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Machinelearning

Is AMD Poised to Redefine the Future of AI and Computing?Advanced Micro Devices (AMD) is rapidly transforming its market position, recently converting a Wall Street skeptic, Melius Research, into a bullish advocate. Analyst Ben Reitzes upgraded AMD stock to "buy" from "hold," significantly raising the price target to \$175 from \$110, citing the company's substantial progress in artificial intelligence (AI) chips and computing systems. This optimistic outlook is fueled by a confluence of factors, including surging demand from hyperscale cloud providers and sovereign entities, alongside colossal revenue opportunities in AI inferencing workloads. Another upgrade from CFRA to "strong buy" further underscores this shifting perception, highlighting AMD's new product launches and an expanding customer base, including key players like Oracle and OpenAI, for its accelerator technology and the maturing ROCm software stack. AMD's advancements in the AI accelerator market are particularly noteworthy. The company's MI300 series, including the MI300X with its industry-leading 192GB HBM3 memory, and the newly unveiled MI350 series, are designed to deliver significant price and performance advantages over rivals like Nvidia's H100. At its "Advancing AI 2025" event on June 12, AMD not only showcased the MI350's potential for up to 38x improvement in energy efficiency for AI training but also previewed "Helios" full-rack AI systems. These comprehensive, plug-and-play solutions, leveraging future MI400 series GPUs and Zen 6-based EPYC "Venice" CPUs, position AMD to directly compete for the lucrative business of hyperscale operators. As AI inference workloads are projected to consume 58% of AI budgets, AMD's focus on efficient, scalable AI platforms puts it in a prime position to capture a growing share of the rapidly expanding AI data center market. Beyond AI, AMD is pushing the boundaries of traditional computing with its upcoming Zen 6 Ryzen CPUs, reportedly targeting "insane" clock speeds, well above 6 GHz, with some leaks suggesting peaks of 6.4-6.5 GHz. Built on TSMC's advanced 2nm lithography node, the Zen 6 architecture, developed by the same team behind the successful Zen 4, promises significant architectural improvements and a substantial increase in performance per clock. While these are leaked targets, the combination of AMD's proven design capabilities and TSMC's cutting-edge process technology makes these ambitious clock speeds appear highly achievable. This aggressive strategy aims to deliver compelling performance gains for PC enthusiasts and enterprise users, further solidifying AMD's competitive stance against Intel's forthcoming Nova Lake CPUs, which are also expected around 2026 and feature a modular design and up to 52 cores.
NASDAQ:AMDLong
by UDIS_View
Is PayPal's Dominance Built on Tech and Ties?PayPal strategically positions itself at the forefront of digital commerce by combining advanced technological capabilities with key partnerships. A core element of this strategy is the company's robust fraud prevention infrastructure, heavily reliant on sophisticated machine learning. By analyzing vast datasets from its extensive user base, PayPal's systems proactively detect and mitigate fraudulent activities in real time, providing a critical layer of security for consumers and businesses in an increasingly complex online environment. This technological edge is particularly vital in markets facing elevated fraud risks, where tailored solutions offer enhanced protection. The company actively pursues strategic collaborations to expand its reach and integrate its services into new digital ecosystems. The partnership with Perplexity to power "agentic commerce" exemplifies this, embedding PayPal's secure checkout solutions directly within AI-driven chat interfaces. This move anticipates the future of online shopping, where AI agents will facilitate transactions. Furthermore, initiatives like PayPal Complete Payments demonstrate a commitment to empowering businesses globally, offering a unified platform for accepting diverse payment methods across numerous markets, optimizing financial operations, and reinforcing security measures. PayPal also adeptly navigates regulatory landscapes to broaden its service offerings and enhance user convenience. Responding to directives like the EU's Digital Markets Act, PayPal has enabled contactless payments on iPhones in Germany, providing consumers with a direct alternative to existing mobile payment options. This ability to leverage regulatory changes to expand accessibility and choice, coupled with its foundational technological strength and strategic alliances, underpins PayPal's assertive approach to maintaining its leadership position in the dynamic global payments market.
NASDAQ:PYPLLong
by UDIS_View
Moonshot Ideas $CFLT > $40- We are entering era of Agentic AI which requires real time data and agent decides to take an action on that event in near real time. - Confluent valuation is so cheap in a massive TAM and an exploding tailwind of AI Agents. I am seeing all the tech companies and startups are building AI Agents and apetite for real time data streaming capability, processing is required. - NASDAQ:CFLT need to ride this wave and one way they could do is by making Apache Flink to natively support Python instead of just relying on Java. If they build sdk, python library which could do the heavy lifting when it comes to stream processing, transformation then NASDAQ:CFLT would be number one choice and popular among growing Python community and ML community. - I wouldn't be surprised to see NASDAQ:CFLT well above 60 dollars in next 2-3 years.
NASDAQ:CFLTLong
by bigbull037
Machine Learning Algorithms for Forex Market AnalysisMachine Learning Algorithms for Forex Market Analysis Machine learning is transforming the currency trading landscape, offering innovative ways to analyse market trends. This article delves into how machine learning algorithms are reshaping forex trading. Understanding these technologies' benefits and challenges provides traders with insights to navigate the currency markets potentially more effectively, harnessing the power of data-driven decision-making. The Basics of Machine Learning in Forex Trading Machine learning for forex trading marks a significant shift from traditional analysis methods. At its core, machine learning involves algorithms that learn from and provide signals based on data. Unlike standard trading algorithms, which operate on predefined rules, these algorithms adapt and improve over time with exposure to more data. Machine learning forex prediction algorithms analyse historical and real-time market data, identifying patterns that are often imperceptible to the human eye. They can process a multitude of technical and fundamental factors simultaneously, offering a more dynamic approach to analysing market trends. This capability can allow traders to make more informed decisions about when to buy or sell currency pairs. The increasing availability of market data and advanced computing power has made machine learning an invaluable tool in a trader's arsenal. Types of Machine Learning Algorithms in Forex Trading In the realm of forex trading, various machine-learning algorithms are utilised to decipher complex market patterns and determine future currency movements. These algorithms leverage forex datasets for machine learning, which encompass historical price data, economic indicators, and global financial news, to train models for accurate analysis. - Support Vector Machines (SVMs): SVMs are particularly adept at classification tasks. In forex, they analyse datasets to categorise market trends as bullish or bearish, helping traders in decision-making. - Neural Networks: These mimic human brain functioning and are powerful in recognising subtle patterns in market datasets. They are often embedded in forex forecasting software to determine future price movements based on historical trends and fundamental data. - Linear Regression: This straightforward approach models the relationship between dependent and independent variables in forex data. It's commonly used for its simplicity and effectiveness in identifying trends. - Random Forest: This ensemble learning method combines multiple decision trees to potentially improve analysis accuracy and reduce overfitting, making it a reliable choice in the forex market analysis. - Recurrent Neural Networks (RNNs): Suited for sequential data, RNNs can be effective in analysing time-series market data, capturing dynamic changes over time. - Long Short-Term Memory (LSTM) Networks: A specialised form of RNNs, LSTMs are designed to remember long-term dependencies, making them effective tools for analysing extensive historical forex datasets. Benefits of Machine Learning in Forex Trading Machine learning offers significant advantages for forex analysis. Its integration into forex prediction software may enhance trading strategies in several key ways: - Real-Time Data Analysis: Algorithms excel in analysing vast amounts of real-time data, which is crucial for accurate forex daily analysis and prediction. - Automated Trading: These algorithms automate the buying and selling process, which may increase efficiency and reaction speed to market changes. - Enhanced Market Understanding: It helps in dissecting historical market data, providing a deeper understanding for informed decision-making. - Accuracy in Analysis: Software powered by machine learning offers superior analysis abilities, leading to potentially more precise and timely trades. - Risk Reduction: By minimising human error and maintaining consistency, machine learning may reduce trading risks, contributing to a safer trading environment. Challenges and Limitations Machine learning in currency trading, while transformative, comes with its own set of challenges and limitations: - Data Quality and Availability: Accurate machine learning analysis depends on large volumes of high-quality data. Forex markets can produce noisy or incomplete data, which can compromise the reliability of the analysis and signals. - Complexity and Overfitting: Developing effective algorithms for forex trading is complex. There's a risk of overfitting, where models perform well on training data but poorly in real-world scenarios. - Interpretability Issues: Machine learning models, especially deep learning algorithms, can be "black boxes," making it difficult to understand how decisions are made. This lack of transparency can be a hurdle in regulatory compliance and trust-building. - Regulatory Challenges: Currency markets are heavily regulated, and incorporating machine learning must align with these regulatory requirements, which can vary significantly across regions. - Cost and Resource Intensive: Implementing machine learning requires significant computational resources and expertise, which can be costly and resource-intensive, especially for smaller trading firms or individual traders. The Bottom Line Machine learning represents a paradigm shift in forex trading – it may offer enhanced analysis accuracy and decision-making capabilities. While challenges like data quality, complexity, and regulatory compliance persist, the benefits of advanced algorithms in understanding and navigating market dynamics are undeniable. For those looking to trade forex, opening an FXOpen account could be a step towards a wide range of markets, lightning execution and tight spreads. This article represents the opinion of the Companies operating under the FXOpen brand only. It is not to be construed as an offer, solicitation, or recommendation with respect to products and services provided by the Companies operating under the FXOpen brand, nor is it to be considered financial advice.
Education
by FXOpen
1
Behind the Curtain: Top Economic Influencers on ZN Futures1. Introduction The 10-Year Treasury Note Futures (ZN), traded on the CME, are a cornerstone of the fixed-income market. As a vital benchmark for interest rate trends and macroeconomic sentiment, ZN Futures attract institutional and retail traders alike. Their liquidity, versatility, and sensitivity to economic shifts make them a go-to instrument for both speculation and hedging. In this article, we delve into the economic forces shaping ZN Futures’ performance across daily, weekly, and monthly timeframes. By leveraging machine learning, specifically a Random Forest Regressor, we identify the most impactful indicators influencing Treasury futures returns. These insights can help traders fine-tune their strategies and navigate the complexities of this market. 2. Product Specifications Contract Size: The standard ZN Futures contract represents $100,000 face value of 10-Year Treasury Notes. Tick Size: Each tick corresponds to 1/64 of 1% of par value. This equals $15.625 per tick, ensuring precise pricing and manageable risk for traders. Margins: Approximately $2,000 per contract (changes through time). Micro Contract Availability: While the standard contract suits institutional traders, the micro-sized Yield Futures provide a smaller-scale option for retail participants. These contracts offer reduced tick values and margin requirements, enabling broader market participation. 3. Daily Economic Drivers Machine learning models reveal that daily fluctuations in ZN Futures are significantly influenced by the following indicators: Building Permits: A leading indicator of housing market activity, an increase in permits signals economic confidence and growth. This optimism often puts upward pressure on yields, while a decline may reflect economic caution, boosting demand for Treasuries. U.S. Trade Balance: This metric measures the difference between exports and imports. A narrowing trade deficit typically signals improved economic health, leading to higher yields. Conversely, a widening deficit can weaken economic sentiment, increasing Treasury demand as a safe-haven asset. China GDP Growth Rate: As a global economic powerhouse, China’s GDP growth influences global trade and financial flows. Strong growth suggests robust international demand, pressuring Treasury prices downward as yields rise. Slower growth has the opposite effect, enhancing Treasury appeal. 4. Weekly Economic Drivers When analyzing weekly timeframes, the following indicators emerge as significant drivers of ZN Futures: Velocity of Money (M2): This indicator reflects the speed at which money circulates in the economy. High velocity signals robust economic activity, often putting upward pressure on yields. Slowing velocity, on the other hand, may indicate stagnation, increasing demand for Treasury securities. Consumer Sentiment Index: This metric gauges the confidence level of consumers regarding the economy. Rising sentiment suggests stronger consumer spending and economic growth, often pressuring bond prices downward as yields rise. Conversely, a decline signals economic caution, favoring safe-haven assets like ZN Futures. Nonfarm Productivity: This measures output per hour worked in the nonfarm sector and serves as an indicator of economic efficiency. Rising productivity typically reflects economic strength and may lead to higher yields, while stagnation or declines can shift sentiment toward Treasuries. 5. Monthly Economic Drivers On a broader monthly scale, the following indicators play a pivotal role in shaping ZN Futures: Net Exports: This metric captures the difference between a country’s exports and imports. A surplus indicates strong global demand for domestic goods, signaling economic strength and driving yields higher. Persistent deficits, however, may weaken economic sentiment and increase demand for Treasuries as a safe haven. 10-Year Treasury Yield: As a benchmark for longer-term borrowing costs, movements in the 10-Year Treasury Yield reflect investor expectations for economic growth and inflation. Rising yields suggest optimism about future economic conditions, potentially reducing demand for Treasury futures. Declining yields indicate caution, bolstering Treasury appeal. Durable Goods Orders: This indicator measures new orders placed with manufacturers for goods expected to last three years or more. Rising orders signal business confidence and economic growth, often leading to higher yields. Conversely, a decline in durable goods orders can indicate slowing economic momentum, increasing Treasury demand. 6. Applications for Different Trading Styles Economic indicators provide distinct insights depending on the trading style and timeframe: Day Traders: Focusing on daily indicators like Building Permits, U.S. Trade Balance, and China GDP Growth Rate to anticipate short-term market movements. For example, an improvement in China’s GDP Growth Rate may signal stronger global economic conditions, potentially driving yields higher and pressuring ZN Futures lower. Swing Traders: Weekly indicators such as Velocity of Money (M2), Consumer Sentiment Index, and Nonfarm Productivity could help identify intermediate trends. For instance, rising consumer sentiment can reflect increased spending expectations, potentially prompting bearish positions in ZN Futures. Position Traders: Monthly metrics like Net Exports, 10-Year Treasury Yield, and Durable Goods Orders may offer a macro perspective for long-term strategies. A sustained increase in durable goods orders, for instance, may indicate economic expansion, influencing traders to potentially adopt bearish sentiment on ZN Futures. 7. Conclusion The analysis highlights how daily, weekly, and monthly economic indicators collectively influence ZN Futures. From more immediate fluctuations driven by Building Permits and China GDP Growth Rate, to longer-term trends shaped by Durable Goods Orders and the 10-Year Treasury Yield, each timeframe provides actionable insights for traders. By understanding these indicators and incorporating machine learning models to uncover patterns, traders can refine strategies tailored to specific time horizons. Whether intraday, swing, or long-term, leveraging these insights empowers traders to navigate ZN Futures with greater precision. Stay tuned for the next installment in the "Behind the Curtain" series, where we examine economic drivers behind another key futures market. When charting futures, the data provided could be delayed. Traders working with the ticker symbols discussed in this idea may prefer to use CME Group real-time data plan on TradingView: www.tradingview.com - This consideration is particularly important for shorter-term traders, whereas it may be less critical for those focused on longer-term trading strategies. General Disclaimer: The trade ideas presented herein are solely for illustrative purposes forming a part of a case study intended to demonstrate key principles in risk management within the context of the specific market scenarios discussed. These ideas are not to be interpreted as investment recommendations or financial advice. They do not endorse or promote any specific trading strategies, financial products, or services. The information provided is based on data believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Trading in financial markets involves risks, including the potential loss of principal. Each individual should conduct their own research and consult with professional financial advisors before making any investment decisions. The author or publisher of this content bears no responsibility for any actions taken based on the information provided or for any resultant financial or other losses.
CBOT:ZN1!Education
by traddictiv
8
Behind the Curtain: Economic Indicators Shaping Corn Futures1: Introduction Corn Futures (ZC), traded on the CME, play a vital role in global markets, particularly in the agriculture and food industries. As a commodity with widespread applications, Corn Futures are influenced by a multitude of factors, ranging from seasonal weather patterns to broader economic trends. Understanding these influences is critical for traders seeking to navigate the market effectively. In this article, we leverage machine learning, specifically a Random Forest Regressor, to identify key economic indicators that have historically correlated with Corn Futures' price changes. By analyzing daily, weekly, and monthly timeframes, we aim to provide a clearer picture of how these indicators potentially shape market behavior and offer actionable insights for traders. The findings are presented through visual graphs highlighting the top economic indicators across different timeframes. These insights can help traders fine-tune their strategies, whether for short-term speculation or long-term investment. 2: Understanding the Key Economic Indicators Economic indicators provide a glimpse into various facets of the economy, influencing commodity markets such as Corn Futures. Using the Random Forest model, the following indicators emerged as significant for Corn Futures on different timeframes: Daily Timeframe: Oil Import Price Index: Reflects the cost of importing crude oil, impacting energy costs in agriculture, such as fuel for equipment and transportation. Durable Goods Orders: Tracks demand for goods expected to last three years or more, often signaling broader economic activity that can influence commodity demand. Natural Gas Prices: Critical for the production of fertilizers, which directly impacts corn farming costs. Weekly Timeframe: China GDP Growth Rate: Indicates global demand trends, as China is a major consumer of agricultural products. Housing Starts: Reflects construction activity, indirectly influencing economic stability and consumer behavior. Corporate Bond Spread (BAA - 10Y): A measure of credit risk that can signal changes in business investment and economic uncertainty. Monthly Timeframe: Retail Sales (YoY): Gauges consumer spending trends, a crucial driver of demand for corn-based products. Initial Jobless Claims: Acts as a measure of labor market health, influencing disposable income and consumption patterns. Nonfarm Productivity: Indicates economic efficiency and growth, impacting broader market trends. By understanding these indicators, traders can interpret their implications on Corn Futures more effectively. 3: How to Use This Information The timeframes for these indicators provide unique perspectives for different trading styles: Daily Traders: Indicators like the Oil Import Price Index and Natural Gas Prices, which are highly sensitive to short-term changes, are valuable for high-frequency trading strategies. Daily traders can monitor these to anticipate intraday price movements in Corn Futures. Swing Traders (Weekly): Weekly indicators, such as the China GDP Growth Rate or Housing Starts, help identify intermediate-term trends. Swing traders can align their positions with these macroeconomic signals for trades lasting several days or weeks. Long-Term Traders (Monthly): Monthly indicators, such as Retail Sales and Nonfarm Productivity, provide insights into overarching economic trends. Long-term traders can use these to assess demand-side factors impacting Corn Futures over extended periods. Additionally, traders can enhance their strategies by overlaying these indicators with seasonal patterns in Corn Futures, as weather-related supply shifts often coincide with economic factors. 4: Applications for Risk Management Understanding the relationship between economic indicators and Corn Futures also plays a critical role in risk management. Here are several ways to apply these insights: Refining Entry and Exit Points: By correlating Corn Futures with specific indicators, traders can potentially time their entries and exits more effectively. For example, a sharp rise in the Oil Import Price Index might signal increased production costs, potentially pressuring corn prices downward. Diversifying Trading Strategies: Leveraging daily, weekly, and monthly indicators allows traders to adapt their strategies across timeframes. Short-term volatility from energy prices can complement long-term stability signals from broader economic metrics like GDP Growth. Mitigating Uncertainty: Tracking indicators such as Corporate Bond Spreads can provide early warnings of economic instability, helping traders hedge their Corn Futures positions with other assets or options. Seasonal Hedging: Combining indicator-based insights with seasonal trends in Corn Futures can enhance risk-adjusted returns. For instance, aligning hedging strategies with both economic and weather-related factors could reduce downside exposure. 5: Conclusion The analysis highlights how diverse economic indicators shape Corn Futures prices across multiple timeframes. From daily volatility influenced by energy costs to long-term trends driven by consumer spending and productivity, each indicator provides unique insights into market dynamics. Traders can use this framework not only for Corn Futures but also for other commodities, enabling a more data-driven approach to trading. The combination of machine learning and economic analysis presents opportunities to refine strategies and improve outcomes in the competitive world of futures trading. Stay tuned for the next article in this series, where we delve into another futures market and its relationship with key economic indicators. When charting futures, the data provided could be delayed. Traders working with the ticker symbols discussed in this idea may prefer to use CME Group real-time data plan on TradingView: www.tradingview.com - This consideration is particularly important for shorter-term traders, whereas it may be less critical for those focused on longer-term trading strategies. General Disclaimer: The trade ideas presented herein are solely for illustrative purposes forming a part of a case study intended to demonstrate key principles in risk management within the context of the specific market scenarios discussed. These ideas are not to be interpreted as investment recommendations or financial advice. They do not endorse or promote any specific trading strategies, financial products, or services. The information provided is based on data believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Trading in financial markets involves risks, including the potential loss of principal. Each individual should conduct their own research and consult with professional financial advisors before making any investment decisions. The author or publisher of this content bears no responsibility for any actions taken based on the information provided or for any resultant financial or other losses.
CBOT:ZC1!Education
by traddictiv
Can a Crystal Ball Really Predict the Future of Tech?In an era where artificial intelligence promises to reshape the technological landscape, Palantir Technologies has emerged as a testament to the power of long-term vision meeting present opportunity. The company's remarkable third-quarter performance, marked by a 30% revenue surge to $725.5 million and doubled net income, isn't merely a financial triumph—it's a validation of two decades spent perfecting the art of data analytics while others were still grappling with its fundamentals. What sets this trajectory apart is Palantir's unique ability to bridge two seemingly disparate worlds. On one side, its deep-rooted expertise in government and defense contracts, evidenced by a 40% growth in U.S. government sales to $320 million, demonstrates unparalleled capability in handling sensitive, mission-critical data. On the other, its commercial division's explosive growth, particularly in the U.S. market with a 54% revenue increase, reveals an organization that has successfully translated complex government-grade technology into practical business solutions. The company's strategic positioning, however, tells a more intriguing story beyond the numbers. While competitors scramble to adapt to the AI revolution, Palantir's Artificial Intelligence Platform (AIP) represents the culmination of years spent understanding the nuances of data integration and security. This foundation, combined with innovative approaches like their hands-on "boot camps" where clients work directly with Palantir engineers, suggests that perhaps the company named after Tolkien's all-seeing orbs has indeed developed a knack for anticipating the future of enterprise technology.
PLong
by MorMedia
Is the Future of Agreements AI-Powered?In today's rapidly evolving digital landscape, the way we conduct business is undergoing a profound transformation. One area that has seen significant disruption is the management of agreements. Traditional paper-based processes are being replaced by electronic solutions, and at the forefront of this revolution is DocuSign. DocuSign has not only pioneered the use of electronic signatures but has also taken a significant step forward by integrating artificial intelligence (AI) into its agreement management platform. This strategic move has positioned DocuSign as a leader in the industry, offering unparalleled efficiency and value to its customers. By leveraging AI, DocuSign's Intelligent Agreement Management (IAM) platform can automate and streamline various aspects of the agreement lifecycle, from creation and negotiation to execution and management. This not only saves time and reduces errors but also provides valuable insights and analytics that can help businesses optimize their operations. Beyond its technological advancements, DocuSign has also demonstrated a strong financial performance, reflecting its ability to capitalize on market opportunities and execute its growth strategy. The company's expansion into new markets and strategic partnerships further solidify its position as a leader in the industry. As we look to the future, it is clear that AI-powered agreement management will play a crucial role in shaping the way businesses operate. DocuSign's commitment to innovation and its strong financial performance make it well-positioned to continue leading the way in this transformative field.
NASDAQ:DOCULong
by MorMedia
A New President's Potential Impact on Oil Prices1. Introduction The U.S. presidential election in 2024 is set to bring new leadership, with a new president guaranteed to take office. As history has shown, political transitions often have a profound effect on financial markets, and crude oil is no exception. Traders, investors and hedgers are now asking the critical question: how will WTI Crude Oil futures react to this change in leadership? While there is much speculation about how a Democrat versus a Republican might shape oil policy, data-driven insights provide a more concrete outlook. Using a machine learning model based on key U.S. economic indicators, we’ve identified potential movements in crude oil prices, spanning short, medium, and long-term timeframes. 2. Key Machine Learning Predictions for Crude Oil Prices Short-Term (1 Week to 1 Month): Based on the machine learning model, the immediate market reaction within the first week following the election is expected to be minimal, with predicted price changes below 2% for both a Republican and Democratic win. The one-month outlook also suggests additional opportunity. Medium-Term (1 Quarter to 1 Year): The model shows a significant divergence in crude oil prices over the medium term, with a potential sharp upward movement one year after the election. Regardless of which party claims the presidency, WTI crude oil prices could potentially rise by over 40%. This is in line with historical trends where significant price shifts occurred one year post-election, driven by economic recovery, fiscal policies, and broader market sentiment. Long-Term (4 Years): Over the course of the full four-year presidential term, the model predicts more moderate growth, averaging around 15%. The data suggests that, while short-term market movements may seem reactive, the long-term outlook is more balanced and less influenced by the winning party. Instead, economic conditions, such as interest rates and industrial activity, will have a more sustained impact on crude oil prices. 3. Feature Importance: The Drivers Behind Crude Oil Price Movements The machine learning model's analysis highlights that crude oil price movements, especially one year after the election, are primarily driven by economic indicators, rather than the political party in power. Below are the top features influencing crude oil prices: Top Economic Indicators Influencing Crude Oil: Fed Funds Rate: The most significant driver of crude oil prices, as interest rate policies affect everything from borrowing costs to overall economic growth. Changes in the Fed Funds Rate can signal shifts in economic activity that directly impact oil demand apart from the US Dollar itself. Labor Force Participation Rate: A critical indicator of economic health, a higher participation rate suggests a stronger labor market, which supports increased industrial activity and energy consumption, including crude oil. Producer Price Index (PPI): The PPI reflects inflation at the producer level, impacting the cost of goods and services, including oil-related industries. Consumer Sentiment Index: A measure of the general public's outlook on the economy, which indirectly influences energy demand as consumer confidence affects spending patterns. Unit Labor Costs: An increase in labor costs can signal inflationary pressures, which could lead to changes in oil prices as businesses pass on higher costs to consumers. This study exclusively uses U.S. economic data, excluding oil-related fundamentals such as OPEC+ supply and demand information, in order to focus on the election’s direct impact through domestic economic channels. Minimal Influence of Political Party on Price Movements: Interestingly, the machine learning model suggests that the political party of the newly elected president has a relatively low impact on crude oil prices. The performance of WTI crude oil appears to be more closely tied to macroeconomic factors, such as employment data and inflation, than the specific party in power. These findings emphasize the importance of focusing on economic fundamentals when analyzing crude oil price movements for longer term exposures, rather than solely relying on political outcomes. 4. Historical Analysis of Crude Oil Price Reactions to U.S. Elections Looking back over the last two decades, the performance of crude oil post-election has varied, depending on global conditions and the economic policies of the newly elected president. Notable Historical Movements: George W. Bush (Republican): In his 2000 election, crude oil dropped nearly 50% within a year, reflecting the broader economic fallout from the bursting of the dot-com bubble and the events of 9/11. In contrast, his 2004 re-election saw oil prices climb 21.5% within a year, driven by the Iraq War and increasing global demand for energy. Barack Obama (Democratic): After his 2008 election, crude oil prices surged by 33.8% within one year, partly due to economic recovery efforts following the global financial crisis. His 2012 re-election saw more modest growth, with an 8.3% rise over the same period. Donald Trump (Republican): His election in 2016 coincided with a moderate 23.8% increase in crude oil prices over one year, as the U.S. ramped up energy production through fracking, contributing to global supply increases. Joe Biden (Democratic): Most recently, crude oil prices skyrocketed by over 100% in the year following Biden’s 2020 victory, driven by post-pandemic economic recovery and supply chain disruptions that affected global energy markets. 5. WTI Crude Oil Contracts: CL and MCL Explained When trading crude oil futures, the two most popular contracts offered by the CME Group are WTI Crude Oil Futures (CL) and Micro WTI Crude Oil Futures (MCL). Both contracts offer traders a way to speculate or hedge on the price movements of crude oil, but they differ in size, margin requirements, and ideal use cases. WTI Crude Oil Futures (CL): Price Fluctuations: The contract moves in increments of $0.01 per barrel, meaning a $10 change for one contract. Margin Requirements: As of recent estimates, the margin requirement for trading a CL contract is around $6,000, though this can fluctuate depending on market volatility. Micro WTI Crude Oil Futures (MCL): Price Fluctuations: 10 times less. The contract moves in increments of $0.01 per barrel, meaning a $1 change for one contract. Margin Requirements: 10 times less, around $600 per contract. Practical Application: During periods of heightened market volatility—such as the lead-up to and aftermath of a U.S. presidential election—traders can use both CL and MCL contracts to navigate expected price fluctuations. Larger traders might use CL to hedge against or capitalize on significant price movements, while retail traders may prefer MCL for smaller, controlled exposure. 6. Conclusion As the 2024 U.S. presidential election approaches, crude oil traders are watching closely for market signals. While political outcomes can cause short-term volatility, the machine learning model’s predictions emphasize that broader economic factors will drive crude oil prices more significantly over the medium and long term. Whether a Democrat or Republican wins, crude oil prices are expected to see a potential increase, particularly one year after the election. This surge, driven by factors such as interest rates, labor market health, and inflation, suggests that traders should focus on these economic indicators rather than placing too much weight on which party claims the presidency. 7. Risk Management Reminder Navigating market volatility, especially during a presidential election period, requires careful risk management. Crude oil traders, whether trading standard WTI Crude Oil futures (CL) or Micro WTI Crude Oil futures (MCL), should be mindful of the following strategies to mitigate potential risks: Use of Stop-Loss Orders: Setting predefined exit points, traders can avoid significant drawdowns if the market moves against their position. Leverage and Margin Control: Overexposure can lead to margin calls and forced liquidation of positions in volatile markets. Position Sizing: Adjusting position sizes according to risk tolerance is vital especially during uncertain periods like elections. Hedging Strategies: Traders might consider hedging their crude oil positions with other instruments, such as options or spreads, to protect against unexpected market moves. Monitoring Economic Indicators: Keeping a close watch on key U.S. economic data can provide valuable clues to future crude oil futures price movements. By using these risk management tools effectively, traders can better navigate the expected volatility surrounding the 2024 U.S. election and protect themselves from significant market swings. When charting futures, the data provided could be delayed. Traders working with the ticker symbols discussed in this idea may prefer to use CME Group real-time data plan on TradingView: www.tradingview.com - This consideration is particularly important for shorter-term traders, whereas it may be less critical for those focused on longer-term trading strategies. General Disclaimer: The trade ideas presented herein are solely for illustrative purposes forming a part of a case study intended to demonstrate key principles in risk management within the context of the specific market scenarios discussed. These ideas are not to be interpreted as investment recommendations or financial advice. They do not endorse or promote any specific trading strategies, financial products, or services. The information provided is based on data believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Trading in financial markets involves risks, including the potential loss of principal. Each individual should conduct their own research and consult with professional financial advisors before making any investment decisions. The author or publisher of this content bears no responsibility for any actions taken based on the information provided or for any resultant financial or other losses.
NYMEX:CL1!Education
by traddictiv
5
HRTX a biotech penny stock with 70% in two months LONGHRTX has been suggested by various trading websites as a potentially explosive penny biotech stock for 2024. It has experienced excellent price actions since an earnings beat in November. It beat the estimates; that is to say it burned about half as much cash as the analysts estimated the it would. Today it pumped 11%. Relevant articles can be found compiled on the Yahoo Finance page linked here. The chart is 120 minutes. A alpha trend indicator is shown and the supertrend since the November earnings is upward. An AI Lorentzian indicator is added with a 2000 candle lookback to generate buy and sell signals. It calculated a 59% win on 83 trades over those 2000 candles two hours each; this amounts to about 2000 x 2 / 6 hrs per session or more than 600 trading days = 2 1/2 years. Also supporting an entry at this time is the faster (45 min) RSI line rising over the 50 level while the slower ( 240 minutes in red) RSI line has been over the 50 level since those earnings. The ADX indicator had a DI- and DI + flip with a mini pullback to close out last week's trading ( profit taking). The volatility indicator also showed that dip with selling volatility greater than buying which has now flipped. Given that price has went 5X in 2 months , there is a possibility HRTX is overbought. Analysts seem to think otherwise. I will take a stock position here and anticipate holding the position into the next earnings. For trade management I will take partials of 5 % each at the high of day for ten days going into earnings and hold the remaining 50% through the earnings. In the meanwhile I will review the trade if the machine-learning alo indicator generates either a buy or sell signal. For those lacking the risk tolerance for money-losing biotechnology penny stocks with high potential but are aware that biotechnology is expected to be "outperforming" in 2024, XBI and LABU are ETFs with risk-mitigation in the form of a diverse portfolio from the sector.
NASDAQ:HRTXLong
by AwesomeAvani
UROY Short Sell Trade from High Tight Flag Breakdown SHORTUROY topped out as shown by my other ideas. Profits are redeployed into it in a short trade to play the volatility. Expect 10% in 1-3 days. Text box comments are on the chart. The volatility is increased;the uranium sector is hot ( no pun here) given the climate warming and t the ongoing debates on fossil fuels and government initiatives supporting green energy and trying to wean the oil addiction. ( ZOOM out and to the left for text comments )
NASDAQ:UROYShort
by AwesomeAvani
AI's Insight from News Cross-Checked with Pattern Recognition 👁Dear Investors, I believe that PLTR might fall to $13.2 in the coming months. Here, I made a short idea from the insights of the different AI algorithms I use for speculative analytics. News Analytics - Natural Language Processing 1 Palantir's revenue growth has slowed in recent quarters. The company's revenue grew by 31% year-over-year in the first quarter of 2023, but this was down from 54% growth in the fourth quarter of 2022. This slowdown in revenue growth could be a sign that Palantir is facing challenges in the market. 2 Palantir's gross margin has been declining. The company's gross margin was 74% in the first quarter of 2023, down from 77% in the fourth quarter of 2022. This decline in gross margin could be a sign that Palantir is having to invest more in sales and marketing to drive revenue growth. 3 Palantir has been losing market share. The company's market share in the data analytics market is estimated to be around 1%, according to Gartner. This is a very small market share, and it has been shrinking in recent years. This could be a sign that Palantir is not as competitive as its rivals. 4 Palantir's stock price has been volatile in recent months. The stock price has fallen by more than 50% from its all-time high in August 2021. This volatility could be a sign that investors are uncertain about Palantir's future. Cross-Checking Logic Of course, there are also some positive news about Palantir that could suggest that the stock price will not fall to $13.2. For example, the company has a strong pipeline of new business opportunities. Palantir is also investing heavily in research and development, which could lead to new products and services that could boost the company's growth. Chart Pattern Recognition - Deep Neural Networks Between the two red trendlines, my neural networks believe to be a bearish channel. Your human eyes can see how Palantir rejected the upper trendline on 11 October and 21 November. I marked these price points with red ellipses. The channel had some bullish aspects when the bottom trendline acted as a support on 02 November and possibly today. Look at the left green arrow. Palantir's last rally related to this point. Today, the stock is near the same trendline again, and there's a chance that it can reignite a similar rally. The white arrow shows this possible scenario. I, however, feel skeptical that history would repeat itself. Ensembling Technical Indicators I asked different AIs to weigh technical indicators to represent their opinions. I ensembled the results of these AI opinions and selected MACD, RSI, and volume to simulate AI's insights in a way you can reproduce on your chart without AI. From declining volume bars I suspect the continuation of the bearish trend. The price action has been bearish over the last week, and I can't see the volume to reverse it. I can see extreme sell volumes every now and then, but they seemed to escalate the bearish trend. I don't see where the orders are that could absorb the end of the bearish trend. RSI tried to make a bullish cross below the volume indicator, but it happened to be a failed cross. RSI reversed as it crossed the SMA, which suggests a lack of bullish momentum. The potential bullish signal turned out to be an indication of how weak bulls are. At the same time, MACD has been going on the bearish side with a strong momentum, and periodically pulsing bearish momentum without signs of weakening. Overall, these indicators simulate what my AI bots believe about the market. Their ensembled opinion seems to be a bearish continuation. Chart Explanation I already explained the red bearish channel, the channel contacts, the indicators, and a potential bullish scenario, but I think bears enjoy a better risk-reward ratio. Theoretically, channel breakdown could pull the price into the support level of 13.2. I've got a green line at this level. Thus, the target price of a short could be within the green box around this level where the bearish trajectory's red arrow shows. The stock might reverse or not at this level. I'll have to reassess if I see the playout of my bearish expectation. Conclusion Ultimately, the direction of Palantir's stock price will depend on a variety of factors, including the company's financial performance, the overall market conditions, and investor sentiment. It is always important to do your own research and consult with a financial advisor before making any trading decisions. Kind regards, Ely
PShort
by Elysian_Mind
9
Bitcoin Futures: A Quantitative Approach to Analyzing BTCIntroduction to Bitcoin Futures Bitcoin, the pioneering digital asset, has carved a niche in the financial markets with its futures contracts. Bitcoin Futures provide traders and investors a regulated avenue to speculate on the price of Bitcoin without holding the actual cryptocurrency. This article delves into a quantitative analysis to analyze the next week's potential value of Bitcoin Futures, employing a sophisticated Neural Network model. Current Market Landscape The Bitcoin market is known for its rapid price movements. Recently, regulatory news, technological advancements, and shifts in investor sentiment have contributed to market fluctuations. Understanding these trends is crucial for traders looking to navigate this dynamic landscape. Quantitative Analysis of BTC Futures' Potential Price Movements Neural Networks & Machine Learning: At the heart of our quantitative approach is a Neural Network model. This model has been trained on historical weekly data of Bitcoin Futures, including key price points and other relevant market indicators. Data Preprocessing: To ensure accuracy, the data underwent rigorous preprocessing, including normalization to make it suitable for the Neural Network. This step is essential in highlighting the true patterns and trends in the data without noise or scale issues distorting the model's view. Model Training: Our model was trained over 500 iterations, adjusting its internal parameters to minimize prediction error. This training process involved feeding the model historical data and letting it learn from the actual price movements. Evaluation and Prediction: After training, the model's performance was evaluated. The actual prices were compared against the model's predictions to assess robustness. This evaluation is crucial in understanding the model's reliability. Impact of External Factors Bitcoin Futures are affected by a range of external factors, including regulatory changes, market sentiment, and technological developments. These factors can cause sudden and unpredictable market movements, making the analysis of future potential prices challenging. Our model takes into account the historical impact of these factors, but it's important to remember that unforeseen future events can lead to deviations from predicted values. Forward-Looking Market Views Based on our Neural Network's learning and the recent market data, the model predicts that the value of Bitcoin Futures for the next week will be around "$44,026.60". This prediction is visualized in our graph comparing actual prices against predicted values over time, providing a clear view of the model's accuracy. Given the fact that the current value of BTC is slightly under 43,000, a trader could plan a long trade targeting 44,026.60 as their exit price. Entries could be taken in many ways such as utilizing key technical supports or waiting for breakouts above key resistance price levels. In all cases, a professional approach to taking risk in the marketplace always require managing such risk using stop-loss orders and making sure the trade size has been pre-calculated. There are many more options on how to have a contingency plan in place in case BTC moved in the opposite direction our AI expected it to. More on this in future articles. The model's learning curve, depicted in the accuracy graph, shows how the prediction accuracy improved over training iterations, reflecting the model's increasing proficiency at understanding the market. Conclusion Our quantitative analysis, utilizing a sophisticated Neural Network model, provides a prediction for the next week's value of Bitcoin Futures. While this prediction is grounded in historical data and advanced algorithms, it's important for traders to consider the inherent volatility and unpredictability of the Bitcoin market. The predictive model is a powerful tool, but it should be used as part of a broader strategy that considers market news, economic reports, and other indicators. When charting futures, the data provided could be delayed. Traders working with the ticker symbols discussed in this idea may prefer to use CME Group real-time data plan on TradingView: www.tradingview.com This consideration is particularly important for shorter-term traders, whereas it may be less critical for those focused on longer-term trading strategies. General Disclaimer: The trade ideas presented herein are solely for illustrative purposes, forming a part of a case study intended to demonstrate key principles in risk management within the context of the specific market scenarios discussed. These ideas are not to be interpreted as investment recommendations or financial advice. They do not endorse or promote any specific trading strategies, financial products, or services. The information provided is based on data believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Trading in financial markets involves risks, including the potential loss of principal. Each individual should conduct their own research and consult with professional financial advisors before making any investment decisions. The author or publisher of this content bears no responsibility for any actions taken based on the information provided or for any resultant financial or other losses.
CME:BTC1!
by traddictiv
2
Bitcoin Cycles: Machine learning (kNN) Sentiment Anomaly InsightMarket Cycle Analysis with the the Machine Learning: kNN sentiment Anomaly detector indicator When analyzing the Bitcoin market on a daily time frame (1D), certain recurring patterns stand out. Using the Machine Learning: kNN sentiment Anomaly indicator, we observe several phases that seem to repeat at regular intervals: 1. End of Bull Market: Following a price expansion phase, the indicator shows a significant drop, accompanied by a prolonged green background color. 2. Indicator lateralization Phase (Bear Market): In this phase, while the asset’s price shows a downtrend, the “Amplified K Distance” line remains relatively stable, hovering around the value of 0, indicating a lateral movement in the indicator. 3. First Expansion: A new price expansion phase emerges. The indicator then shows a spike upwards, signified by a red background color. This rise is followed by a moderate correction (Excluding the COVID black swan event), which could foreshadow the beginning of a market bubble. 4. Bull Market Phase: During this upward trend period, the “Amplified K Distance” line shows a gradual downward trend, culminating in a pronounced oversold peak, signaled by a green background color. This stage suggests a saturation of the bull market and potentially heralds the transition to a new cycle, as previously described. Back to step 1. So far, this pattern seems to have occurred twice on Bitcoin, between 2014-2018 and 2018-2021. The current signs, from 2021 to 2023, suggest that we have already passed through the first three stages and are currently in a correction phase after the first expansion. The future market trend remains uncertain, and while this indicator has shown precursor signs in the past, it is essential to approach any prediction with caution. The efficacy of this indicator in predicting future market cycles remains to be validated over time.
BITSTAMP:BTCUSDLong
by Ox_kali
2
#NMR/USDT 4h (Binance Futures) Descending wedge breakoutNumeraire regained both 50MA & 200MA and is pulling back to them, looks good for another bounce. ⚡️⚡️ #NMR/USDT ⚡️⚡️ Exchanges: Binance Futures Signal Type: Regular (Long) Leverage: Isolated (4.0X) Amount: 6.9% Current Price: 13.13 Entry Targets: 1) 13.10 Take-Profit Targets: 1) 15.94 Stop Targets: 1) 11.68 Published By: @Zblaba $CRYPTOCAP:NMR BINANCE:NMRUSDT.P #Numeraire #AI numer.ai Risk/Reward= 1:2.0 Expected Profit= +86.7% Possible Loss= -43.4%
Long
by TradingOn
2
ETH Bullish From Here? Looking at our TA on ETH combined with our new AI/Machine learning indicators (releasing soon), we can see ETH is setting up for another bullish run. Our indicators have called the tops and bottoms pretty accurately, now it looks like we may have short-term upside. The big levels to watch out for are 1815 (support) and 1850 (resistance). If Ethereum cracks 1815 then that would invalidate our bullish thesis and set for a potential fall to 1780 then further of 1650. Although if we break 1850, then that sets clear skies to our next target of 1900, followed by 2000. As we're in this current chop, we always advise everyone to never force a trade and wait for a trade to come to you. This means waiting until multiple indications signal a buy (oversold at support, etc) or vice versa. Trading is all about discipline and those who don't have it will quickly be humbled. If you enjoyed our TA or have any questions about our upcoming indicator release, please comment below or send us a DM :)
Long
by CryptoScripts
HYMC- Gold is Glittering Penny Junior Miner LONGGold is on a bullrun and what better equity to look to capture profits than gold junior miner penny stocks. The risk is high and the potential profit is well - perhaps on the way to the moon. Shown here is HYMC on 2 hr chart. The momentum is obvious with the climb of the green HA candles. The MTF RSI shows the low TF RSI shot up over 80 while the higher TF RSI is rising at a more gradual slope. No signs of bearish divergence are seen. The MACD shows the lines well above the positive histogram in fact three times the amplitude. Again, no signs of bearish divergence. The AI Lorentzian indicator shows a buy signal. Its algo tested a projected win rate of 73% on the signals which should buttress my confidence in this trade. I will follow the signals in the context of my targets based on TA and see how they correlate. Gold is on fire ! Junior miners are perhaps in the midst of a wildfire. I will take a long trade on this penny stock. My targets are $0.56 and $.71 representing swing high pivots earlier this year. Overall, I am expectant of 50% profit and a risk free trade once the price rises 10% and a 5% stop loss is moved to break-even.
NASDAQ:HYMCLong
by AwesomeAvani
Will SPY continue to rise? LONGOn the 30- minute chart, SPY is in an uptrend continuing from the end of the last trading week. I see this as continuing for the following reasons on analysis: 1. The Lorentzian AI machine learning indicator's last signal was a buy signal. Given its specific accuracy of 73% as the table reports, I suspect the uptrend will continue until a sell signal prints. 2. The VWAP anchored to July 6th shows price riding the upper VWAP bands suggesting that buying pressure exceeds selling pressure over the past week. 3. The MTF RSIs are steadily rising with the lower TF above the higher TF and no evidence of weakening or bearish divergence. 4. The zero-lag MACD shows lines crossed and are now parallel and about to cross over the horizontal zero line. 5. In the last trading day, the price ran up then momentum stalled for profit-taking and consolidation to rest for the next. 6. Trading volumes have been at or above the running mean throughout the recent past showing higher than usual trader interest which bodes well for volatility to be played for profit. 7. If I were looking for chart patterns, I would say that SPY is currently a high tight flag. It is expectant of bullish continuation Overall, I have further interest in trading call options with a low time interval until expiration. I will use intraday pivots on low time frames to select entries and pick strikes based on expected moves in analysing VWAP bands or Bollinger Bands.
AMEX:SPYLong
by AwesomeAvani
BYND- Is there more meat on the bone?BYND has had a good trend up over about 15 days rising about 40% over the interval. The question that arises is whether the trend is now near to a top and so consolidation or reverse or instead can it continue higher? The indicators may give a hint on the 4 hr chart which being a higher time frame has better reliability than a low TF. About a week ago price crossed over the mean VWAP anchored to the beginning of the year. This demonstrates bullish momentum and concurs with the other indicators. Professional traders see the VWAP as an " over/under" of sorts something well known to sports betters. The Lorentzian Indicator which uses machine learning an many parameters including moving averages, average directional index, RSI and CCI printed a buy signal on June 23rd and has not yet printed a corresponding sell signal. The MTF RSI by Chris Moody shows both TFs with RSIs in the 65-70 range showing BYND not to be overbought and overvalued. The MACD indicator shows the K and D lines in parallel well above the positive histogram. There is no the suggestion of an impending line cross. Fundamentally, BYND products have not inflated to the extent of beef, port and chicken. Overall, I see an opportunity for a long trade. I will drill down to the 1 to 5 minute time frames and look for a pivot low. The target is about 50% upside at $22.5 the pivot high of this year in March. I will take partia profits along the way while raising the stop loss in lockstep with those profits as an effective risk management exercise.
NASDAQ:BYNDLong
by AwesomeAvani
VALE - a senior gold miner LONGVALE is a long-established gold miner with global assets and is well positioned to leverage rises in spot gold and futures in its overall operations. On the 1H chart, I have added two sets of VWAP lines and their standard deviations anchored at the beginning of both May and June. They have similar line trends. Price is crossing over those mean VWAPs a classical sign of predominating bullish momentum The Lorentzian machine learning indicator issued a buy signal two trading days ago. This indicator uses RSI, CCI, moving averages and directional indices to produce high-quality algos and trading signals. It has no psychology and issues alerts on purely mathematical criteria. The MTF RSI indicator shows the lower TF line crossed over both the higher TF line in black and the 50 level one day ago. The 3 in 1 indicator is green for all three including money flow and bullish momentum and confirmatory on RSI. Overall, given the rise in gold on the forex and futures market, I see VALE moving higher and the TA supports that view. I will take a long trade of stock and consider a call option if I can find one with enough volume to trade and a reasonable spread.
NYSE:VALELong
by AwesomeAvani