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.
Machinelearning
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.
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.
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.
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 )
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
Latest XRP Gemini Pro Vision PredictionSo I'm basically posting this just as a way of recording the predictions I've received from the Gemini Pro Vision tests I've started messing around with. So far, as you can see by the chart, it's been pretty good and predicting future price directions within a 10-25 pip range.
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.
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.
#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%
MTL Loves a good wick Part IIQuick update to prior idea: Zoomed out - every green (buy) triangle on this 'machine learning' indicator, save for the very first one, results in a wick to the orange trend lines.
Fascinating.. lets see if this happens.
Prior idea:
Link to indicator.
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 :)
NVDA- the frontrunner in the new AI revolutiongives some room to its cousin UPST. They took off from the same runway and the blue sky
awaits them. There are others in the same squadron. The flight show will be unprecedented.
Buckle yourself in. Make sure you can find your oxygen mask because there will be some
high-altitude flying. Enthusiastic traders will provide the fuel. Itwill be awesome for sure.
- Avani
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.
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.
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.
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.
Bitcoin Forecast Cloudy☁️ Clear 0 %2:00 Cloudy☁️ Clear 0 %
The Bitcoin market is facing dark clouds and gloomy skies, indicating a challenging and difficult trading environment that poses significant obstacles for investors.
We Called The ETH/BTC Pump!Looking at our chart, we see that Ethereum started getting oversold near the bottom of our standard deviation bands, along with an oversold reading on our new DVO indicator. This, combined with the oversold green X's we received (combination of multiple indicators), and our dark blue candles (another oversold indication), led us to have a VERY successful ETH long!
The next resistance we're looking at is $1915. This has been a big level on the daily chart and may provide a rejection once reached. You can also see our candles starting to get overbought (turning orange). Once they turn red, that's when I start looking to completely exit or take a majority of my profits.
If you're looking to buy on a pullback, watch the $1715 level. That acted as great support over the past few weeks and could provide a good R/R if we reverse here.
With BlackRock recently applying for a Bitcoin ETF and many other banks following suit, the crypto market is about to get a whole lot crazier!
-Stayed tuned for our new indicators launching soon (shown on the chart + more), along with a slew of great trading info for you guys! You won't want to miss it :)
Let us know if you have any questions!
The environmental impact of AI: a case studyIn our previous blog, Will AI workloads consume all the world’s energy?, we looked at the relationship between increasing processing power and an increase in energy demand, and what this means for artificial intelligence (AI) from an environmental standpoint. In this latest blog, we aim to further illuminate this discussion with a case study of the world’s biggest large language model (LLM), BLOOM.
Case study on environmental impact: BLOOM
An accurate estimate of the environmental impact of an LLM being run is far from a simple exercise. One must understand, first, that there is a general ‘model life cycle.’ Broadly, the model life cycle could be thought of as three phases1:
Inference: This is the phase when a given model is said to be ‘up-and-running.’ If one is thinking of Google’s machine translation system, for example, inference is happening when the system is providing translations for users. The energy usage for any single request is small, but if the overall system is processing 100 billion words per day, the overall energy usage could still be quite large.
Training: This is the phase when the parameters of a model have been set and the system is exposed to data from which it is able to learn such that outputs in the inference phase are judged to be ‘accurate’. There are cases where the greenhouse gas emissions impact for training large, cutting-edge models can be comparable to the lifetime emissions of a car.
Model development: This is the phase when developers and researchers are seeking to build the model and will tend to experiment with all sorts of different options. It is easier to measure the impact of training a finished model that becomes public, as opposed to seeking to measure the impact of the research and development process, which might have included many different paths prior to getting to the finished model that the public actually sees.
Therefore, the BLOOM case study focuses on the impact from training the model.
BLOOM is trained on 1.6 terabytes of data in 46 natural languages and 13 programming languages.
Note, at the time of the study, Nvidia did not disclose the carbon intensity of this specific chip, so the researchers needed to compile data from a close approximate equivalent setup. It’s an important detail to keep in mind, in that an accurate depiction of the carbon impact of training a single model requires a lot of information and, if certain data along the way is not disclosed, there must be more and more estimates and approximations (which will impact the final data).
If AI workloads are always increasing, does that mean carbon emissions are also always increasing2?
Considering all data centres, data transmission networks, and connected devices, it is estimated that there were about 700 million tonnes of carbon dioxide equivalent in 2020, roughly 1.4% of global emissions. About two-thirds of the emissions came from operational energy use. Even if 1.4% is not yet a significant number relative to the world’s total, growth in this area can be fast.
Currently, it is not possible to know exactly how much of this 700 million tonne total comes directly from AI and machine learning. One possible assumption to make, to come to a figure, is that AI and machine learning workloads were occurring almost entirely in hyperscale data centres. These specific data centres contributed roughly 0.1% to 0.2% of greenhouse gas emissions.
Some of the world’s largest firms directly disclose certain statistics to show that they are environmentally conscious. Meta Platforms represents a case in point. If we consider its specific activities:
Overall data centre energy use was increasing 40% per year from 2016.
Overall training activity in machine learning was growing roughly 150% per year.
Overall inference activity was growing 105% per year.
But Meta Platforms’ overall greenhouse gas emissions footprint was down 90% from 2016 due to its renewable energy purchases.
The bottom line is, if companies just increased their compute usage to develop, train and run models—increasing these activities all the time—then it would make sense to surmise that their greenhouse gas emissions would always be rising. However, the world’s biggest companies want to be seen as ‘environmentally conscious’, and they frequently buy renewable energy and even carbon credits. This makes the total picture less clear; whilst there is more AI and it may be more energy intensive in certain respects, if more and more of the energy is coming from renewable sources, then the environmental impact may not increase at anywhere near the same rate.
Conclusion—a fruitful area for ongoing analysis
One of the interesting areas for future analysis will be to gauge the impact of internet search with generative AI versus the current, more standard search process. There are estimates that the carbon footprint of generative AI search could be four or five times higher, but looking solely at this one datapoint could be misleading. For instance, if generative AI search actually saves time or reduces the overall number of searches, in the long run, more efficient generative AI search may help the picture more than it hurts3.
Just as we are currently learning how and where generative AI will help businesses, we are constantly learning more about the environmental impacts.
Sources
1 Source: Kaack et al. “Aligning artificial intelligence with climate change mitigation.” Nature Climate Change. Volume 12, June 2022.
2 Source: Kaack et al., June 2022.
3 Source: Saenko, Kate. “Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins.” The Conversation. 23 May 2023.
Cloudy☁️ (Confidence: 0.26 )🌤️ Welcome to the Bitcoin weather forecast! 🌤️
Unfortunately, I have some cloudy news for Bitcoin investors. ☁️ Looking at the chart index for the past hour, the confidence level that the weather in the Bitcoin world will be sunny is only 0.26, which is significantly less than the baseline of 0.864.
Let's take a closer look at the data. The opening price was 29187, and the high was 29264, while the low was 29174. The closing price was 29214. This indicates that there has been some volatility in the market, but overall the price has remained relatively stable.
In terms of technical indicators, the exponential moving averages (ema) have been trending upwards, with ema9 at 29078, ema21 at 28988, ema50 at 28855, ema100 at 28786, and ema200 at 28796. The relative strength index (rsi) is at 61, which suggests that Bitcoin is neither overbought nor oversold.
However, the fast and slow stochastic oscillators (fast_k at 62, slow_k at 59, and slow_d at 51) indicate that there may be some bearish pressure on the price. Additionally, the Moving Average Convergence Divergence (macd) is negative at -83, which also indicates a bearish trend.
Overall, the Bitcoin weather forecast is looking cloudy, and investors may want to exercise caution in the short term. Keep an eye on the technical indicators and be prepared for potential volatility in the market. ☁️💰💻
Sunny🌞 (Confidence: 1.0 )🌞 Good news for bitcoin investors! 🚀 Based on the chart, the bitcoin weather seems sunny ☀️ with a high confidence level of 1.0. The opening price of 28498 has been followed by an even higher closing price of 28505, with a high of 28528 and a low of 28433.
📈 The exponential moving averages (EMA) show an upward trend with the EMA9 at 28536 and the EMA21 at 28447, while the EMA50 and EMA100 are also on an upward trajectory at 28471 and 28617 respectively. The EMA200 is also showing bullish sentiment at 28668.
💹 The relative strength index (RSI) of 54 and fast_k at 61, indicate moderate bullish sentiment, with the slow_k and slow_d both in the bullish zone at 65 and 69 respectively. The moving average convergence divergence (MACD) also shows positive momentum, with a value of 256.
💰 With all these factors combined, the bitcoin market seems to be in a healthy position for investors, with the potential for further gains in the near future.
Sunny🌞 (Confidence: 1.0 )🌤️ Bitcoin Weather Forecast 🌤️
It's looking like sunny skies ahead for Bitcoin! ☀️
In the past hour, Bitcoin's price opened at 28158 and climbed as high as 28384, with a low of 28000. The closing price was 28350, above the ema9 of 28446, but below the ema21 of 28655. Despite this, the long-term trend is still looking good with the ema50 at 28908, ema100 at 28951, and ema200 at 28807.
Although the RSI is only at 36, indicating oversold conditions, the fast_k is at 50 and the slow_k is at 31, suggesting a potential bullish momentum. The slow_d is at 26, which may signal a continuation of the bullish trend.
Overall, with a high confidence level of 1.0, it's looking like a good time to invest in Bitcoin. However, it's always important to keep an eye on market trends and adjust your strategy accordingly. Happy trading! 💰💻📈
