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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.
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NYMEX:CL1!Education
by traddictiv
5
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BITSTAMP:BTCUSDEducation
by JS_TechTrading
Options flow predicting moves on Derivatives (Futures)Options have been and are an important instrument on the financial market for a trader trading Intraday Futures. Therefore, while exploring the mechanics of the option market over the last several months, as a result of work, indicators were created that load data from Quandl and then look for patterns that may herald a change of direction on the derivative market - in this case Futures Contracts. There are two main types of Options: CALL - allow their owner to buy a given product in the future at a predetermined price (Strike Price) PUT - allow you to sell this product at a predetermined price (Strike Price) By observing the market volumes of both types of Options, we can observe the sentiment of investors. The key factors are which volume (call or put) prevail in the volume and the dynamics of the volume - what is the trend on volume, whether the difference between them increases or decreases. In addition, the Put / Call Ratio analysis allows you to confirm or negate the signals from the Option volume. The Ratio indicator behaves inversely to the price movement - in the case of a bearish sentiment, we expect the ratio to increase, and in the case of bullish sentiment - the indicator should decrease. If the Ratio follows the price in the same direction, it is an anomaly. Of course, the mere observation of the Option volumes and the Put / Call ratio is not sufficient, as the Options Market is a much more complicated activity. It is worth including in the calculations such factors as Expiration Date, Bonus Amount, option type (In the Money, Out of Money or At the Money). Not each of the factors is equally important, therefore the key is additionally the appropriate selection of the weighting factors. For this purpose, due to the multitude of data, it is worth using Machine Learning, which I also do by saving the resulting data in a dataset in Quandl and displaying the data in TradingView using Pine Script. Below are some additional examples from recent sessions on ES showing the predictive nature of the Option sentiment, often preceding major movements in the ES index (during the spot session): First, from the left, the session from November 15 is shown and an opportunity to play Short. On the right, the session from November 16 and an opportunity to play the Long position this time. Session from November 10, where we first got the Bull's signal, and at the top we got a warning signal of traffic reversal and the possibility of entering Short: And one of my favorite moves on November 3:
CME_MINI:ES1!Education
by skyline-trader
Indicators Input Window Length - Problems And SolutionsIntroduction Most technical indicators possesses a user defined input window length, this input affect the indicator output and for a long time, have been the cause of many innovations in technical analysis. In this post i want to discuss the effects and particularities of indicators inputs window length, the challenges they introduce in trading and their effect when paired with machine learning forecasting models, i hope this post will be easy to read, let me know if you had difficulties understanding it. Speed And Efficiency Problems An input window length can involve the number of data processed by the indicator, therefore higher window length's will process more data, which result in a slower computation time, therefore in high-frequency/algorithmic trading where response time matter, maximizing the profitability might be made at the cost of the indicator response time, and even if computerized trading has been praised for its speed, small lag times can actually affect your strategy, therefore one might enter a trade at a different value than the targeted price. Note : High frequency trading (hft) is a commonly mistaken term, one might believe that hft require the trader to open and close a large number of trades in a short amount of time, in reality hft is related to the "rate at which data is processed". Solution - Efficient Data Processing Solutions have been proposed in order to make certain tools more efficient. For example the simple moving average is a common tool that is the basis of many other indicators, its calculation involve summing the length last data points and diving this sum by length . In signal processing, such tool require what is called "memory", the data points must be stored in order for them to be processed, this is extremely inefficient and slow, therefore alternatives have been proposed, one of them is still mainly used in technical analysis today and is called the exponential moving average (ema), the process of computing an exponential moving average is called exponential averaging, and has the form of : ema = sc*input+(1-sc)*(past ema value) where sc is called the smoothing constant where 1 > sc > 0 . We only need 2 data values in order to perform this computation, lets denote a moving average of period length sma(input,length) , we can estimate it using exponential averaging with sc = 2/(length+1) . The computation time of the exponential moving average is way lower than the one of the sma . This is the most elegant and efficient estimation of the simple moving average. The exponential moving average is the simplest "IIR Filter", or infinite response filter, those filters are as well extremely efficient since they use recursion. Exponential averaging is also the core of many adaptive indicators. In my experience, recursion will always let you create extremely efficient tools. Window Length And Optimization Problems Optimization is a branch of mathematics that help us find the best parameters in order to maximize/minimize a certain function, and thanks to computers this process can be made faster. Optimizing technical indicators during backtesting involve finding the input window length (set of inputs if there are more than 1 input) that maximize the profit of a strategy. The most common approach is brute forcing, in which we test every indicator inputs window length combination and keep the one that yield the best results. However optimization is still computationally intensive, having 2 indicators already involve a high number of combinations. This is why it is important to select a low number of indicators for your strategy. But then other problems arise, the best input window length (set of inputs) might change in the future. This is due to the fact the market price is non-stationary and one of the reasons technical indicators are looked down. In order to deal with this problem, we can propose the following solutions : Use indicators/Information with no input window length -> Vwap/Volume/True Range/Cumulative Mean...etc. Study the relationship between the optimal input window length and price evolution -> Regression analysis Forecast the optimal input window length -> Forecasting The last two are extremely inefficient, kinda nightmarish, and would be time consuming if one use a serious backtesting procedure. However the first solution is still appealing and might actually provide a efficient result. Machine Learning Forecasting - Performance And Technical indicators Input Window Length Dependency Technical indicators outputs can be used as inputs for machine learning algorithms. We could think that we also need to optimize the input window length of the indicators when using machine learning (which would lead to high computations time, machine learning already involve optimization of a high number of parameters), however a research paper named "Forecasting price movements using technical indicators: Investigating the impact of varying input window length" by Yauheniya Shynkevicha, T.M. McGinnitya, Sonya A. Colemana, Ammar Belatrechec and Yuhua Li highlight an interesting phenomenon, the abstract tell us that : "The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon" In short, if you want to forecast market price 14 step ahead with a machine learning model, you should use indicators with input window length approximately equal to 14 as inputs for the model in order to get the best performance. This would allow to skip a lot of optimizations processes regarding the technical indicators used in the model. They used 3 different type of ML algorithms, support vector machine (svm) , adversarial neural networks (ann) and k nearest neighboring (knn) , which reinforce their conclusion. In the paper, we can see something interesting with the indicators they selected as inputs, they used : A simple moving average, an exponential moving average, the average true range, the Average Directional Movement Index, CCI, ROC, RSI, %R, stochastic oscillator. First thing we can see is that they used the exponential moving average instead of the wilder moving average for certain calculations, which i think is a good choice. We can also see they used many indicators outputting the same kind of information, in this case we often talk about "Multicollinearity", for example : The CCI, ROC, RSI, %R, Stochastic output similar information, all remove the trend in the price, the CCI and ROC are both centered around 0 and the %R, RSI and stochastic oscillator around 50. The SMA and EMA also output similar information. In technical analysis this practice is often discouraged since the indicators will output the same kind of information, this lead to redundancy. However such practice has been seen a lot in machine learning models using technical indicators. Maybe that a higher amount of multicollinearity between indicators allow to strengthen the relationship between the forecast horizon and the indicators input window length. Conclusion We talked (a lot) about indicator inputs window length, what problems they cause us and how we can find solutions to those problems. Also we have seen that the forecasting performance of ML models can be higher when they use indicators outputs with input window length equal to the forecasting horizon. This can make to make the process of forecasting financial market price with ML models using technical indicators more efficient. ML is a recurring subject in financial forecasting, those algorithms offer the hope to make technical indicators more useful, and indeed, technical indicators and ML models can benefits from each others, however it is sad to observe that classical indicators are mainly used instead of newer ones, but its also encouraging in the sense that more research can be done, using newer material/procedures. Thanks for reading !
NASDAQ:INTCEducation
by alexgrover
12
High Probability Intraday Trade Setup for Gold FuturesThe following are trades setup ideas in 15 mins chart for Gold Futures. There are 2 distinctive dotted lines labelled as 1. AI Day Resistance 2. AI Day Support These 2 signals are generated by machine learning AI robots as a high probability trade setup where to long or short. If price action was above the AI Day resistance line AND price closed above Pivot Point R1 line, the ideas is to long and take profit at Pivot R2 line OR If price action was below the AI Day support line AND price closed below Pivot Point S1 line, the ideas is to short and take profit at Pivot S2 line Instead of relying on 100% discretionary (human) trading, the robots will provide trade execution plan and it is entirely up to the human trader's decision to follow.
COMEX:GC1!Education
by WolfeDayRocker