Zeiierman

█ تسلط بر بازارهای پرنوسان - قسمت 3: چرا صبر بزرگترین مزیت شماست اگر قسمت اول را در مورد تعیین اندازه موقعیت و قسمت دوم را در مورد نقدینگی خوانده‌اید، پس از قبل می‌دانید که چگونه با مکانیک بازارهای پرنوسان سازگار شوید. ابزار بزرگ بعدی در زرادخانه شما صبر بود. بزرگترین حریف شما در بازارهای wild ذهن خودتان است. در بازارهای پرنوسان، احساسات شما به راحتی می‌توانند بر شما غلبه کنند. ترس از دست دادن (FOMO) یکی از خطرناک‌ترین احساساتی است که تصمیمات ضعیف را هدایت می‌کند. █ FOMO (ترس از دست دادن) در بازارهای پرنوسان به شدت ضربه می‌زند نوسانات شدید قیمت، مانند حرکات 300-500 واحدی در نزدک یا جهش Bitcoin به میزان 1000 دلار در چند ثانیه، می‌تواند این احساس را ایجاد کند که پول آسان همه‌جا وجود دارد. شما می‌توانید به سرعت وسوسه شوید که به دنبال حرکات باشید، به خصوص زمانی که به نظر می‌رسد هر فرصتی را از دست می‌دهید. اینجاست که اکثر معامله‌گران ضرر می‌کنند. اجازه دهید چند حقیقت تلخ را بیان کنم که مجبور شدم از طریق ضررهای فراوان hard یاد بگیرم: نوسانات برابر با فرصت نیست. حرکات سریع به معنای معاملات آسان نیست. اکثر حرکات قیمت wild برای به دام انداختن نقدینگی و مجازات بی‌صبری طراحی شده‌اند. واقعیت این است که بازار می‌خواهد شما در این شرایط بیش از حد واکنش نشان دهید. می‌خواهد بعد از یک حرکت بزرگ خرید کنید. می‌خواهد بعد از یک سقوط، موقعیت فروش باز کنید. بازار با احساسی بودن، دنبال کردن و واکنش نشان دادن شما رشد می‌کند. زیرا معامله‌گران واکنشی = تامین‌کنندگان نقدینگی برای پول هوشمند. هر معامله‌گری این اشتباه را مرتکب شده است - نه فقط یک بار، بلکه بارها و بارها. پریدن به بازار بعد از یک حرکت بزرگ، به امید اینکه ادامه پیدا کند… اما معمولاً چه اتفاقی می‌افتد؟ بازار برمی‌گردد و شما را متوقف می‌کند. آیا می‌توانید ارتباط برقرار کنید؟ داستان یا تجربه خود را در این مورد در نظرات زیر به اشتراک بگذارید! █ معامله‌گران با تجربه به جای آن چه می‌کنند ⚪ آنها می‌دانند که اولین حرکت اغلب تله است شکست؟ انتظار یک فیک‌اوت داشته باشید. سقوط؟ انتظار یک بازگشت سریع داشته باشید. بالاترین سطح جدید؟ مراقب شکار استاپ‌ها باشید. پایین‌ترین سطح جدید؟ مراقب یک سقوط باشید. در واقع، معامله‌گران حرفه‌ای بازار را دنبال نمی‌کنند. ما منتظر می‌مانیم تا شکار استاپ‌ها کامل شود، برداشت نقدینگی به پایان برسد، قیمت به منطقه آنها بازگردد و قبل از ورود به بازار تأییدیه بگیریم. ⚪ آنها صبر را مانند یک مهارت تمرین می‌دهند معامله‌گران حرفه‌ای به این دلیل صبورتر نیستند که "ویژه" هستند. ما صبور هستیم زیرا از hard یاد گرفته‌ایم که دنبال کردن منجر به درد می‌شود. ⚪ آنها می‌دانند چه زمانی معامله نکنند معامله کردن زمانی که ساختار مشخصی وجود ندارد، تأییدیه تمیزی وجود ندارد، اگر اسپرد خیلی زیاد باشد یا زمانی که نقدینگی خیلی کم باشد، بد است. در عوض، معامله‌گران حرفه‌ای اجازه می‌دهند بازار به سمت آنها بیاید، نه برعکس. ⚪ آنها FOMO را به اعتماد به نفس تبدیل می‌کنند به جای اینکه بگویید، "من حرکت را از دست می‌دهم…"، توصیه می‌کنم فکر کنید: "اگر بدون من اجرا شد - معامله من نبود." "اگر به تنظیمات من بازگردد - اکنون معامله من است." █ خب، امروز چه یاد گرفتیم؟ نوسانات باعث FOMO می‌شود. FOMO باعث تصمیمات بد می‌شود. تصمیمات بد باعث ضرر می‌شود. برای win در دراز مدت، باید آرام، گزینشی و حرفه‌ای بمانید. اجازه دهید سایر معامله‌گران نقدینگی احساسی باشند. اینگونه است که در بازارهای پرنوسان زنده می‌مانید. █ آنچه قبلاً پوشش دادیم: قسمت 1: کاهش اندازه موقعیت قسمت 2: نقدینگی معاملات شما را می‌سازد یا از بین می‌برد قسمت 3: چرا صبر بزرگترین مزیت شماست █ آنچه در ادامه این مجموعه می‌آید: قسمت 4: روند بهترین دوست شماست ----------------- سلب مسئولیت محتوای ارائه شده در اسکریپت‌ها، شاخص‌ها، ایده‌ها، الگوریتم‌ها و سیستم‌های من فقط برای اهداف آموزشی و اطلاع‌رسانی است. این محتوا به منزله مشاوره مالی، توصیه‌های سرمایه‌گذاری یا درخواست خرید یا فروش هیچ ابزار مالی نیست. من هیچ مسئولیتی در قبال هیچ گونه خسارت یا آسیبی، از جمله بدون محدودیت هرگونه ضرر سود، که ممکن است به طور مستقیم یا غیرمستقیم ناشی از استفاده یا اتکا به چنین اطلاعاتی باشد، نمی‌پذیرم. تمام سرمایه‌گذاری‌ها شامل ریسک است و عملکرد گذشته یک اوراق بهادار، صنعت، بخش، بازار، محصول مالی، استراتژی معاملاتی، بک تست یا معاملات یک فرد، نتایج یا بازده‌های آینده را تضمین نمی‌کند. سرمایه‌گذاران مسئولیت کامل هرگونه تصمیم سرمایه‌گذاری که می‌گیرند را بر عهده دارند. چنین تصمیماتی باید صرفاً بر اساس ارزیابی شرایط مالی، اهداف سرمایه‌گذاری، تحمل ریسک و نیازهای نقدینگی آنها باشد.

█ Order Imbalance and Change Point DetectionTrading might sometimes seem like magic, but at its core, the market operates on simple principles, supply and demand, and the flow of information. Recent academic work shows that retail traders can gain an edge even without expensive data feeds by understanding some fundamental ideas, like order imbalance and change point detection.In this article, we break down key concepts such as order imbalance, sudden volume shifts, change point detection, and the CUSUM algorithm. We also explain how retail traders can apply these ideas to improve their strategies.█ What Is the Order Book and Order Imbalance?⚪ The Order BookEvery market has an order book, simply a list of all buy orders (bids) and sell orders (asks) for an asset. ⚪ Order Imbalance – A Key IndicatorOrder imbalance measures the difference between the total buying and selling orders for the order book.Definition: Order imbalance is the difference in volume between buy orders and sell orders.Why It Matters: A strong imbalance means one side (buyers or sellers) is dominating. For example, if there are significantly more buy orders than sell orders, the market may be gearing up for a price increase.⚪How It’s Detected in Research:Researchers calculate a volume-weighted average price (VWAP) across multiple price levels in the order book (typically the top 20 levels) and compare it to the mid-market price.A positive imbalance indicates aggressive buying, while a negative imbalance suggests selling pressure.█ Sudden Volume Shifts and Change Point Detection⚪Sudden Volume ShiftsWhat It Means: Sometimes, there is an abrupt and noticeable change in the number of orders placed. This sudden shift in volume can signal a big move on the horizon.Example: In a trading context, this might be seen when volume bars spike unexpectedly on a price chart, often accompanying rapid price moves or breakouts.⚪Why They Are Crucial:Sudden volume increases often coincide with significant order flow events. For instance, if a large number of buy orders hit the market at once, this could indicate a rapid shift in trader sentiment and serve as a precursor to a sustained price move.█ Change Point Detection – Spotting the ShiftDefinition: Change point detection is a statistical technique used to identify the exact moment when the properties of a data series change significantly.Purpose: In trading, it helps distinguish meaningful shifts in market behavior from random noise.How It’s Used: Researchers apply this to order imbalance data to flag moments when the market’s buying or selling pressure changes abruptly. These flagged moments (or “change points”) can then be used to forecast short-term price movements.█ Meet CUSUM: The Cumulative Sum AlgorithmCUSUM stands for Cumulative Sum. It’s a simple yet powerful algorithm that detects changes in a data series over time.⚪ How CUSUM Works:Tracking Deviations: The algorithm continuously adds up minor differences (or deviations) from an expected value (like a running average).Signal for Change: When the cumulative sum exceeds a predetermined threshold, it signals that a significant change has occurred.In Trading: CUSUM can be applied to measure the order imbalance. When the cumulative deviation is high enough, it indicates a strong change in market pressure, an early warning signal for a potential price move. For example, a rising cumulative sum based on increasing buy-side pressure might indicate that the price will likely move upward.█ How Can Retail Traders Benefit Without Full LOB Data?Full access to the order book (all price levels and orders) can be expensive and is usually reserved for institutional traders. However, retail traders can still gain valuable insights by:⚪ Using Proxies for Order Imbalance:Many trading platforms offer basic volume indicators.Look for volume spikes or unusual shifts in trading volume as a sign that order imbalance might occur.⚪ Leveraging Simplified Change Detection:Even if you don’t have complex LOB data, you can set up simple alerts on your trading platform.For instance, you might create a custom indicator that watches for rapid increases in volume or price moves, similar to a basic version of the CUSUM algorithm.⚪ Focusing on Key Price Levels:Even with limited data, monitor support and resistance levels. A sudden break (accompanied by high volume) can serve as a proxy for a change in market dynamics.⚪ Adopting a Data-Driven Mindset:Integrate these concepts into your routine analysis. When you see a significant volume shift or a sudden spike in activity, consider it a potential “change point” and adjust your strategy accordingly.█ In SummaryOrder Imbalance measures the difference between buying and selling volumes in the order book, offering insights into market direction.Sudden Volume Shifts are significant changes in trading volume that can signal a shift in market sentiment.Change Point Detection helps identify the precise moments when these shifts occur, filtering out noise and highlighting actionable signals.CUSUM is a powerful tool that continuously tracks cumulative deviations in market data, alerting traders when the market undergoes a significant change.For retail traders, these methods underscore the importance of watching price and understanding the underlying order flow. While you might not have access to full-depth order book data, using volume indicators and setting up alert systems can help you capture the essence of these insights, providing a valuable edge in your trading decisions.-----------------DisclaimerThe content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

In today’s digital age, social media has become a cornerstone of information for nearly every aspect of our lives. From lifestyle tips to financial advice, influencers wield significant power over public sentiment. Among them are financial influencers, or "finfluencers," who share investment tips, stock picks, and market analyses. But how reliable is their advice? Can retail traders use their recommendations to improve their trading strategies?A recent research paper titled Finfluencers by the Swiss Finance Institute dives deep into these questions. The study examines the accuracy, influence, and implications of finfluencers’ advice. Its findings are both eye-opening and actionable for retail traders looking to navigate the crowded world of social media-driven investing.█ The Truth About FinfluencersThe study analyzed tweet-level data from over 29,000 finfluencers on StockTwits, classifying them into three distinct groups:Skilled Finfluencers: These individuals represent 28% of the sample and are the true gems among finfluencers. Their advice generates an average of 2.6% monthly abnormal returns, indicating that they provide genuinely valuable insights. Skilled finfluencers often tweet less frequently and tend to post data-driven and sometimes negative assessments, which align with their ability to outperform.Unskilled Finfluencers: Accounting for 16% of the sample, unskilled finfluencers have little to no impact on returns. Their advice is neither harmful nor particularly beneficial, making them neutral players in the social media finance space. Despite their lack of effectiveness, these influencers still attract some attention due to their activity levels and relatability.Antiskilled Finfluencers: Shockingly, 56% of finfluencers fall into this category, making them the majority. Antiskilled influencers consistently provide poor advice, generating an average of -2.3% monthly abnormal returns. Their recommendations often reflect overly optimistic or flawed beliefs, leading followers astray. Despite their negative track record, antiskilled finfluencers tend to have the largest followings and the most influence, driven by behavioral biases such as homophily and their frequent activity on social media.Surprisingly, the study found that unskilled and antiskilled finfluencers have more followers and exert greater influence than their skilled counterparts. This phenomenon is linked to behavioral biases such as homophily—a tendency for people to align with others who share similar opinions, even if those opinions lack merit.█ Why Antiskilled Influencers ThriveOne might wonder how antiskilled finfluencers manage to amass large followings despite their poor track records. The research highlights several reasons:Popularity Over Precision: Social media rewards engagement and relatability, often sidelining the importance of accuracy.Behavioral Biases: Retail traders are drawn to familiar or optimistic messages, even when they’re unfounded.Tweet Frequency: Antiskilled influencers tend to post more frequently, increasing their visibility and perceived authority.Interestingly, the study also found that skilled finfluencers tend to post less frequently and are more likely to share negative but accurate assessments. This trait aligns with their ability to generate better returns but limits their mass appeal.█ How Retail Traders Can BenefitThe research offers valuable lessons for retail traders looking to cut through the noise and make informed decisions:⚪ Think Critically About Popular AdviceJust because an influencer has a large following doesn’t mean their advice is sound. Popularity often correlates with engagement rather than expertise. Before acting on any recommendation, evaluate the influencer’s track record and consider the rationale behind their advice.⚪ Embrace Contrarian InvestingOne of the study’s most intriguing findings is the profitability of a contrarian approach. By systematically trading against the advice of antiskilled influencers, traders can achieve abnormal returns. This strategy, humorously dubbed “the wisdom of the antiskilled crowd,” underscores the potential of doing the opposite of what bad advice suggests.⚪ Look for Quality Over QuantitySkilled finfluencers often tweet less frequently but provide higher-quality insights. Traders should prioritize substance over volume, seeking out influencers who back their recommendations with data and sound reasoning.⚪ Understand Behavioral BiasesBeing aware of biases like homophily can help traders make more rational decisions. Instead of gravitating toward advice that feels familiar or comforting, focus on advice that is well-supported and objective.█ A Practical ExampleImagine you follow an antiskilled finfluencer who frequently posts bullish advice on various stocks. According to the research, these recommendations are likely to lead to losses. Instead of following their advice, you could develop a contrarian strategy by shorting or avoiding their suggested stocks. Backtesting this approach could reveal a consistent edge over time.Similarly, tracking skilled finfluencers who post less often but provide thoughtful analyses can complement this strategy, offering a balanced approach to decision-making.█ Final ThoughtsThe Finfluencers research sheds light on the complex dynamics of financial advice on social media. While social platforms have democratized access to information, they’ve also amplified the voices of unskilled and antiskilled influencers. For retail traders, this presents both challenges and opportunities.By approaching social media advice with a critical eye and leveraging the insights from this research, traders can navigate the pitfalls of herd mentality and capitalize on the inefficiencies created by antiskilled influencers. Ultimately, the key is to focus on evidence-based strategies and remember that the messenger’s popularity doesn’t always reflect the quality of their message.As the researchers aptly conclude: “The message is more important than the messenger.” In the ever-evolving world of retail trading, this is advice worth heeding.-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ Understanding Price Clustering in the Bitcoin MarketPrice clustering is a phenomenon where certain price levels, particularly round numbers, tend to appear more frequently in financial markets. This study focuses on how price clustering occurs in the Bitcoin market, providing insights that can be valuable for traders. █ The Psychology Behind Price ClusteringOne of the primary reasons behind price clustering in the Bitcoin market is the psychological impact of round numbers. Market participants often perceive prices ending in 0 or 00 as significant, which leads to a concentration of buy and sell orders around these levels. This behavior is not unique to Bitcoin; it has been observed across various financial markets, from stocks to foreign exchange.For instance, when Bitcoin prices approach a round number like $30,000 or $50,000, traders might expect strong resistance or support at these levels. This expectation can lead to increased trading activity, causing prices to cluster around these key levels. The psychological importance of these numbers can also cause traders to place stop-loss or take-profit orders around them, further reinforcing the clustering effect.█ Key Findings from the Study⚪ Clustering Around Round Numbers: The study highlights that Bitcoin prices tend to cluster around round numbers, such as $10,000, $20,000, or $50,000. This is primarily driven by psychological barriers, where traders view these round numbers as significant price levels, leading to an increased concentration of trading activity.⚪ Impact of Time Frames: The extent of price clustering varies significantly with the time frame. In shorter time frames (like 1-minute or 15-minute intervals), price clustering is less pronounced due to the randomness of price movements. However, as the time frame lengthens (hourly or daily), the clustering effect becomes more apparent, suggesting that traders may be more likely to anchor their strategies around these round numbers over longer periods.⚪ Differences in Open, High, and Low Prices: The study also finds differences in clustering patterns between open, high, and low prices. High prices tend to cluster around the digits 8, 9, and 0, while low prices cluster around 1, 2, and 0. Open prices generally show less clustering, suggesting they are less influenced by immediate market psychology. This pattern suggests that traders should pay particular attention to high and low prices during trading sessions, as these are more likely to show clustering around key levels.High Price: This is the highest price that Bitcoin reaches during a specific time period (for example, during a day or an hour). The study found that high prices cluster more around certain numbers, especially numbers ending in 0 or 9. So, high prices often end in numbers like $10, $100, $1,000, or $9,999 because traders tend to react to these round numbers.Low Price: This is the lowest price Bitcoin hits during a certain time period. Similar to high prices, low prices also cluster, but more around numbers ending in 0 and 1. So, low prices might end in numbers like $10, $1,001, or $5,001.Why is there a difference?High prices tend to cluster at numbers ending in 0 or 9 because those feel like natural stopping points for traders.Low prices tend to cluster at numbers ending in 0 or 1 for similar reasons.⚪ Price Level Influence: The study highlights that clustering behavior changes with the overall price level of Bitcoin. At lower price levels (e.g., below $10,000), there is more clustering around multiples of 5, such as $25, $50, or $75. As the price increases, the significance of these smaller increments diminishes, and clustering around larger round numbers becomes more dominant.█ Practical Insights for Retail TradersUnderstanding price clustering is crucial for traders because it sheds light on how market participants behave, particularly around psychologically significant price levels. These insights can help traders anticipate where the market might encounter resistance or support, allowing them to make more informed decisions.⚪ Identify Key Psychological Levels: Retail traders can benefit from identifying and monitoring round number levels in Bitcoin prices, such as $10,000, $30,000, or $50,000. These levels are likely to act as psychological barriers, leading to increased trading activity. Understanding these levels can help traders anticipate potential support or resistance areas where price reversals may occur.⚪ Adjust Trading Strategies Based on Time Frame: The study suggests that the effectiveness of using price clustering in trading strategies depends on the time frame. For short-term traders, clustering may be less reliable, but for those operating on longer time frames, clustering around round numbers could provide actionable signals for entry or exit points.⚪ Focus on High and Low Prices: Retail traders should pay particular attention to clustering in high and low prices during a trading session. These prices are more likely to exhibit clustering, indicating areas where traders might place stop-loss orders or where price reversals could occur. By aligning their trades with these clusters, traders could improve their risk management. If you’re setting stop-loss orders, for instance, placing them just beyond a cluster point could help you avoid being stopped out prematurely by normal market noise. Similarly, identifying clusters at high prices could offer better opportunities for taking profits.⚪ Consider the Overall Price Level: The level at which Bitcoin is trading also affects clustering. For example, when Bitcoin is at a lower price, traders might find opportunities by focusing on price levels ending in 5 or 0. However, as Bitcoin’s price increases, clustering becomes more concentrated around larger round numbers. Adjusting trading strategies to consider the current price level can enhance decision-making. Price Clustering at Low Levels (<$10 USD):There is significant clustering at prices ending in 0, but also notable clustering at prices ending in 5, which acts as a psychological barrier at these lower levels. Prices ending with 50 are also frequently observed as significant psychological barriers. Clustering is weaker overall at these levels compared to higher price ranges, but still noticeable at certain intervals.Price Clustering at Mid-Levels ($100–$1,000 USD):Clustering becomes more focused on round numbers like 00, 50, and 25. As prices increase, clustering around smaller numbers like 5 or 10 reduces. Larger psychological barriers, such as 100 and 500, emerge as significant points of clustering.Price Clustering at Higher Levels (≥ $10,000 USD):At these price levels, clustering becomes even more prominent around major round numbers like 10,000, 20,000, etc. The last two digits 00 become much more frequent, and there is almost no clustering at digits like 5 or 1. Clustering becomes very strong at larger round figures, with a strong psychological barrier hypothesis at play.Summary of Clustering at Different Levels:Low Prices (<$10): Clustering at 5, 10, 50, and 100.Mid Prices ($100–$1,000): Strong clustering at 00, 50, and 25.High Prices (≥$10,000): Dominant clustering around 00 and multiples of 1,000 (e.g., 10,000, 20,000).█ ConclusionPrice clustering is more than just an academic concept; it’s a practical tool that can significantly enhance your trading strategy. By understanding how prices tend to cluster around psychological levels, adapting your approach based on time frames, and recognizing the impact of Bitcoin’s price level, you can make more informed trading decisions. By integrating these insights into your trading plan, you’re not only aligning your strategy with the behavior of the broader market but also positioning yourself to capitalize on key price movements. Whether you’re a seasoned trader or just starting out, the knowledge of price clustering can help you navigate the volatile Bitcoin market with greater confidence and precision.█ ReferenceXin, L., Shenghong, L., & Chong, X. (2020). Price clustering in Bitcoin market—An extension. Finance Research Letters, 32, 101072. -----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ Interpreting Long/Short Ratios in Futures Trading: Beyond Bullish and BearishFor beginner traders, the long/short ratio in futures markets can seem like a clear-cut indicator of market sentiment. Many assume that a high ratio of longs to shorts means the market is bullish, while more shorts than longs signals a bearish outlook. But in reality, this interpretation is oversimplified and can lead to misguided trading decisions.In this article, we'll break down the nuances of the long/short ratio in futures trading, explaining why positions on the “short side” don’t always indicate a bearish stance and how traders can better interpret these ratios for a well-rounded perspective.█ Understanding the Basics: Futures Trading Is Not Spot TradingIn the futures market, every trade requires a buyer (long position) and a seller (short position). For each person going long, there’s a counterpart going short. This zero-sum structure means that, by definition, there’s always a balance between longs and shorts. However, the reasons why traders take long or short positions vary widely—and not all of them are directional bets on price movement.█ Why Not All Shorts Are Bearish (And Not All Longs Are Bullish)Let’s dig into why a trader might take the short side without actually betting on a price drop:⚪Hedging: Some traders go short to hedge an existing position. For instance, if they already hold a large amount of Bitcoin in the spot market, they might take a short position in Bitcoin futures to protect against potential downside risk. This doesn’t mean they’re bearish on Bitcoin; they’re just managing risk.⚪Arbitrage: Some traders take short positions for arbitrage purposes. For example, they might go long in one market and short in another to profit from small price differences without having any directional view on Bitcoin’s future price. Their short position is purely for balancing and not a bet on falling prices.⚪Market Making: Market makers provide liquidity to the market by taking both long and short positions. Their goal isn’t to profit from price movements but to capture the spread between the bid and ask prices. They don’t have a directional view—they’re simply facilitating trades.⚪Closing Long Positions: When traders close long positions, they effectively create a new short transaction. For instance, if a trader decides to exit a long position by selling, they’re adding to the short side of the market. But this action doesn’t necessarily mean they expect prices to drop—it could just mean they’re taking profits or reallocating their portfolio.█ Interpreting CoinGlass Long/Short Ratio Charts: Volume vs. AccountsLet’s look at the long/short ratio charts on CoinGlass as an example. CoinGlass provides two main types of ratios:⚪ Volume-Based Ratio: This chart shows the volume of capital in long vs. short positions. For example, a high volume in longs might suggest that large players are buying into Bitcoin. However, it’s important to remember that some of these long positions could be from market makers, hedgers, or arbitrageurs, who may not expect Bitcoin to rise. The volume itself doesn’t tell us why they’re in these positions.⚪ Account-Based Ratio: This chart tracks the number of accounts on each side (long vs. short) on exchanges like Binance. A higher number of accounts on the short side doesn’t mean all those traders are bearish. Many could be taking short positions to balance other trades or hedge risks. They’re not necessarily expecting Bitcoin to decline; they’re just managing their positions.█ Example Analysis: Misinterpreting Long/Short RatiosImagine you’re looking at a CoinGlass chart that shows an increase in long volume around November 5th. A beginner might see this and think, “Everyone’s bullish on Bitcoin!” But as we discussed, some of this long volume could be non-directional. It could include positions taken by market makers providing liquidity or hedgers who are long on Bitcoin futures but have a corresponding short in another market.Similarly, if you see a spike in the number of short accounts, don’t automatically assume that everyone expects Bitcoin to fall. Some of those accounts might just be managing risk or taking advantage of arbitrage opportunities.█ Avoiding the Pitfall of Overinterpreting the Long/Short RatioThe biggest mistake traders make is interpreting the long/short ratio as a direct indicator of market sentiment. Remember, every trade has a counterparty. If there’s a high volume of longs, it simply means there’s an equal volume of shorts on the other side. The market’s overall sentiment isn’t always reflected in this ratio.Instead of relying solely on the long/short ratio, consider these other factors to form a clearer market view:Market Sentiment Indicators: Use sentiment tools, news, and social media sentiment to understand how traders are feeling beyond just positions.Volume Trends: Look at overall market volume to see if there’s conviction behind the moves.Context and Price Action: Interpret the ratio in the context of price action and recent events. If there’s a strong bullish trend, a higher long ratio might reflect confidence in the trend rather than simply volume.█ Conclusion: A Balanced Perspective for Smarter TradingUnderstanding the long/short ratio requires a more nuanced perspective. Just because the “longs” are up doesn’t mean everyone’s bullish—and just because the “shorts” are up doesn’t mean everyone’s bearish. The futures market is filled with diverse participants, each with unique motives, from hedging and arbitrage to liquidity provision.By looking at these ratios with a balanced view, traders can avoid common pitfalls and interpret the data more accurately. Trading is about context and strategy, not just numbers on a chart. So, next time you’re checking the long/short ratio, remember: there’s more to it than meets the eye.█ Final Takeaway: Focus on Context, Not Just RatiosThe long/short ratio can be a helpful tool, but it’s only one piece of the puzzle. Use it in combination with other market indicators, and always consider the motives behind trades. By doing so, you’ll make better-informed trading decisions and avoid falling into the trap of oversimplifying complex market data.-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ The disposition effect in team investment decisions: Experimental evidenceThe disposition effect is a well-documented phenomenon in behavioral finance. Investors tend to sell winning investments too early and hold onto losing investments for too long. This behavior is primarily driven by emotional responses such as regret and joy. To delve deeper into this bias, a recent study compared the disposition effects in team investment decisions versus individual decisions. Here are the key takeaways, implications for traders, and how we can learn from these findings to improve investment strategies.Summary: Disposition Effect Overview: The disposition effect describes the tendency of investors to sell assets that have increased in value (winners) while holding onto assets that have decreased in value (losers). This behavior is influenced by emotional responses and is explained by theories like prospect theory and mental accounting.Team vs. Individual Investors: The study revealed that team investors exhibit stronger disposition effects compared to individual investors. Teams are more reluctant to realize losses and more prone to selling winners prematurely. This suggests that group dynamics can exacerbate these biases.Emotional Influence: Emotional responses, especially regret, play a crucial role in amplifying the disposition effect in team settings. Teams reported higher levels of regret and rejoice, indicating that group dynamics, such as groupthink and group polarization, heighten these emotions.█ Key FindingsThe results of this study provide compelling evidence about the impact of team dynamics on investment decisions, specifically regarding the disposition effect.⚪ Increased Disposition Effect in Teams: One of the standout findings is that teams exhibited a significantly higher disposition effect than individual investors. Quantitatively, teams were found to sell winning stocks at a rate of 22%, compared to just 17% for individuals. Moreover, they held onto losing stocks with greater tenacity, only selling 13% of such positions compared to 17% for individual traders.⚪ Reluctance to Realize Capital Losses: Teams' reluctance to realize capital losses suggests a heightened aversion to admitting mistakes or confronting poor outcomes when decisions are made collaboratively. This behavioral pattern could be attributed to a psychological mechanism called' loss aversion.'⚪ Premature Sale of Winning Investments: Similarly, teams' tendency to sell winning investments prematurely can be linked to a desire to secure quick wins to validate group decisions. This behavior aligns with the concept of 'resulting,' where the outcome of a decision disproportionately influences one's perception of the decision's quality.The study suggests that psychological phenomena like group thinking and emotional amplification play significant roles in shaping team investment behaviors. These phenomena lead teams to minimize conflict and reach a consensus without critically evaluating alternative viewpoints.█ How Traders Can Benefit from This KnowledgeSelf-awareness and Training: Traders should be trained to recognize the disposition effect in their decision-making processes. By being aware of their tendencies to hold onto losers and sell winners prematurely, they can critically evaluate their decisions and strive for more rational outcomes.Implementation of Structured Decision-Making: Structured decision-making protocols can help traders, especially in team settings, mitigate the influence of emotions. Techniques such as pre-defined selling rules, automatic stop-loss orders, and regular portfolio reviews can reduce emotional biases.Use of Technology: Trading algorithms that follow strict rules for buying and selling can help traders avoid the disposition effect. Additionally, tools that prominently display purchase prices or highlight long-term performance trends can assist traders in making more rational decisions.Nudging Techniques: Implementing nudges such as automatic reminders about initial investment goals or highlighting long-term gains can counteract the immediate emotional responses driving the disposition effect. These nudges can encourage traders to make more balanced decisions.Group Dynamics Management: Teams should be aware of groupthink and group polarization and actively work to counteract these effects through diverse perspectives and critical evaluations. Regular debriefing sessions and third-party evaluations can help teams make more balanced decisions.Adopting these measures could help trading teams counteract the negative aspects of the disposition effect and enhance overall performance by fostering a more disciplined investment approach.█ ConclusionsThe disposition effect is a significant behavioral bias that can adversely affect investment performance. The study demonstrates that this effect is more pronounced in team settings due to amplified emotional responses. By understanding and addressing the emotional drivers behind the disposition effect, traders can develop strategies to mitigate its impact and improve their investment decisions. Structured decision-making, the use of technology, nudging techniques, and proper management of group dynamics are practical ways to combat the disposition effect in both individual and team settings. Embracing these strategies can lead to more rational and profitable investment practices.█ Reference Rau, H. A. (2015). The disposition effect in team investment decisions: Experimental evidence. Journal of Banking & Finance, 61, 272-282. doi:10.1016/j.jbankfin.2015.09.015-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ انطباق با هنجار جدید: چگونه معامله گران می توانند در بازارهای در حال تحول پیشرفت کنند دنیای معاملات دائماً پویا است و استراتژی هایی که زمانی بر بازار مسلط بودند، با تکامل سرمایه گذاران و فناوری، اثربخشی کمتری پیدا می کنند. یک مطالعه جامع اخیر با عنوان "بازارها دقیقا چگونه سازگار می شوند؟ شواهدی از قانون میانگین متحرک در سه بازار توسعه یافته"، نگاهی عمیق به چگونگی کاهش اثربخشی استراتژی های میانگین متحرک (MA) ارائه می دهد که زمانی بسیار موفق بودند، در بازارهایی مانند DJIA، FT30 و TOPIX. این تغییر نه تنها بر ماهیت سازگار بازارهای مالی تأکید می کند، بلکه زنگ هشداری برای معامله گران در سراسر جهان است تا در استراتژی های خود تجدید نظر کنند. در اینجا نحوه انطباق و پیشرفت معامله گران در این چشم انداز جدید معاملاتی آورده شده است. █ شن های روان پیش بینی پذیری بازار از لحاظ تاریخی، میانگین های متحرک سیگنال های قابل اعتمادی را در اختیار معامله گران قرار می دادند که به آنها کمک می کرد حرکات بازار را به طور موثر پیش بینی کنند. با این حال، این مطالعه نشان می دهد که این استراتژی ها به مرور زمان مقداری از قدرت پیش بینی خود را از دست داده اند. این کاهش به اقدامات پیش بینی کننده بازار نسبت داده می شود - معامله گران حتی قبل از اینکه سیگنال ها به طور رسمی ایجاد شوند، به آنها واکنش نشان می دهند. این امر بر نیاز حیاتی معامله گران برای پیشی گرفتن با فعال تر بودن به جای منفعل بودن در استراتژی هایشان تأکید می کند. █ پذیرش فرضیه بازار تطبیقی (AMH) فرضیه بازار تطبیقی ​​نشان می دهد که کارایی بازار یک وضعیت ثابت نیست، بلکه یک وضعیتی است که تکامل می یابد. این فرضیه به خوبی با روندهای مشاهده شده در اثربخشی استراتژی MA مطابقت دارد. معامله گرانی که با ریتم و جریان فعلی بازار سازگار می شوند و درک می کنند که آنچه دیروز کارساز بوده است، ممکن است فردا کارساز نباشد، به احتمال زیاد موفق می شوند. این امر مستلزم یک رویکرد چابک به معاملات است، جایی که استراتژی ها به طور مرتب بررسی و در پاسخ به پویایی های متغیر بازار اصلاح می شوند. █ اهرم کردن پیش بینی برای سودآوری یکی از جنبه های جالب این مطالعه، سودآوری بالقوه معاملات بر اساس سیگنال های پیش بینی شده است. معامله گرانی که می توانند به طور موثر این سیگنال ها را پیش بینی و بر اساس آن اقدام کنند، ممکن است فرصت های پرسودی را حتی در بازاری که شاخص های سنتی در حال تضعیف هستند، بیابند. این رویکرد آینده نگر نیاز به ابزارهای تحلیلی قوی و یک شهود قوی برای احساسات بازار دارد و معامله گران را تشویق می کند تا درک دقیقی از محرک ها و روندهای بازار ایجاد کنند. █ استراتژی هایی برای معامله گر مدرن برای هدایت این چشم انداز تکامل یافته بازار، معامله گران باید چندین تغییر استراتژیک را در نظر بگیرند: ⚪یادگیری مستمر: از روندهای بازار و تغییرات در الگوهای معاملاتی مطلع باشید. معامله گران باید به طور مداوم درک خود را از رفتارهای بازار به روز کنند و استراتژی های خود را بر این اساس تطبیق دهند. تکیه بر مدل های قدیمی یا داده های تاریخی بدون در نظر گرفتن تحول بازار ممکن است منجر به تصمیمات معاملاتی نامطلوب شود. ⚪تنوع بخشیدن به تکنیک ها: روش های سنتی مانند تحلیل تکنیکال را با رویکردهای مدرن مانند یادگیری ماشین و تجزیه و تحلیل داده های بزرگ ترکیب کنید تا یک استراتژی جامع ایجاد کنید. ⚪تطبیق پویا: برای تغییر استراتژی ها به سرعت در پاسخ به اطلاعات جدید یا تغییرات در شرایط بازار آماده باشید. این ممکن است شامل زمان پاسخگویی سریعتر به روندهای نوظهور یا پذیرش سیستم های معاملاتی خودکار باشد که می توانند معاملات را بر اساس معیارهای از پیش تعیین شده انجام دهند. ⚪نظارت بر شرایط بازار: معامله گران باید نسبت به تغییرات در شرایط بازار که می تواند اثربخشی قوانین معاملاتی تثبیت شده را تغییر دهد، هوشیار باشند. این شامل زیر نظر داشتن شاخص های کلان اقتصادی گسترده تر، احساسات بازار و پیشرفت های تکنولوژیکی در معاملات است. ⚪مدیریت ریسک: با افزایش غیرقابل پیش بینی بودن بازار، استراتژی های مدیریت ریسک قوی حتی حیاتی تر می شوند. تنوع بخشیدن به سرمایه گذاری ها و استفاده از دستورات توقف ضرر می تواند به کاهش زیان های احتمالی کمک کند. █ نتیجه گیری تکامل کارایی بازار نشان می دهد آینده ای که در آن انطباق پذیری و دوراندیشی ارزشمندتر از همیشه هستند. برای معامله گران، کلید موفقیت در درک و پیش بینی تغییرات بازار است، نه اینکه صرفاً به داده های تاریخی تکیه کنند. با پیشرفت، توانایی سازگاری، دوران جدید موفقیت معاملاتی را تعریف کرد. در این چشم انداز بازار همیشه در حال تغییر، مطلع ماندن، سازگار بودن و فعال بودن نه تنها مزیت است، بلکه ضروریاتی برای معامله گر مدرن به شمار می رود. ----------------- سلب مسئولیت این یک مطالعه آموزشی فقط برای اهداف سرگرمی است. اطلاعات موجود در اسکریپت ها/شاخص ها/ایده ها/الگوها/سیستم های من به منزله مشاوره مالی یا درخواست برای خرید یا فروش اوراق بهادار نیست. من هیچ مسئولیتی در قبال هیچ گونه ضرر یا خسارت، از جمله بدون محدودیت هرگونه از دست دادن سود، که ممکن است به طور مستقیم یا غیرمستقیم از استفاده یا اتکا به چنین اطلاعاتی ناشی شود، نمی پذیرم. همه سرمایه گذاری ها شامل ریسک هستند و عملکرد گذشته یک اوراق بهادار، صنعت، بخش، بازار، محصول مالی، استراتژی معاملاتی، تست برگشتی یا معاملات فردی، نتایج یا بازده های آتی را تضمین نمی کند. سرمایه گذاران مسئولیت کامل هرگونه تصمیم سرمایه گذاری را که می گیرند بر عهده دارند. چنین تصمیماتی باید صرفاً بر اساس ارزیابی شرایط مالی، اهداف سرمایه گذاری، تحمل ریسک و نیازهای نقدینگی آنها باشد. اسکریپت ها/شاخص ها/ایده ها/الگوها/سیستم های من فقط برای اهداف آموزشی هستند!

█ Diving Into Dark PoolsIn recent years, dark pools have become a significant part of the financial markets, offering an alternative trading venue for institutional traders. But what exactly are dark pools, and how do they impact market quality and price efficiency? This article delves into the comprehensive study titled "Diving Into Dark Pools" by Sabrina Buti, Barbara Rindi, and Ingrid Werner, which sheds light on the complexities of dark pool trading in the US stock market.█ What Are Dark Pools?Dark pools are private financial forums or exchanges for trading securities. Unlike public stock exchanges, dark pools do not display the order book to the public until after the trade is executed, providing anonymity to those placing trades. This lack of pre-trade transparency can help prevent large orders from impacting the market price, which is particularly beneficial for institutional investors looking to trade large volumes without revealing their intentions.█ How Do Dark Pools Work?In dark pools, the details of trades are not revealed to other market participants until the trade is completed. This lack of transparency helps prevent significant price movements that could occur if the order were known beforehand. Dark pools typically execute trades at the midpoint of the best bid and ask price in the public markets, ensuring fair pricing for both parties involved.█ Why Are Dark Pools Used?Dark pools are primarily used by institutional investors who need to execute large trades without revealing their trading intentions. Displaying such large orders on public exchanges could lead to unfavorable price movements due to market speculation and front-running by other traders.█ Benefits of Dark PoolsReduced Market Impact: Large orders can be executed without affecting the stock's market price.Anonymity: Traders can buy or sell significant amounts without revealing their identity or strategy.Lower Transaction Costs: By avoiding the public markets, traders can often reduce the costs associated with large trades.Improved Execution: Dark pools can offer better execution prices due to the lack of market impact and reduced volatility.█ Why Do Large Actors Hide Their Orders Using Dark Pools?Large institutional investors use dark pools to hide their orders to:Avoid Market Manipulation: Prevent others from driving the price up or down based on the knowledge of a large pending trade.Maintain Strategic Advantage: Keep trading strategies and intentions confidential to avoid imitation or counter-strategies by competitors.Achieve Better Prices: Execute trades at more favorable prices by not alerting the market to their actions.█ Actionable Insights for TradersUnderstand Market Dynamics: Knowing how and why dark pools are used can provide insights into market liquidity and price movements.Monitor Market Quality: Be aware that increased dark pool activity can improve overall market quality by reducing volatility and spreads.Assess Price Efficiency: Recognize that while dark pools can enhance market quality, they might also lead to short-term inefficiencies like price overreaction.█ Key Findings from the StudyThe study analyzed unique data on dark pool activity across a large cross-section of US stocks in 2009. Here are some of the critical insights:Concentration in Liquid Stocks: Dark pool activity is predominantly concentrated in liquid stocks. Specifically, Nasdaq stocks show higher dark pool activity compared to NYSE stocks when controlling for liquidity factors.Market Quality Improvement: Increased dark pool activity correlates with improvements in various market quality measures, including narrower spreads, greater depth, and reduced short-term volatility. This suggests that dark pools can enhance market stability and efficiency for certain stocks.Complex Relationship with Price Efficiency: The relationship between dark pool activity and price efficiency is multifaceted. While increased activity generally leads to lower short-term volatility, it can also be associated with more short-term overreactions in price for specific stock groups, particularly small and medium-cap stocks.Impact on Market Dynamics: On days with high share volume, high depth, low intraday volatility, and low order imbalances, dark pool activity tends to be higher. This indicates that traders are more likely to use dark pools when market conditions are favorable for large trades.█ ConclusionDark pools play a crucial role in modern financial markets by allowing large trades to be executed without revealing the trader’s intentions, thus minimizing market impact and reducing costs. For retail traders, understanding the mechanics and implications of dark pools can lead to better-informed trading decisions and a deeper comprehension of market behavior. The study concludes that while dark pools generally contribute to improved market quality by reducing volatility and enhancing liquidity, their effect on price efficiency is nuanced. For small and medium stocks, dark pools can lead to short-term price overreactions, while large stocks remain largely unaffected. The findings underscore the importance of understanding the different impacts on various stock categories to make informed trading decisions.For institutional traders and market participants, understanding the role and impact of dark pools is crucial for navigating the modern financial landscape. By offering an alternative venue for executing large trades discreetly, dark pools play a pivotal role in today's trading ecosystem.█ ReferenceButi, S., Rindi, B., & Werner, I. (2011). Diving into Dark Pools. Charles A. Dice Center for Research in Financial Economics, Fisher College of Business Working Paper Series, 2010-10.-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ How exactly do markets adapt? Evidence from the moving average rule in three developed markets.The Efficient Market Hypothesis (EMH) has long been an important theory in finance.Brought forth by Fama in the 1960s, the EMH suggests that it is impossible to consistently achieve returns over the average market on a risk-adjusted basis, given that price changes should only arise due to new information entering the market.According to the weak form of EMH, this information includes historical price movements. That, by extension, renders technical trading strategies based on past price data theoretically ineffective. However, the dynamic nature of financial markets has given rise to an alternative perspective known as the Adaptive Market Hypothesis (AMH), proposed by Andrew Lo in 2004.The AMH posits that the degree of market efficiency can vary over time due to the interactions of market participants, each adapting to changes within the market environment. This hypothesis allows for the potential profitability of trading rules during periods when markets are less efficient.The moving average (MA) rule serves as a litmus test for the validity of both EMH and AMH. Historically, this rule has enjoyed periods of significant predictive power, famously demonstrated by Brock, Lakonishok, and LeBaron in 1992.The primary objective of this study was to investigate the ongoing effectiveness of the moving average (MA) rule in predicting stock market prices post-1986. Andrew et al. focused on three developed markets: the DJIA in the United States, the FT30 in the United Kingdom, and the TOPIX in Japan.█ Conclusion: The study concluded that the predictive power of the MA rule has significantly diminished in all three markets examined since 1986. This decline in effectiveness aligns with the Adaptive Market Hypothesis (AMH), which posits that market efficiency is not a static condition but evolves as market participants adapt to exploiting profitable opportunities.The findings indicated that while the MA rule was once highly predictive, market participants' increased awareness and adaptation to these trading strategies likely eroded their profitability.█ Methodology ⚪Data Set and TimeframeThe study analyzed the period from 1987 to 2013, carefully selecting data from three major stock indices: the DJIA (US), the FT30 (UK), and the TOPIX (Japan).This timeframe follows the period studied in the original BLL research, allowing for a fresh evaluation of the MA rule in a contemporary market context.⚪Analytical Techniques UsedThe study used a comparative analysis of the MA rule against a traditional buy-and-hold strategy. It serves as a benchmark for market performance over time. By evaluating the returns generated by following the MA signals versus simply holding stocks, it aimed to determine the rule's effectiveness in generating excess returns.Additionally, the analysis included a detailed examination of market reactions to buy and sell signals generated by the MA rule. This approach assessed the immediate impact of these signals on stock prices and looked at how quickly and efficiently the markets absorbed this information.█ Key FindingsAcross all three markets studied—DJIA, FT30, and TOPIX—the findings consistently showed a decline in the predictive power of the MA rule post-1986. This trend was evident in the reduced profitability of strategies based on this rule.⚪Market Adaptation to Trading SignalsThe study revealed significant insights into how markets have adapted to trading signals. It appears that as market participants have become more sophisticated, the ability of traditional trading rules like the MA to outperform simpler strategies has decreased.This adaptation may be partly due to the increased predictability of market reactions to known trading signals, leading to quicker adjustments in stock prices.⚪Anticipation of MA Signals and Shift in StrategyOne of the more novel findings from the study was the shift in how traders anticipate MA signals. Traders, aware of the historical profitability of these signals, have begun to preemptively act on expected signals rather than waiting for the signals to be formally generated.This anticipation leads to a scenario where actual trading on the anticipated signals the day before their formal generation often yielded superior profits compared to following the signals post-generation.This shift in strategy underscores a more proactive approach among traders, who rely on forecasting and predictive models to stay ahead of traditional signal-generation techniques.█ Implications for Market ParticipantsThe findings suggest that traders who have relied heavily on MA strategies should reassess their trading approaches. While MA strategies may not need to be completely discarded, they should be used with a grain of salt alongside other comprehensive tools for analysis.The decreased predictability of returns using MA rules supports the Efficient Market Hypothesis (EMH). This confirms the hypothesis that markets may efficiently reflect all known information, including known trading strategies like MA, thus negating their effectiveness over time.On the other hand, the study strongly supports the Adaptive Market Hypothesis (AMH), emphasizing that market efficiency is not a static state but varies over time with the actions of market participants.The AMH's view that trading strategies can ebb and flow in effectiveness depending on market conditions is corroborated by the varying success rates of MA strategies over different periods and markets.In the context of moving averages, which are often used to identify trends by smoothing out price data over a specified period, their effectiveness can change. For instance, in a highly volatile market, MA strategies might generate many false signals, leading to poor performance. Conversely, in a trending market with less volatility, MA strategies could be quite successful. This variation in success rates across different times and market environments supports the AMH view that the profitability of trading strategies can fluctuate as market dynamics evolve.TrendConsolidation█ Study LimitationsWhile the study provides insightful findings, it has certain limitations that should be noted.Firstly, focusing on only three developed markets—DJIA, FT30, and TOPIX—may not fully represent global market dynamics. The behaviors and trends in these markets might not be universally applicable, especially in less developed or emerging markets.Additionally, the study's methodology does not account for transaction costs, which could significantly impact the profitability and practical application of MA strategies in a real-world trading environment. █ ReferenceUrquhart, A., Gebka, B., & Hudson, R. (2015). How exactly do markets adapt? Evidence from the moving average rule in three developed markets. Journal of International Financial Markets, Institutions & Money, 38, 127-147. doi:10.1016/j.intfin.2015.05.019-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

█ Adaptive versus Simple Moving Average Trading Strategies. Is smarter really better?Computer-aided trading systems have revolutionized the way trading decisions are made. We now employ sophisticated algorithms to predict market movements and execute trades at optimal times. Among these, moving average(MA) strategies stand out for their simplicity and effectiveness among the many available strategies. This study by Craig A. Ellis and Simon A. Parbery compares two prominent MA strategies: the Adaptive Moving Average(AMA) and the Simple Moving Average(SMA).Conclusion: While adaptive moving average strategies may provide an edge in certain market conditions by capturing trends more efficiently than simple moving averages, investors must carefully consider transaction costs.These costs can significantly impact net returns, particularly in frequent trading strategies. Findings suggest that the effectiveness of adaptive versus simple moving average trading strategies is nuanced in varying market conditions, with no one-size-fits-all answer. Investors should weigh the potential benefits of adaptability against the increased costs and risks associated with such strategies.█ Moving Average Trading SystemsAmong the various types of moving averages, the Simple Moving Average(SMA) and the Adaptive Moving Average(AMA) are particularly noteworthy due to their distinct characteristics and applications in trading strategies.⚪ Simple Moving Average and Its CalculationSMA is one of the most basic moving averages in trading. It calculates the average price of a security over a defined number of periods. The SMA is straightforward to compute; you sum up the security's closing prices for a set number of periods and then divide this total by the number of periods.This process results in a smooth line that traders can overlay on their price charts to assess the direction of the trend. For example, a 20-day SMA would add up the closing prices of the past 20 days and divide the total by 20. This calculation is continuously updated as new closing prices become available, giving traders a dynamic view of the market's trend.Pine Script®// Function to calculate the SMA using an array sma(source, length) => // Initialize an array to hold the prices prices = array.new_float(length) // Fill the array with the most recent `length` prices for i = 0 to length - 1 array.set(prices, i, source) // Calculate the sum of the array elements sum = array.sum(prices) // Return the average sum / lengthExpand 6 lines⚪ Adaptive Moving Average and Its CalculationThe Adaptive Moving Average (AMA), proposed by Perry Kaufman in his book "New Trading Systems and Methods," represents a significant advancement in moving average technology. Unlike the SMA, which gives equal weight to all data points, the AMA adjusts its sensitivity based on the market's volatility. This adaptability makes the AMA particularly useful in identifying market trends with varying degrees of volatility.The core of the AMA's adaptability lies in its Efficiency Ratio (ER), which measures the directionality of the market over a given period. The ER is calculated by dividing the absolute change in price over a period by the sum of the absolute differences in daily prices over the same period.Pine Script®// Calculate the Efficiency Ratio (ER) change = math.abs(close - close[length]) volatility = math.sum(math.abs(close - close[1]), length) ER = change / volatilityThe ratio helps determine how efficiently the price is moving in one direction. A higher ER indicates a more directional market, prompting the AMA to react quickly to price changes. A lower ER suggests a consolidating market, leading the AMA to respond more to recent price changes.█ Data and Research MethodologyThe data set encompasses daily closing prices for three major stock indices: the Australian All Ordinaries, the Dow Jones Industrial Average (DJIA), and the S&P 500, spanning from 1980 to 2002. This period provides a comprehensive view of market behavior, including various economic cycles, bull and bear markets, and periods of high volatility. Such a diverse data set is crucial for testing the robustness of the AMA in different market environments.This study investigates whether AMA's adaptive nature results in superior performance compared to the more static SMA and the passive buy-hold approach. The key steps in the research methodology include:Parameter Selection: Identifying optimal parameters for both AMA and SMA to ensure a fair comparison. This involves selecting the look-back periods and thresholds for triggering buy or sell signals.Strategy Implementation: Developing trading strategies based on AMA, SMA, and a buy-hold benchmark. Each strategy is applied to the data set to simulate real-world trading, with buy or sell signals generated according to the specific rules of each approach.Performance Evaluation: The performance of each strategy is assessed using several metrics, including total return, risk-adjusted return, and maximum drawdown.This comprehensive evaluation aims to determine the effectiveness of AMA in navigating various market conditions compared to SMA and buy-hold strategies.Statistical Testing: Conducting statistical tests to ascertain the significance of the differences in performance outcomes among the strategies. This includes tests for statistical significance in returns and risk metrics, providing a robust framework for comparison.Sensitivity Analysis: Exploring how changes in the parameters of AMA and SMA affect the strategies' performance. This analysis helps understand the flexibility and adaptability of AMA in response to different market dynamics█ ResultsThe empirical analysis focused on comparing the performance of Adaptive Moving Average (AMA) and Simple Moving Average (SMA) strategies across a variety of indices, including the S&P 500, Dow Jones Industrial Average (DJIA), and NASDAQ.The performance metrics were primarily based on the total return over the investment period, the Sharpe ratio, and the maximum drawdown to assess each strategy's risk-adjusted returns and resilience during market downturns.The table demonstrates that the AMA strategy consistently outperformed the SMA strategy across all indices regarding total return and Sharpe ratio, indicating a superior risk-adjusted return. However, it's important to note that the AMA strategy also experienced slightly higher drawdowns than the SMA in certain instances, suggesting a potentially higher risk during market downturns.⚪ In discussing the market timing ability of AMA, the analysis found that AMA could better adapt to changing market conditions, thereby capturing trends more efficiently than the SMA strategy. This adaptability resulted in higher returns during periods of significant market movements. However, when accounting for transaction costs, the advantage of AMA over SMA diminished, particularly in markets characterized by frequent, small movements that triggered more trading activity by the AMA strategy.█ ReferenceEllis, C. A., & Parbery, S. A. (2005). Is smarter better? A comparison of adaptive, and simple moving average trading strategies. Research in International Business and Finance, 19, 399-411.-----------------DisclaimerThis is an educational study for entertainment purposes only.The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!