaman pasar saya luas sejak tahun 2007 di saham dan 2012 di kripto, pengalaman langsung saya dengan TA terbatas. Pendekatan saya menggunakan bot unik karena sesuai dengan prinsip saya untuk menghasilkan pendapatan pasif daripada berjudi.


Pendekatan Langsung untuk Pengujian Strategi

Saya mengembangkan dan menguji strategi saya menggunakan modal nyata, bukan akun demo. Dengan mengamati hasilnya selama tiga bulan, saya belajar bahwa kesabaran dan waktu sangat penting untuk pendapatan pasif. Strategi saya sederhana namun efektif: membuat sistem yang memastikan keuntungan konsisten.

Saya memantau trading bot saya melalui pemeriksaan langsung di exchange dan notifikasi Telegram. Jika sebuah bot berkinerja buruk, saya cukup menghapusnya. Rutinitas ini sudah menjadi kebiasaan selama bertahun-tahun, memastikan strategi saya tetap efektif tanpa sering melakukan penyesuaian.

Menggunakan trading bot telah secara signifikan mengubah rutinitas trading saya. Bot menghemat waktu dan memantau market 24/7, berbeda dengan trading manual yang memakan waktu. Bot membantu mengelola risiko emosional, menjaga ketakutan dan keserakahan tetap terkendali, memungkinkan saya untuk tetap berpegang pada strategi tanpa gangguan emosional.

Saya menggunakan Dashboard 3Commas untuk mengelola aktivitas trading saya, memeriksa keuntungan dan kerugian yang belum direalisasi, dan melakukan pengeditan massal pada bot saya. Alat ini meningkatkan efisiensi dan efektivitas trading saya.

Saya memegang Bitcoin tanpa batas waktu dan terus mengakumulasi lebih banyak, percaya pada potensinya yang tak tertandingi sebagai aset.

Bagi mereka yang baru mengenal trading bot, saya merekomendasikan untuk mengatur bot long dan short dengan rentang yang luas dan memainkan semua pair. Hindari sering mengedit atau menghapus bot untuk menjaga pembukuan yang akurat.

Meskipun sukses, dorongan untuk terus trading berasal dari keyakinan bahwa selalu ada lebih banyak yang bisa dicapai secara finansial.

Discover the journey of a long-term investor transitioning from traditional stock holding to automated crypto trading with bots. Learn how to develop effective trading strategies, manage risk, and maximize profits using 3Commas trading bots. Gain insights into the differences between stock and crypto markets and get valuable tips for beginners to avoid common mistakes and achieve financial success. Perfect for those looking to enhance their trading efficiency and secure a profitable passive income.

How to win with trading bots: An interview with a long-term investor

From Holding to Automated Trading: A Journey of Discovery

How to strategically use trading bots: An interview with a long-term investor

3Commas offers a variety of grid bot strategies designed to cater to different market conditions and trading preferences. These strategies, including Rising, Stable, To the Moon, Reversal, and Falling, provide users with automated tools to maximize their trading potential. Let's delve into each of these strategies in detail.

The Rising strategy is designed for markets with an upward trend. It uses more buy orders than sell orders, setting the High price closer to the current price. This setup allows trading to start with a smaller amount of base currency and minimizes the risk of significant loss during sudden price drops.

The Rising strategy is ideal for traders who anticipate a continuous upward trend in the market. By leveraging the Trailing Up function, this strategy allows for consistent trading even as the market price rises beyond the initially configured range. This adaptability makes it a popular choice for bullish markets.

- Trailing Up Function: Enables the bot to continue trading when the price exceeds the initially configured range.

- Automatic Adjustment: The bot creation form will increase the number of lines, primarily for buy orders, based on the available balance of the chosen pair.

The Stable strategy is best suited for trading in a sideways market with high volatility within a stable channel. It maintains fixed High and Low prices for the bot's trading range.

The Stable strategy works well in markets where prices fluctuate within a defined range without significant upward or downward trends. It capitalizes on the repetitive price movements within this range, making it an excellent choice for profiting in a stable, volatile market.

- Fixed Trading Range: High and Low prices are set to maintain a stable trading channel.

- Automatic Adjustment: The number of lines will be adjusted based on the available balance.

Discover how to leverage 3Commas Grid Bots in your 2025 trading strategy. Learn about Rising, Stable, To the Moon, Reversal, and Falling strategies to maximize your trading potential and profits. Read now!

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