Get started

Algo

How to Trade Gold with AI-Powered Algos in 2025📊 How to Trade Gold with AI-Powered Algos in 2025 A practical action plan for serious gold traders 🔍 1. Know Why Gold Requires Custom Algo Tactics Gold is volatile, news-sensitive, and driven by macro events like Fed policy, geopolitics, and inflation. Generic stock or crypto bots fail here — gold needs precise, event-aware automation. 🧠 2. Use AI-Powered Bots Trained for Gold Volatility Deploy bots that adapt to real-time data like CPI releases, bond yields, and geopolitical headlines. Use machine learning models that detect gold breakouts, consolidations, and safe-haven flows. Top AI algos for gold traders: Multiple systems based on MT4/MT5 Fully-automated, AI-based gold bot with breakout detection, precision entries, and built-in risk control. ⚙️ 3. Build or Choose the Right Algo Strategy for Gold Trend-Following: Use 21/50 EMA crosses on H1 and H4 Mean Reversion: Bollinger Band fades in range-bound sessions Breakout Algos: Trigger trades on CPI or FOMC event volatility Volume-Based AI: Analyze volume spikes vs. historical patterns 🧪 4. Backtest Gold-Specific Models Always test your bot using historical gold data, especially during NFP weeks, Fed meetings, and geopolitical escalations. Use data from 2018 to 2024 for high-volatility periods. Tools: TradingView for Pine Script testing, MetaTrader 5 for EA deployment 🛡️ 5. Control Risk with Gold-Specific Parameters Max drawdown: Keep under 15 percent Stop-loss: Always use hard stops (not just trailing) Position sizing: 0.5 to 1 percent of capital per trade Use volatility filters: Avoid entries during thin liquidity hours 🔄 6. Automate Monitoring and Adaptation Run multiple bots for breakout, momentum, and reversal setups Use dashboards to track gold-specific metrics like VIX, USDX, DXY, and 10Y Treasury yields Integrate AI that adjusts parameters after major data releases 🚀 7. Prepare for 2025 Market Structure Gold is increasingly driven by Central bank digital currency rollouts USD de-dollarization risks Global stagflation or recession themes DeFi and tokenized gold products Your algo must factor in these macro narratives using real-time data feeds 📌 Gold Algo Trading Success Plan 2025 Use AI bots built for gold volatility Trade high-probability breakouts post-news Backtest with gold-specific macro filters Maintain strict risk limits with max 15 percent drawdown Monitor global news and macro data with bot triggers Continuously optimize and adapt Gold is not just a commodity — it’s a signal of global risk. Automate smartly, manage risk tightly, and use AI to stay one move ahead.
OANDA:XAUUSDEducation
by ProjectSyndicate
27
Algorithmic vs. Manual Trading - Which Strategy Reigns SupremeIntro: In the dynamic world of financial markets, trading strategies have evolved significantly over the years. With advancements in technology and the rise of artificial intelligence (AI), algorithmic trading, also known as algo trading, has gained immense popularity. Algo trading utilizes complex algorithms and automated systems to execute trades swiftly and efficiently, offering numerous advantages over traditional manual trading approaches. In this article, we will explore the advantages and disadvantages of algo trading compared to manual trading, providing a comprehensive overview of both approaches. We will delve into the speed, efficiency, emotion-free decision making, consistency, scalability, accuracy, backtesting capabilities, risk management, and diversification offered by algo trading. Additionally, we will discuss the flexibility, adaptability, intuition, experience, emotional intelligence, and creative thinking that manual trading brings to the table. Advantages of Algo trading: Speed and Efficiency: One of the primary advantages of algo trading is its remarkable speed and efficiency. With algorithms executing trades in milliseconds, algo trading eliminates the delays associated with manual trading. This speed advantage enables traders to capitalize on fleeting market opportunities and capture price discrepancies that would otherwise be missed. By swiftly responding to market changes, algo trading ensures that traders can enter and exit positions at optimal prices. Emotion-Free Decision Making: Humans are prone to emotional biases, which can cloud judgment and lead to irrational investment decisions. Algo trading removes these emotional biases by relying on pre-programmed rules and algorithms. The algorithms make decisions based on logical parameters, objective analysis, and historical data, eliminating the influence of fear, greed, or other human emotions. As a result, algo trading enables more disciplined and objective decision-making, ultimately leading to better trading outcomes. Consistency: Consistency is a crucial factor in trading success. Algo trading provides the advantage of maintaining a consistent trading approach over time. The algorithms follow a set of predefined rules consistently, ensuring that trades are executed in a standardized manner. This consistency helps traders avoid impulsive decisions or deviations from the original trading strategy, leading to a more disciplined approach to investing. Enhanced Scalability: Traditional manual trading has limitations when it comes to scalability. As trade volumes increase, it becomes challenging for traders to execute orders efficiently. Algo trading overcomes this hurdle by automating the entire process. Algorithms can handle a high volume of trades across multiple markets simultaneously, ensuring scalability without compromising on execution speed or accuracy. This scalability empowers traders to take advantage of diverse market opportunities without any operational constraints. Increased Accuracy: Algo trading leverages the power of technology to enhance trading accuracy. The algorithms can analyze vast amounts of market data, identify patterns, and execute trades based on precise parameters. By eliminating human error and subjectivity, algo trading increases the accuracy of trade execution. This improved accuracy can lead to better trade outcomes, maximizing profits and minimizing losses. Backtesting Capabilities and Optimization: Another significant advantage of algo trading is its ability to backtest trading strategies. Algorithms can analyze historical market data to simulate trading scenarios and evaluate the performance of different strategies. This backtesting process helps traders optimize their strategies by identifying patterns or variables that generate the best results. By fine-tuning strategies before implementing them in live markets, algo traders can increase their chances of success. Automated Risk Management: Automated Risk Management: Managing risk is a critical aspect of trading. Algo trading offers automated risk management capabilities that can be built into the algorithms. Traders can program specific risk parameters, such as stop-loss orders or position sizing rules, to ensure that losses are limited and positions are appropriately managed. By automating risk management, algo trading reduces the reliance on manual monitoring and helps protect against potential market downturns. Diversification: Diversification: Algo trading enables traders to diversify their portfolios effectively. With algorithms capable of simultaneously executing trades across multiple markets, asset classes, or strategies, traders can spread their investments and reduce overall risk. Diversification helps mitigate the impact of individual market fluctuations and can potentially enhance long-term returns. Removal of Emotional Biases: Finally, algo trading eliminates the influence of emotional biases that often hinder trading decisions. Fear, greed, and other emotions can cloud judgment and lead to poor investment choices. Byrelying on algorithms, algo trading removes these emotional biases from the decision-making process. This objective approach helps traders make more rational and data-driven decisions, leading to better overall trading performance. Disadvantage of Algo Trading System Vulnerabilities and Risks: One of the primary concerns with algo trading is system vulnerabilities and risks. Since algo trading relies heavily on technology and computer systems, any technical malfunction or system failure can have severe consequences. Power outages, network disruptions, or software glitches can disrupt trading operations and potentially lead to financial losses. It is crucial for traders to have robust risk management measures in place to mitigate these risks effectively. Technical Challenges and Complexity: Technical Challenges and Complexity: Algo trading involves complex technological infrastructure and sophisticated algorithms. Implementing and maintaining such systems require a high level of technical expertise and resources. Traders must have a thorough understanding of programming languages and algorithms to develop and modify trading strategies. Additionally, monitoring and maintaining the infrastructure can be challenging and time-consuming, requiring continuous updates and adjustments to keep up with evolving market conditions. Over-Optimization: Another disadvantage of algo trading is the risk of over-optimization. Traders may be tempted to fine-tune their algorithms excessively based on historical data to achieve exceptional past performance. However, over-optimization can lead to a phenomenon called "curve fitting," where the algorithms become too specific to historical data and fail to perform well in real-time market conditions. It is essential to strike a balance between optimizing strategies and ensuring adaptability to changing market dynamic Over Reliance on Historical Data: Algo trading heavily relies on historical data to generate trading signals and make decisions. While historical data can provide valuable insights, it may not always accurately reflect future market conditions. Market dynamics, trends, and relationships can change over time, rendering historical data less relevant. Traders must be cautious about not relying solely on past performance and continuously monitor and adapt their strategies to current market conditions. Lack of Adaptability: Another drawback of algo trading is its potential lack of adaptability to unexpected market events or sudden changes in market conditions. Algo trading strategies are typically based on predefined rules and algorithms, which may not account for unforeseen events or extreme market volatility. Traders must be vigilant and ready to intervene or modify their strategies manually when market conditions deviate significantly from the programmed rules. Advantages of Manual Trading Flexibility and Adaptability: Manual trading offers the advantage of flexibility and adaptability. Traders can quickly adjust their strategies and react to changing market conditions in real-time. Unlike algorithms, human traders can adapt their decision-making process based on new information, unexpected events, or emerging market trends. This flexibility allows for agile decision-making and the ability to capitalize on evolving market opportunities. Intuition and Experience: Human traders possess intuition and experience, which can be valuable assets in the trading process. Through years of experience, traders develop a deep understanding of the market dynamics, patterns, and interrelationships between assets. Intuition allows them to make informed judgments based on their accumulated knowledge and instincts. This human element adds a qualitative aspect to trading decisions that algorithms may lack. Complex Decision-making: Manual trading involves complex decision-making that goes beyond predefined rules. Traders analyze various factors, such as fundamental and technical indicators, economic news, and geopolitical events, to make well-informed decisions. This ability to consider multiple variables and weigh their impact on the market enables traders to make nuanced decisions that algorithms may overlook. Emotional Intelligence and Market Sentiment: Humans possess emotional intelligence, which can be advantageous in trading. Emotions can provide valuable insights into market sentiment and investor psychology. Human traders can gauge market sentiment by interpreting price movements, news sentiment, and market chatter. Understanding and incorporating market sentiment into decision-making can help traders identify potential market shifts and take advantage of sentiment-driven opportunities. Contextual Understanding: Manual trading allows traders to have a deep contextual understanding of the markets they operate in. They can analyze broader economic factors, political developments, and industry-specific dynamics to assess the market environment accurately. This contextual understanding provides traders with a comprehensive view of the factors that can influence market movements, allowing for more informed decision-making. Creative and Opportunistic Thinking: Human traders bring creative and opportunistic thinking to the trading process. They can spot unique opportunities that algorithms may not consider. By employing analytical skills, critical thinking, and out-of-the-box approaches, traders can identify unconventional trading strategies or undervalued assets that algorithms may overlook. This creative thinking allows traders to capitalize on market inefficiencies and generate returns. Complex Market Conditions: Manual trading thrives in complex market conditions that algorithms may struggle to navigate. In situations where market dynamics are rapidly changing, volatile, or influenced by unpredictable events, human traders can adapt quickly and make decisions based on their judgment and expertise. The ability to think on their feet and adjust strategies accordingly enables traders to navigate challenging market conditions effectively. Disadvantage of Manual Trading Emotional Bias: Algo trading lacks human emotions, which can sometimes be a disadvantage. Human traders can analyze market conditions based on intuition and experience, while algorithms solely rely on historical data and predefined rules. Emotional biases, such as fear or greed, may play a role in decision-making, but algorithms cannot factor in these nuanced human aspects. Time and Effort: Implementing and maintaining algo trading systems require time and effort. Developing effective algorithms and strategies demands significant technical expertise and resources. Traders need to continuously monitor and update their algorithms to ensure they remain relevant in changing market conditions. This ongoing commitment can be time-consuming and may require additional personnel or technical support. Execution Speed: While algo trading is known for its speed, there can be challenges with execution. In fast-moving markets, delays in order execution can lead to missed opportunities or less favorable trade outcomes. Algo trading systems need to be equipped with high-performance infrastructure and reliable connectivity to execute trades swiftly and efficiently. Information Overload: In today's digital age, vast amounts of data are available to traders. Algo trading systems can quickly process large volumes of information, but there is a risk of information overload. Filtering through excessive data and identifying relevant signals can be challenging. Traders must carefully design algorithms to focus on essential information and avoid being overwhelmed by irrelevant or noisy data. The Power of AI in Enhancing Algorithmic Trading: Data Analysis and Pattern Recognition: AI algorithms excel at processing vast amounts of data and recognizing patterns that may be difficult for human traders to identify. By analyzing historical market data, news, social media sentiment, and other relevant information, AI-powered algorithms can uncover hidden correlations and trends. This enables traders to develop more robust trading strategies based on data-driven insights. Predictive Analytics and Forecasting: AI algorithms can leverage machine learning techniques to generate predictive models and forecasts. By training on historical market data, these algorithms can identify patterns and relationships that can help predict future price movements. This predictive capability empowers traders to anticipate market trends, identify potential opportunities, and adjust their strategies accordingly. Real-time Market Monitoring: AI-based systems can continuously monitor real-time market data, news feeds, and social media platforms. This enables traders to stay updated on market developments, breaking news, and sentiment shifts. By incorporating real-time data into their algorithms, traders can make faster and more accurate trading decisions, especially in volatile and rapidly changing market conditions. Adaptive and Self-Learning Systems: AI algorithms have the ability to adapt and self-learn from market data and trading outcomes. Through reinforcement learning techniques, these algorithms can continuously optimize trading strategies based on real-time performance feedback. This adaptability allows the algorithms to evolve and improve over time, enhancing their ability to generate consistent returns and adapt to changing market dynamics. Enhanced Decision Support: AI algorithms can provide decision support tools for traders, presenting them with data-driven insights, risk analysis, and recommended actions. By combining the power of AI with human expertise, traders can make more informed and well-rounded decisions. These decision support tools can assist in portfolio allocation, trade execution, and risk management, enhancing overall trading performance. How Algorithmic Trading Handles News and Events? In the fast-paced world of financial markets, news and events play a pivotal role in driving price movements and creating trading opportunities. Algorithmic trading has emerged as a powerful tool to capitalize on these dynamics. Automated News Monitoring: Algorithmic trading systems are equipped with the capability to automatically monitor news sources, including financial news websites, press releases, and social media platforms. By utilizing natural language processing (NLP) and sentiment analysis techniques, algorithms can filter through vast amounts of news data, identifying relevant information that may impact the market. Real-time Data Processing: Algorithms excel in processing real-time data and swiftly analyzing its potential impact on the market. By integrating news feeds and other event-based data into their models, algorithms can quickly evaluate the relevance and potential market significance of specific news or events. This enables traders to react promptly to emerging opportunities or risks. Event-driven Trading Strategies: Algorithmic trading systems can be programmed to execute event-driven trading strategies. These strategies are designed to capitalize on the market movements triggered by specific events, such as economic releases, corporate earnings announcements, or geopolitical developments. Algorithms can automatically scan for relevant events and execute trades based on predefined criteria, such as price thresholds or sentiment analysis outcomes. Sentiment Analysis: Sentiment analysis is a crucial component of news and event-based trading. Algorithms can analyze news articles, social media sentiment, and other textual data to assess market sentiment surrounding a specific event or news item. By gauging positive or negative sentiment, algorithms can make informed trading decisions and adjust strategies accordingly. Backtesting and Optimization: Algorithmic trading allows for backtesting and optimization of news and event-driven trading strategies. Historical data can be used to test the performance of trading models under various news scenarios. By analyzing the past market reactions to similar events, algorithms can be fine-tuned to improve their accuracy and profitability. Algorithmic News Trading: Algorithmic news trading involves the automatic execution of trades based on predefined news triggers. For example, algorithms can be programmed to automatically buy or sell certain assets when specific news is released or when certain conditions are met. This automated approach eliminates the need for manual monitoring and ensures swift execution in response to news events. Risk Management: Algorithmic trading systems incorporate risk management measures to mitigate the potential downside of news and event-driven trading. Stop-loss orders, position sizing algorithms, and risk management rules can be integrated to protect against adverse market movements or unexpected news outcomes. This helps to minimize losses and ensure controlled risk exposure. Flash Crash 2010: A Historic Market Event On May 6, 2010, the financial markets experienced an unprecedented event known as the "Flash Crash." Within a matter of minutes, stock prices plummeted dramatically, only to recover shortly thereafter. This sudden and extreme market turbulence sent shockwaves through the financial world and highlighted the vulnerabilities of an increasingly interconnected and technology-driven trading landscape. The Flash Crash Unfolds: On that fateful day, between 2:32 p.m. and 2:45 p.m. EDT, the U.S. stock market experienced an abrupt and severe decline in prices. Within minutes, the Dow Jones Industrial Average (DJIA) plunged nearly 1,000 points, erasing approximately $1 trillion in market value. Blue-chip stocks, such as Procter & Gamble and Accenture, saw their prices briefly crash to a mere fraction of their pre-crash values. This sudden and dramatic collapse was followed by a swift rebound, with prices largely recovering by the end of the trading session. The Contributing Factors: Several factors converged to create the perfect storm for the Flash Crash. One key element was the increasing prevalence of high-frequency trading (HFT), where computer algorithms execute trades at lightning-fast speeds. This automated trading, combined with the interconnectedness of markets, exacerbated the speed and intensity of the crash. Additionally, the widespread use of stop-loss orders, which are triggered when a stock reaches a specified price, amplified the selling pressure as prices rapidly declined. A lack of adequate market safeguards and regulatory mechanisms further exacerbated the situation. Role of Algorithmic Trading: Algorithmic trading played a significant role in the Flash Crash. As the markets rapidly declined, certain algorithmic trading strategies failed to function as intended, exacerbating the sell-off. These algorithms, designed to capture small price discrepancies, ended up engaging in a "feedback loop" of selling, pushing prices even lower. The speed and automation of algorithmic trading made it difficult for human intervention to effectively mitigate the situation in real-time. Market Reforms and Lessons Learned: The Flash Crash of 2010 prompted significant regulatory and technological reforms aimed at preventing similar events in the future. Measures included the implementation of circuit breakers, which temporarily halt trading during extreme price movements, and revisions to market-wide circuit breaker rules. Market surveillance and coordination between exchanges and regulators were also enhanced to better monitor and respond to unusual trading activity. Additionally, the incident highlighted the need for greater transparency and scrutiny of algorithmic trading practices. Implications for Market Stability: The Flash Crash served as a wake-up call to market participants and regulators, underscoring the potential risks associated with high-frequency and algorithmic trading. It highlighted the importance of ensuring that market infrastructure and regulations keep pace with technological advancements. The incident also emphasized the need for market participants to understand the intricacies of the trading systems they employ, and for regulators to continually evaluate and adapt regulatory frameworks to address emerging risks. The Flash Crash of 2010 stands as a pivotal moment in financial market history, exposing vulnerabilities in the increasingly complex and interconnected world of electronic trading. The event triggered significant reforms and led to a greater focus on market stability, transparency, and risk management. While strides have been made to enhance market safeguards and regulatory oversight, ongoing vigilance and continuous adaptation to technological advancements are necessary to maintain the integrity and stability of modern financial markets. How Algorithmic Trading Thrives in Changing Markets? Algorithmic trading (ALGO) can tackle changing market conditions through various techniques and strategies that allow algorithms to adapt and respond effectively. Here are some ways ALGO can address changing market conditions: Real-Time Data Analysis: Algo systems continuously monitor market data, including price movements, volume, news feeds, and economic indicators, in real-time. By analyzing this data promptly, algorithms can identify changing market conditions and adjust trading strategies accordingly. This enables Algo to capture opportunities and react to market shifts more rapidly than human traders. Dynamic Order Routing: Algo systems can dynamically route orders to different exchanges or liquidity pools based on prevailing market conditions. By assessing factors such as liquidity, order book depth, and execution costs, algorithms can adapt their order routing strategies to optimize trade execution. This flexibility ensures that algo takes advantage of the most favorable market conditions available at any given moment. Adaptive Trading Strategies: Algo can utilize adaptive trading strategies that are designed to adjust their parameters or rules based on changing market conditions. These strategies often incorporate machine learning algorithms to continuously learn from historical data and adapt to evolving market dynamics. By dynamically modifying their rules and parameters, algo systems can optimize trading decisions and capture opportunities across different market environments. Volatility Management: Changing market conditions often come with increased volatility. Algo systems can incorporate volatility management techniques to adjust risk exposure accordingly. For example, algorithms may dynamically adjust position sizes, set tighter stop-loss levels, or modify risk management parameters based on current market volatility. These measures help to control risk and protect capital during periods of heightened uncertainty. Pattern Recognition and Statistical Analysis: Algo systems can employ advanced pattern recognition and statistical analysis techniques to identify recurring market patterns or anomalies. By recognizing these patterns, algorithms can make informed trading decisions and adjust strategies accordingly. This ability to identify and adapt to patterns helps algocapitalize on recurring market conditions while also remaining adaptable to changes in market behavior. Backtesting and Simulation: Algo systems can be extensively backtested and simulated using historical market data. By subjecting algorithms to various market scenarios and historical data sets, traders can evaluate their performance and robustness under different market conditions. This process allows for fine-tuning and optimization of algo strategies to better handle changing market dynamics. In summary, algo tackles changing market conditions through real-time data analysis, dynamic order routing, adaptive trading strategies, volatility management, pattern recognition, statistical analysis, and rigorous backtesting. By leveraging these capabilities, algo can effectively adapt to evolving market conditions and capitalize on opportunities while managing risks more efficiently than traditional trading approaches The Rise of Algo Traders: Is Technical Analysis Losing Ground? Although algorithmic trading (algo trading) can automate and optimize certain elements of technical analysis, it is improbable that it will fully substitute it. Technical analysis is a financial discipline that encompasses the examination of historical price and volume data, chart patterns, indicators, and other market variables to inform trading strategies. There are several reasons why algo traders cannot entirely supplant technical analysis: Interpretation of Market Psychology: Technical analysis incorporates the understanding of market psychology, which is based on the belief that historical price patterns repeat themselves due to human behavior. It involves analyzing investor sentiment, trends, support and resistance levels, and other factors that can influence market movements. Algo traders may use technical indicators to identify these patterns, but they may not fully capture the nuances of market sentiment and psychological factors. Subjectivity in Analysis: Technical analysis often involves subjective interpretation by traders, as different individuals may analyze the same chart or indicator differently. Algo traders rely on predefined rules and algorithms that may not encompass all the subjective elements of technical analysis. Human traders can incorporate their experience, intuition, and judgment to make nuanced decisions that may not be easily captured by algorithms. Market Adaptability: Technical analysis requires the ability to adapt to changing market conditions and adjust strategies accordingly. While algorithms can be programmed to adjust certain parameters based on market data, they may not possess the same adaptability as human traders who can dynamically interpret and respond to evolving market conditions in real-time. Unpredictable Events: Technical analysis is often challenged by unexpected events, such as geopolitical developments, economic announcements, or corporate news, which can cause significant market disruptions. Human traders may have the ability to interpret and react to these events based on their knowledge and understanding, while algo traders may struggle to respond effectively to unforeseen circumstances. Fundamental Analysis: Technical analysis primarily focuses on price and volume data, while fundamental analysis considers broader factors such as company financials, macroeconomic indicators, industry trends, and news events. Algo traders may not have the capacity to analyze fundamental factors and incorporate them into their decision-making process, which can limit their ability to fully replace technical analysis. In conclusion, while algo trading can automate certain elements of technical analysis, it is unlikely to replace it entirely. Technical analysis incorporates subjective interpretation, market psychology, adaptability, and fundamental factors that may be challenging for algorithms to fully replicate. Human traders with expertise in technical analysis and the ability to interpret market dynamics will continue to play a significant role in making informed trading decisions. The Ultimate Winner - Algo Trading or Manual Trading? Determining whether algo trading or manual trading is best depends on various factors, including individual preferences, trading goals, and skill sets. Both approaches have their advantages and limitations, and what works best for one person may not be the same for another. Let's compare the two: Speed and Efficiency: Algo trading excels in speed and efficiency, as computer algorithms can analyze data and execute trades within milliseconds. Manual trading involves human decision-making, which may be subject to cognitive biases and emotional factors, potentially leading to slower execution or missed opportunities. Emotion and Discipline: Algo trading eliminates emotional biases from trading decisions, as algorithms follow predefined rules without being influenced by fear or greed. Manual trading requires discipline and emotional control to make objective decisions, which can be challenging for some traders. Adaptability: Algo trading can quickly adapt to changing market conditions and execute trades based on pre-programmed rules. Manual traders can adapt their strategies as well, but it may require more time and effort to monitor and adjust to rapidly evolving market dynamics. Complexity and Technical Knowledge: Algo trading requires programming skills or the use of algorithmic platforms, which can be challenging for traders without a technical background. Manual trading, on the other hand, relies on an understanding of fundamental and technical analysis, which requires continuous learning and analysis of market trends. Strategy Development: Algo trading allows for systematic and precise strategy development based on historical data analysis and backtesting. Manual traders can develop their strategies as well, but it may involve more subjective interpretations of charts, patterns, and indicators. Risk Management: Both algo trading and manual trading require effective risk management. Algo trading can incorporate predetermined risk management parameters into algorithms, whereas manual traders need to actively monitor and manage risk based on their judgment. Ultimately, the best approach depends on individual circumstances. Some traders may prefer algo trading for its speed, efficiency, and objective decision-making, while others may enjoy the flexibility and adaptability of manual trading. It is worth noting that many traders use a combination of both approaches, utilizing algo trading for certain strategies and manual trading for others. In conclusion, algorithmic trading offers benefits such as speed, efficiency, and risk management, while manual trading provides adaptability and human intuition. AI enhances algorithmic trading by processing data, recognizing patterns, and providing decision support. Algos excel in automated news monitoring and event-driven strategies. However, the Flash Crash of 2010 exposed vulnerabilities in the interconnected trading landscape, with algorithmic trading exacerbating the market decline. It serves as a reminder to implement appropriate safeguards and risk management measures. Overall, a balanced approach that combines the strengths of both algorithmic and manual trading can lead to more effective and resilient trading strategies.
NASDAQ:AAPLEducation
by Money_Dictators
11
Do you know what it takes to be an Algo Trader?To be an algo trader, you typically need to have a strong background in computer science and programming, as well as a good understanding of financial markets and trading strategies. Here are some of the important elements you need to be a top Alog Trader: Experience with database management and data analysis Knowledge of statistical analysis and machine learning techniques Understanding of financial markets and trading strategies Strong analytical and problem-solving skills Attention to detail and ability to work under pressure Overall, to be algo trader requires a combination of technical expertise, financial knowledge, and strong analytical and problem-solving skills. It can be as simple as having an easy and proven mechanical strategy that you can demo, back test, forward test, analyse, monitor and evaluate your results. This way, you'll have a decent idea on what your system and strategy potentially could yield in the near future. Trade well, live free. MATI Trader Feel free to follow our socials below if you enjoy this content :) Thank you.
JSE:J200Education
by Timonrosso
HOW-TO : analyze the market with script051 indicatorsYou can find the indicators with the link below Indicators If you are in a 50% loss in the current bearish market, you probably did not choose the right point to enter or you got involved in the wrong hope that the price will return soon. So what should we do to avoid such losses? You should be familiar with market cycles 2 cycles that you are probably familiar with: psychology of a market cycle wyckoff price cycle The problem with the above cycles is that We cannot easily recognize the phases of the cycle But look at the cycle below This cycle is exactly like the Wykoff cycle, only the names have been changed. "Hidden truth" is named because the real price of a share is only in this phase, and if someone wants to invest, it is better to enter in this price phase, but often everyone loses a large percentage of their capital at this phase, and if the price stay in this phase for a long time, they will probably leave position with a large loss. "Obvious truth" : In this phase, the direction of the price is easily recognized by traders and investors. The price is most likely moving upwards rapidly and the greed is also increasing. "Hidden lie" : It is the worst point to enter the market. This is a place where big players leave the market, but it seems to you that the price is going to grow, that's why this name was chosen! "Obvious lie" : This phase is also easily recognizable, but the greed that has formed during the 2 phases does not allow you to leave the market. The market trend has clearly changed, but you are trading against the direction of the market. For example, the market is now in the "Hidden truth" phase and good point for investment. The point that I need to mention is that -ONE LOOK is designed for the crypto market, but due to the general correlation of the markets, you can get good results for other markets as well. You don't need to identify the phases, the -ONE LOOK will do it for you. -ONE LOOK tells you where you are in the cycle at any moment with the help of 10 different indicators. If you enter the market at the right point with the help of -ONE LOOK , I promise you that 50% of your problems will be solved. Also, this indicator has features such as: 1.Heatmap 2.Screener You can use it according to your needs. Let's go to the next step ! Well, you were able to recognize the phases of the cycle and entered at the right point. But the fact is that cycles are formed in long periods of time (I know the fractal structure market :|) maybe you need to find the trend in "15m" chart. " -hero" and " -master cycle" will help you here. Look at the chart below These indicators are not magic, you can find the trend with moving average or market structure. With the difference that these indicators remove fakeout for you that might make you think the trend has changed We talked about cycles and trends. Another thing that I can boldly say that everyone uses is the key areas of the market. If you want to enter a trading position, you are usually looking for how the price reacts to the key areas. we also get help from these areas for exit and stop points, so obviously it is necessary to be able to find them correctly. Look at the chart below to see how we found the important price areas using indicators and how the price reacted and how we enter the trading position. With the help of volume profile and high and low points of daily weekly candles and channel bands, we can find suitable signals. But one of the key points of the market is the round numbers. Fortunately, we can find these points with the help of the " -auto round level" indicator But how can we find the signal and test them properly? First, let's see how to find good market entry with the -master cycle indicator : You already learned to find the trend with the master cycle. You can enter the position in 3 situations: 1.When the flow element changes state. 2.When the score element moves against the flow direction. 3.When the spot element appears You can also find good signals using -Hero 4 parameters are determined for the -hero indicator, by changing them you can get different signals (definitely, the accuracy and efficiency of each signal is different from each other) And the last indicator allows you to see at a glance what signal -Hero indicator has send for each symbol and at what time. You can find the indicators with the link below Indicators
BITSTAMP:BTCUSDEducation
by script051
3
Candles continued.So here I am following on from the previous post, we have moved to the 4H chart, and I have taken a deeper delve into how to filter the candles out to find optimal ones, and look here, all the Bullish engulfing under the moving average... very poor performance, but the Bearish engulfing that is rejecting the moving average, remember previous post we talked about what price is attempting on pullbacks to the moving average? also has an orderblock rejecting as the moving average is down, remember what that tells us?? I will post the links to them under this post so you can go back over it. The Bollinger band squeezed just before aswell before the expansion. What to take away... Patience and optimisation! make the rules to your system click! do not react to every candle, find where price has action!
Education
by ZenFlo
CandlesPrice action is a very good way to trade, it produces clean and clear charts, but the clue is in the name... 'Price action' if I made a bot to literally enter every single time a bullish or bearish engulfing appeared Id be broke very fast! this is because these candles by themselves are not as a effective as when used in conjunction with trendlines, moving averages, and supply and demand zones. So remember if using candle analysis be sure to use it with other confirmations to find the best trades... I personally recommend a Volume profile! this is a great tool designed to show orders... Dont you agree that using 'Price' action on areas where price is transacting makes sense? Volume profile measures the orders, and the candles tell you the action. Just a quick and simple post to make you think!
Education
by ZenFlo
Average True Range... and BollingersATR is a great indicator designed to show you the previous ranges of the previous candles depending on the value chosen, in this example I have done 6 periods, so you can see in this chart I have highlighted when we have peaks and troughs and one thing to do is compare the times of day this activity happens, you can see at certain times the atr climbs, it stalls at others or can fall, so ATR is showing us previous candles range, so if you are in a trade you want the range to be growing usually so that your trade can head to TP, but the important thing to takeaway is the fact that price is moving alot, this is because it is experiencing higher level of trading activity price is trending, where as a falling ATR reading means typically things are slowing down or accumulating, remember this doensnt give direction though as price can still move up or down despite a falling range per candle. However what it can do is tell you good times to look for trades, you can filter down by time the best time to take trades based on your strategy winning or losing in the peaks of troughs. ATR can also be used to determine stop losses of TP, by taking the the reading and using a 2xreading stop loss or TP, the more volatile the market the bigger your stop losses and tp will be, but more volatility generally correlates well with that idea, not only does it offer greater protection it also prevents missing out on good moves. So 2nd part is Bollinger bands we can see how it works, it basically again is telling you the range of things, so Id like you to compare the reading on ATR to the Bollingers, and you can see when ATR falls and the Bollingers are squeezing tight we have very little to trade, energy is low and range is small, In crypto I have heard this term called the crab which I have to say... I do find quite amusing. When ATR is rising the Bollingers expand creating a wide cloud, so on the last box, where price falls despite ATR falling... what is the difference this time? That is right, Bollingers are not squeezed together, which tells us the ATR reading is acting like it is small and stuck in a squeezing formation but in fact we are just in an expansion of the Bollinger moving slowly. What do I want you to take away from this? Just a deeper thought about which market conditions are best for your strategy and how to avoid times which will not really offer a good trade yet ect, and have a look for patterns in how you trade around these volatility indicators! Happy trading... More to come
Education
by ZenFlo
What is a moving average and do they work?Moving average is an average of price closes over a certain amount of time, so at a base level they rise when price rises and fall when price falls, so why are they important? Because they give you a sense of the average direction of price over a certain amount of time, if you take the chart at face value you are not even witnessing one price close at that specific moment in time! unless you are then well done you lol, so the moving average is giving us data of maybe 89 or 50 or 200 ect, this overall analysis of the trend can defiantly aid your decision making, for example if you use two moving averages like the ma8 and ma89, what we can look at is the moving average MA8 reverting back to test the baseline which is the MA89 in this example, so price is now attempting to some extent to change trend, if it breaks lower than the baseline the line will start falling! MA89 will start declining as negative closes come in and alter the formula, that is why these areas can offer great buying opportunities or selling depending which side of the baseline you are, Price will test the baseline and bounce in strong trends before price will eventually break the baseline down the line. I will follow this post up with a post on moving averages being used on indicators now we have the first bit out the way.
Education
by ZenFlo
6
How to generate more profitsTrading requires entries similar to how a computer would enter positions, what I mean by this is entries should be so disciplined it hurts, when you are sitting there bored in the early hours of the morning and you take sloppy trades this has an effect on your results! you should create enough discipline to be able to trade a specific time frame when your entry and exit system provides better returns. Adding rules for confirmation will also lead to better entries, things such as a Baseline, what this will do is create a layer of the bike lock, now you never take buys below the baselines, meaning you no longer take reversals, your winrate will improve and your profits improve. For GBPUSD it is wise to trade over the London session and you can see just how much the pair moved over this session, your trades have a better chance at delivering profits in this zone. You may find your strategy works well in other timezones, and I suggest following the path, dont be restricted if it works elsewhere, but get some good backtest results.
Education
by ZenFlo
Most gains for $SPY happen overnight: a quantitative studyAccording to the New York Times 2018: most gains for $SPY happen overnight. "If you had bought the SPY at the last second of trading on each business day since 1993 and sold at the market open the next day — capturing all of the net after-hour gains — your cumulative price gain would be 571 percent." We thought why not to review this thesis in a bit more detailed way. We set a simple backtest that is not going back to 1993 but to Jan 1, 2010: hold overnight and sell in the morning. The Setup $10,000 to trade daily (nightly actually) Trading only $SPY Buy at 3:59 using a market order Sell at 9:30, 9:31, 9:32, 9:33, 9:34, 9:35 (let's see which one does better) using a market order Do not trade on early close days Algo Parameters Enter Criteria: 1.#No condition - just enter a position 2.True Exit Criteria: 1.#Exit next day, when there is a position 2.$Position.size>0 The Results Strategy: Hold overnight, exit 9:30 Total wins: 1651 Total loses: 1359 Total gain: 79.54% Total gain 2022: -2.59% Strategy: Hold overnight, exit 9:31 Total wins: 1629 Total loses: 1341 Total gain: 80.04% Total gain 2022: -2.88% Strategy: Hold overnight, exit 9:32 Total wins: 1628 Total loses: 1342 Total gain: 88.80% Total gain 2022: -3.31% Strategy: Hold overnight, exit 9:33 Total wins: 1618 Total loses: 1352 Total gain: 94.28% Total gain 2022: -3.96% Strategy: Hold overnight, exit 9:34 Total wins: 1617 Total loses: 1353 Total gain: 94.11% Total gain 2022: -3.63% Strategy: Hold overnight, exit 9:35 Total wins: 1633 Total loses: 1337 Total gain: 90.48% Total gain 2022: -4.36% Disclaimer: This post is for fun and educational purposes only with no attempt to beat buy and hold. Do not use this as trading/investment advice.
AMEX:SPYEducation
by BreakingEquity_official
2
Most traders underperform to a coin flip day trading botFirst-time day traders are most likely going to lose money. CNBC quotes at least four studies with a similar conclusion: 90% of traders fail to make money. The primary reason that most traders fail is not because of their strategy, it is because of their psychology. As Benjamin Graham liked to say, “The worst enemy of the investor is most likely himself.” To quantify how bad the fact of 90% losing money is, we compared it to a coin flip trading bot which makes a trading decision based on a virtual coin flip random(0,1) Below are the results for Trading $QQQ Random enter and exit at 10min intervals No stop loss Always exit EOD (no hold overnight) 100 simulations per year to smooth it out Algo parameters: Enter Criteria: $Random.random >=0.5 Exit Criteria: $Random.random >=0.5 Year over year performance: 2016: Average Performance 1.92% Best Performance 20.38% Worst Performance -10.21% Average MDD -6.65% 2017: Average Performance 3.08% Best Performance 10.92% Worst Performance -4.10% Average MDD -3.99% 2018: Average Performance -3.56% Best Performance 21.65% Worst Performance -21.08% Average MDD -12.05% 2019: Average Performance 4.80% Best Performance 15.61% Worst Performance -6.75% Average MDD -6.03% 2020: Average Performance 6.66% Best Performance 33.63% Worst Performance -17.12% Average MDD -10.69% 2021: Average Performance 2.41% Best Performance 26.31% Worst Performance -17.97% Average MDD -7.25% 2022: Average Performance -4.21% Best Performance 3.91% Worst Performance -10.00% Average MDD -5.40% We expected it to perform worse than that ;) Disclaimer: NOT financial/investment advice
NASDAQ:QQQEducation
by BreakingEquity_official
ICHIMOKU AND RVI BEGINNERS PLAY BOOKNow ichimoku is relatively simple look for buys above the cloud and look for sells under the cloud. so when we backtest that over our 5/5 winners with rvi we get two less entrys, however as a beginner to avoid them whipsaw movements that isnt always a bad thing. The cloud itself offers dynamic support and resistance based of averages. price breaking through the cloud signals a breakout and a change in the trend usually. if new to trading I recommend learning about ichimoku on youtube, its not the all time great plan but if you have no plan its better than that. to keep discipline and entry requirements.
TVC:USOILEducation
by UnknownUnicorn6102956
Bots / Algo / Whales & Miners Controlling Market | EOS AnalysisHere we have a different chart... This EOS chart threw me off, it was hard to read at first as it is a completely new chart for me. You see, these numbers and laters on the screen /"pairs", Altcoins or whatever they are, all basically move in exactly the same way with just a few variations. We have bots/algorithms controlling different parts of the movements and depending on which bots are affecting which token/altcoin project pair at any given time, that is what will decide how the chart will be drawn. So depending on the strength of the project, the team or the pull it has, just to pick a few of the factors that affect the chart, the chart will be drawn differently. I think there are around 5 major algorithms controlling the market and many other individual custom made ones interacting with them, as well as the public. Now that I think about it... These bots/whales/groups/algo/etc. can be miners as well for all we know... That doesn't really matter... We buy when prices are down, to sell when everything is green and up. So all the technicalities are just entertainment if you can here like me for work. We look for the charts with the lowest risks, aiming always first to win above all... Once you get used to the feeling of winning, you can decide if you want to sell at 20%, 30% or more. The trick is to get used to selling, securing/collecting profits. Because if you don't sell, everything will be gone when the market goes low. Buy when the prices are low. Sell when prices are moving up. EOS Chart Analysis We have EOS Token (EOSBTC) trading at support levels, consolidating sideways. This consolidation can lead to another drop or a move up. Which one will it be? Signals MA200 dropping fast with momentum is my signal that EOSBTC has good chances to move up. Each time we see MA200 (black line) behaving this way, prices tend to pull towards the Moving Average line indicator. Let's see how it goes. Even with all this information, we still remain open to all scenarios and always have a plan before we trade. Example If prices go lower we stop the trade at xxx price. If prices start to move higher, this is where I sell and collect profits. Enjoyed the content? Feel free to hit LIKE to show your support. This is Alan Masters. Namaste.
EEducation
by MasterAnanda
7
BCH - Algorithmic Entry and Target hit beautifully!I realise it's easy to state after the fact, but BCH has produced a clear example of fibonacci entry and first target hit. I just missed my entry as I noticed this a little late, highlighting the difference between trading and TA. However, its impossible to miss the confluence with the entry, RSI moving above 30, divergence appearing on the EWO and the entry at the Golden Pocket. Stops just outside the 65 needed to be careful as a cheeky wick could have caught them. Its also satisfying to see my automatic algo, written using pine script, producing a fantastic trade set up in seconds. Sam
BEducation
by crypto_0sam0