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Bullish Outlook on XRPUSDKey Reasons for a Bullish Bias: 1. Positive Market Sentiment: XRP has recently broken through an important resistance level, which shows that traders are feeling optimistic about its future. 2. Bullish Technical Patterns: An inverted Head and Shoulders pattern has formed, suggesting that XRP might be ready for a price increase. 3. Improving Regulations: Recent developments in cryptocurrency regulations are becoming more favorable, which could attract more institutional investors to XRP. I plan to use probabilities based on historical data and the X1X2 methodology to enter long positions in XRP. Here’s why: - Learning from the Past: By looking at past price movements and historical data of XRP, I can spot biases that might help predict future behavior. - X1X2 Methodology: This method helps me identify key price levels to enter and exit trades, making my strategy more focused. - Smart Risk Management: By using probabilities, I can set stop-loss orders at strategic points, reducing my risk and making more informed decisions. In summary, with a positive market outlook and a solid trading strategy based on historical data and mathematical rules, I’m confident in taking long positions in XRPUSD. Traders, if you found this idea helpful or have your own thoughts on it, please share in the comments. I’d love to hear from you! 12M: 2W: 1H:
CRYPTO:XRPUSDLong
by Jasminex1x2
1
Probabilities Powering BTCUSD TradesUtilizing probabilities based on historical data is a cornerstone of my bullish strategy for BTCUSD. Here’s why I believe this approach is not only effective but essential for positioning long trades successfully. Understanding the Importance of Probabilities Probabilities in Trading Trading is inherently uncertain, and relying on probabilities allows traders to make informed decisions rather than guesses. By analyzing historical price movements and patterns, we can identify trends that have previously led to upward or downward movements. This statistical approach helps mitigate risks associated with emotional decision-making. Historical Data as a Guide Historical data provides a wealth of information about how BTCUSD has reacted under various market conditions. By employing a mechanical trading strategy that incorporates these indicators, I can increase my chances of entering profitable trades. Mechanical Trading Strategy What is a Mechanical Trading Strategy? A mechanical trading strategy is a systematic approach that uses predefined rules based on historical data to make trading decisions. This method eliminates emotional bias and ensures consistency in trade execution. Benefits of a Mechanical Approach 1. Consistency: Adhering to a mechanical strategy means that trades are executed based on data rather than emotions. 2. Backtesting: Historical data allows for backtesting strategies to see how they would have performed in the past, providing confidence in their potential effectiveness. 3. Risk Management: By employing probabilities, I can better manage risk through calculated position sizing and stop-loss orders. Current Market Context In the current market environment, BTCUSD shows signs of bullish momentum. The formation of higher lows indicates strength, and historical patterns suggest that we may be at the beginning of another significant upward trend. By leveraging probabilities derived from past performance, I am positioning myself to capitalize on this potential movement. Conclusion In summary, utilizing probabilities based on historical data through a mechanical trading strategy equips me with a robust framework for entering long positions in BTCUSD. This approach not only enhances my decision-making process but also aligns with my overall bullish bias. As we navigate the complexities of the crypto market, relying on data-driven strategies will be crucial for achieving success in our trades. 1D: 6H:
CRYPTO:BTCUSDLong
by Jasminex1x2
1
LTCUSD: Strong Bullish Momentum with 68.87% Probability for TP1!I’m optimistic about Litecoin (LTCUSD) right now, and here are some compelling reasons to consider this trade: - Market Recovery: The overall cryptocurrency market is bouncing back, with many coins, including Litecoin, showing positive price movements after recent dips. - Growing Adoption: More people and businesses are starting to use cryptocurrencies for transactions, which could increase demand for Litecoin. - Tech Improvements: Litecoin is undergoing updates that make it more efficient and user-friendly, attracting more interest. - Positive Sentiment: Many analysts are optimistic about the future of cryptocurrencies, suggesting that prices could continue to rise. To get positioned for long trades on LTCUSD, I rely on probabilities based on historical data in a mechanical trading system. In short, my bullish outlook on LTCUSD is supported by strong market fundamentals, and by using probabilities from historical data, I aim to position myself effectively for potential long trades. Please share your ideas and charts in the comments section below! 12M: 2W: 6H:
CRYPTO:LTCUSDLong
by Jasminex1x2
2
Why I’m Betting Bearish on GBPNZD: Key Market Drivers ExplainedAs I prepare to share my trade idea for GBPNZD, my overall bias is bearish. Here are some key fundamentals currently influencing this outlook: 1. UK Economic Slowdown: The UK is facing economic challenges, with high inflation and downgraded growth forecasts. This situation tends to weaken the British Pound against other currencies, including the New Zealand Dollar. 2. RBNZ's Hawkish Stance: The Reserve Bank of New Zealand (RBNZ) is likely to maintain a strong monetary policy, focusing on controlling inflation. This contrasts sharply with the UK's more cautious approach, which supports a stronger NZD. 3. Seasonal Trends: Historically, GBPNZD has shown a bearish trend from mid-August through December. This seasonal behavior suggests that now is an opportune time to consider short positions. In my trading strategy for GBPNZD, I rely on probabilities to guide my decisions for entering short positions. In summary, by leveraging probabilities based on historical data and current market fundamentals, I aim to position myself advantageously for short trades on GBPNZD. This disciplined approach aligns with my bearish outlook and enhances my trading effectiveness. I look forward to sharing my journey in this trade and welcome any thoughts or feedback! 2W: Hourly TF:
Short
by Jasminex1x2
The Formula That Helped Me Get Into in the Top 2% of TradersI spent years testing different strategies, obsessing over charts, and trying to find the perfect entry point. It took me a while to realize that it wasn’t just about picking the right trades—it was about knowing how much to risk on each trade. This is where the Kelly Criterion came into play and changed my entire approach. You’ve probably heard the saying, “Don’t put all your eggs in one basket.” Well, Kelly Criterion takes that idea and puts some hard math behind it to tell you exactly how much you should risk to maximize your long-term growth. It’s not a guessing game anymore—it’s math, and math doesn’t lie. What is Kelly Criterion? The Kelly Criterion is a formula that helps you figure out the optimal size of your trades based on your past win rate and the average size of your wins compared to your losses. It’s designed to find the perfect balance between being aggressive enough to grow your account but cautious enough to protect it from major drawdowns. F = W - (1 - W) / R F is the fraction of your account you should risk. W is your win rate (how often you win). R is your risk/reward ratio (the average win relative to the average loss). Let’s break it down. How It Works Let’s say you have a strategy that wins 60% of the time (W = 0.6), and your average win is 2x the size of your average loss (R = 2). Plugging those numbers into the formula, you’d get: F = 0.6 - (1 - 0.6) / 2 F = 0.6 - 0.4 / 2 F = 0.6 - 0.2 = 0.4 So, according to Kelly, you should risk 40% of your account on each trade. Now, 40% might seem like a lot, but this is just the theoretical maximum for optimal growth. The thing about using the full Kelly Criterion is that it’s aggressive. A 40% recommended risk allocation, for example, can be intense and lead to significant drawdowns, which is why many traders use half-Kelly, quarter-Kelly or other adjustments to manage risk. It’s a way to tone down the aggressiveness while still using the principle behind the formula. Personally, I don’t just take Kelly at face value—I factor in both the sample size (which affects the confidence level) and my max allowed drawdown when deciding how much risk to take per trade. If the law of large numbers tells us we need a good sample size to align results with expectations, then I want to make sure my risk management accounts for that. Let’s say, for instance, my confidence level is 95% (which is 0.95 in probability terms), and I don’t want to allow my account to draw down more than 10%. We can modify the Kelly Criterion like this: 𝑓 = ( ( 𝑊 − 𝐿 ) / 𝐵 )× confidence level × max allowed drawdown Where: 𝑊 = W is your win probability, 𝐿 = L is your loss probability, and 𝐵 = B is your risk-reward ratio. Let’s run this with actual numbers: Suppose your win probability is 60% (0.6), loss probability is 40% (0.4), and your risk-reward ratio is still 2:1. Using the same approach where the confidence level is 95% and the max allowed drawdown is 10%, the calculation would look like this: This gives us a risk percentage of 0.95% for each trade. So, according to this adjusted Kelly Criterion, based on a 60% win rate and your parameters, you should be risking just under 1% per trade. This shows how adding the confidence level and max drawdown into the mix helps control your risk in a more conservative and tailored way, making the formula much more usable for practical trading instead of over-leveraging. Why It’s Powerful Kelly Criterion gives you a clear, mathematically backed way to avoid overbetting on any single trade, which is a common mistake traders make—especially when they’re chasing losses or getting overconfident after a win streak. When I started applying this formula, I realized I had been risking too much on bad setups and too little on the good ones. I wasn’t optimizing my growth. Once I dialed in my risk based on the Kelly Criterion, I started seeing consistent growth that got me in the top 2% of traders on TradingView leap competition. Kelly in Action The first time I truly saw Kelly in action was during a winning streak. Before I understood this formula, I’d probably have gotten greedy and over-leveraged, risking blowing up my account. But with Kelly, I knew exactly how much to risk each time, so I could confidently scale up while still protecting my downside. Likewise, during losing streaks, Kelly kept me grounded. Instead of trying to "make it back" quickly by betting more, the formula told me to stay consistent and let the odds play out over time. This discipline was key in staying profitable and avoiding big emotional trades. Practical Use for Traders You don’t have to be a math genius to use the Kelly Criterion. It’s about taking control of your risk in a structured way, rather than letting emotions guide your decisions. Whether you’re new to trading or have been in the game for years, this formula can be a game-changer if applied correctly. Final Thoughts At the end of the day, trading isn’t just about making the right calls—it’s about managing your risks wisely. The Kelly Criterion gives you a clear path to do just that. By understanding how much to risk based on your win rate and risk/reward ratio, you’re not just gambling—you’re playing a game with a serious edge. So, whether you’re in a winning streak or facing some tough losses, keep your cool. Let the Kelly formula take care of your risk calculation. If you haven’t started using the Kelly Criterion yet, now’s the time to dive in. Calculate your win rate, figure out your risk/reward ratio, and start applying it. You’ll protect your account while setting yourself up for long-term profitability. Trust me, this is the kind of math that can change the game for you. Bonus: Custom Kelly Criterion Function in Pine Script If you’re ready to take your trading to the next level, here’s a little bonus for you! I’ve put together a custom Pine Script function that calculates the optimal risk percentage based on the Kelly Criterion. You can easily enter the variables to fit your trading strategy. // @description Calculates the optimal risk percentage using the Kelly Criterion. // @function kellyCriterion: Computes the risk per trade based on win rate, loss rate, average win/loss, confidence level, and maximum drawdown. // @param winRate (float) The probability of winning trades (0-1). // @param lossRate (float) The probability of losing trades (0-1). // @param avgWin (float) The average win size in risk units. // @param avgLoss (float) The average loss size in risk units. // @param confidenceLevel (float) Desired confidence level (0-1). // @param maxDrawdown (float) Maximum allowed drawdown (0-1). // @returns (float) The calculated risk percentage for each trade. kellyCriterion(winRate, lossRate, avgWin, avgLoss, confidenceLevel, maxDrawdown) => // Calculate Kelly Fraction: Theoretical fraction of the bankroll to risk kellyFraction = (winRate - lossRate) / (avgWin / avgLoss) // Adjust the risk based on confidence level and maximum drawdown adjustedRisk = (kellyFraction * confidenceLevel * maxDrawdown) // Return the adjusted risk percentage adjustedRisk Use this function to implement the Kelly Criterion directly into your trading setup. Adjust the inputs to see how your risk percentage changes based on your trading performance!
Editors' picks
COINBASE:BTCUSDEducation
by Fractalyst
11
How to dollar cost averge with precisionI've seen several dollar cost averaging calculator online, however there is something I usually see missing. How many stocks should you buy if you want your average cost to be a specific value. Usually the calculators will ask how much you bought at each level ang give you the average, but not the other way around (telling you how much to buy to make your average a specific value). For this, I decided to make the calculations on my own. Here, you can see the mathematical demonstration: www.mathcha.io
NASDAQ:TSLAEducation
by DarkMessiah777
$ETH and $BTC Price Level in USD to achieve $ETHBTC ATHI'm going to put this straight forward simple. BINANCE:ETHBTC , essentially representing the price ratio of Ethereum to Bitcoin, serves as a key indicator of market dynamics between these two leading cryptocurrencies. Due to the recent Break Of Structure on this Chart, I was curious enough, at what prices are we looking at in USD, in order for the ATH to break. Last ATH was on June 12th, 2017. Prices at that ATH were following: ETH: $414.8 BTC: $2980 According to my beloved friends ChatGPT, he could give me many scenarious, at which the ATH at 0.15636 would have be broken. Regarding of the multiplier, you get a different answer, here few very possible for me at this stage of market. Multiplier: 1.5 New Price of ETH: $3,766 New Price of BTC: $24,085 Multiplier: 1.7 New Price of ETH: $4,269 New Price of BTC: $27,302 Multiplier: 1.9 New Price of ETH: $4,771 New Price of BTC: $30,513 Multiplier: 2.0 New Price of ETH: $5,022 New Price of BTC: $32,118 Multiplier: 2.2 New Price of ETH: $5,524 New Price of BTC: $35,329 Multiplier: 2.4 New Price of ETH: $6,026 New Price of BTC: $38,539 This might be the biggest signal, showing Ethereum has a lot of potential in the upcoming Altcoin Season / Bullmarket. Not trying to convince anyone, just speculating on some interesting numbers. Feel free to come up with more different scenarious. 100k for BTC & 15k for ETH might also be possible :D
by luxorif
Scott Carney's "Deep Crab" & the Fields Medal in MathematicsQ: What does the former have to do with the later? A: The intuition in the former (S. Carney) is born out by the later (A. Avila; Fields Medal - 2014) From Scott Carney's website; --------------------------------------------------------------------------------------------------------- "Harmonic Trading: Volume One Page 136 The Deep Crab Pattern™, is a Harmonic pattern™ discovered by Scott Carney in 2001. The critical aspect of this pattern is the tight Potential Reversal Zone created by the 1.618 of the XA leg and an extreme (2.24, 2.618, 3.14, 3.618) projection of the BC leg but employs an 0.886 retracement at the B point unlike the regular version that utilizes a 0.382-0.618 at the mid-point. The pattern requires a very small stop loss and usually volatile price action in the Potential Reversal Zone." --------------------------------------------------------------------------------------------------------- From Artur Avila's Fields Medal Citation; --------------------------------------------------------------------------------------------------------- "Artur Avila is awarded a Fields Medal for his profound contributions to dynamical systems theory, which have changed the face of the field, using the powerful idea of renormalization as a unifying principle. Description in a few paragraphs: Avila leads and shapes the field of dynamical systems. With his collaborators, he has made essential progress in many areas, including real and complex one-dimensional dynamics, spectral theory of the one-frequency Schrödinger operator, flat billiards and partially hyperbolic dynamics. Avila’s work on real one-dimensional dynamics brought completion to the subject, with full understanding of the probabilistic point of view, accompanied by a complete renormalization theory. His work in complex dynamics led to a thorough understanding of the fractal geometry of Feigenbaum Julia sets. In the spectral theory of one-frequency difference Schrödinger operators, Avila came up with a global description of the phase transitions between discrete and absolutely continuous spectra, establishing surprising stratified analyticity of the Lyapunov exponent." --------------------------------------------------------------------------------------------------------- The connection here, as it is related to the specific "Deep Crab" harmonic pattern in trading, between intuition and general, analytical result, is illustrated somewhat simplified (but without distortion). In essence, Avila has shown that in dynamical systems, in the neighborhood of phase-transitions in the case of one-dimensional (such as: Price) unimodal distributions, after the onset of chaos, there are islands of stability surrounded nearly entirely by parameters that give rise to stochastic behavior where transitions are Cantor Maps - i.e., fractal. From that point it is an obvious next step to generalize to other self-affine fractal curves , such as the blancmange curve , which is a special case of w=1/2 of the general form: the Takagi–Landsberg curve. The "Hurst exponent"(H) = -log2(w) , which is the measure of the long-term-memory of a time series . Putting it all together, it is not pure coincidence that a reliable pattern (representation) emerges from intuition (observation) which proves to be a highly stable (reliable) pattern that is most often the hallmark of a near-term, violent transition.
CME_MINI:NQ1!Education
by Nemo_Confidat
BTC to 40k by 2024? Algo Alert's take from a qualitative POVIntroduction: The world of cryptocurrencies has always been accompanied by speculation and predictions about their future prices. One popular model that has gained attention is the LGS2F (Limited Growth Stock-to-Flow) model, which presents a modified version of the original stock-to-flow model for predicting Bitcoin prices. In this blog, we will delve into the LGS2F model and its implications for Bitcoin price predictions. Understanding the LGS2F Model: The LGS2F model acknowledges the limitations of the original stock-to-flow model, which projected an infinite growth trajectory for Bitcoin prices. This new model takes a more conservative approach by incorporating the concept of limited growth. By doing so, it aims to provide more realistic predictions that align with the inherent characteristics of Bitcoin. Limited Growth Concept: The concept of limited growth implies that the price of Bitcoin will not skyrocket indefinitely but will experience more moderate growth over time. This idea reflects the understanding that as Bitcoin matures and gains wider adoption, its growth potential becomes constrained by various factors such as market saturation, regulatory influences, and competition from other cryptocurrencies. Predictions for Bitcoin Price: According to the LGS2F model, Bitcoin is projected to reach a price of around 40,000 USD by the end of 2024. This prediction suggests a more measured growth pattern compared to previous models, which envisioned exponential price increases. The modified model takes into account the increasing scarcity of Bitcoin as well as its growing acceptance in various industries and financial markets. Factors Influencing the Predictions: The LGS2F model considers several key factors that impact Bitcoin price predictions: Stock-to-Flow Ratio: The stock-to-flow ratio is a measure of scarcity that compares the existing supply of Bitcoin (stock) to the newly generated supply (flow) each year. It plays a crucial role in the model's calculations and reflects Bitcoin's limited supply. Market Dynamics: The model takes into account market dynamics, including investor sentiment, market cycles, macroeconomic conditions, and regulatory developments. These factors can influence the demand for Bitcoin and consequently affect its price. Adoption and Integration: As Bitcoin gains wider adoption and integration into mainstream financial systems, its perceived value and utility increase. The LGS2F model considers the impact of adoption and integration on price predictions. Conclusion: The LGS2F model provides a modified approach to Bitcoin price predictions by incorporating the concept of limited growth. Its projection of Bitcoin reaching around 40,000 USD by the end of 2024 reflects a more conservative estimate compared to previous models. However, it's important to remember that cryptocurrency markets are inherently volatile and subject to numerous unpredictable factors. As with any predictive model, it's crucial to approach Bitcoin price predictions with caution and consider them alongside other fundamental and technical analysis tools. The LGS2F model offers a fresh perspective that acknowledges the evolving nature of Bitcoin and provides a more realistic framework for understanding its future price movements. read more about the Limited Growth Stock to Flow model: medium.com
BITSTAMP:BTCUSD
by AutomatedTradingAlgorithms
The Ultimate Algorand (ALGO) Analysis - Bottom $0.1618On the 22nd June 2019, Algorand opened at a price of around $3.28 on Coinbase, and slightly higher on Binance. Over the next few months, it dropped to around $0.1648 (maybe $0.1618 on some exchanges) and then $0.097 at the Covid crisis. Before the 2021 bull run, in November, ALGO's Support level was around $0.2247 (Point X of the harmonic) before it began its ascend. In early February of 2021, ALGO topped around $1.8427 (Point A of the harmonic) This increase is by an exact amount of $1.618, the main number in the Fibonacci sequence. Coincidence? I don't think so. After it dropped to Point B of the harmonic, around $0.67, which is a very strong Support/Resistance level. Notice the number - 0.67 is exactly 2/3 of 100. If I multiply 0.6667 by 0.6667 I get 0.44444. 0.6667 - 0.44444 = 0.223, the EXACT NUMBER of Algorand's Support level before the bullrun. OK, now this is getting crazy. Algorand then increased by 161.8% (A-B) to create Point C (around 2.5589). It then dropped to around $1.5144 - the 0.444 support level (which I have marked "S"). (Remember that 0.4444 number from earlier? Yeah.....) The price was then manipulated up to around $2.99-$3. This manipulation point is a whole new conversation involved with even more complex numbers and I think its best we avoid this in this argument, since it doesn't affect this current idea. ANYWAY, if we ignore the manipulation which we usually do in these circumstances and create Point C as our harmonic level, we can see that BC is a +1.618% of AB. Now if we draw a fib between ZERO and A we get 0.618 which is at point B OR if we draw a fib between $0.223 (Start of 2021 bull run) and $1.84 ish (Point A), we get the retracement value around 0.707 which is half of the value of 1.414, and 1.414 is the square root of 2. So AB is (XA x half of the square root of 2) and the next move entails a 1.618 move of that figure. Crazy maths... Anyway, In a standard AB=CD HARMONIC PATTERN, we have 3 different variations, AB=CD, AB=CDx1.272 or AB=CDx1.618. The most common one is 1.272, which is the square root of 1.618. Now what happens if we measure BC x 1.272? The answer is a price of ALGO of $0.1618. As soon as I saw that it hit me. That's the bottom. $0.1618, the Fibonacci golden number will likely be the bottom of Algorand in this cycle. So what is the profit target? So I checked a few measurements. I tried CD x 1.618 (if we hypothetically say that $0.1618 is the bottom of Algorand this cycle) and that gave me a figure of around $4.03. I also did (All Time High minus All Time Low) x 1.272 (the square root of 1.618) and that gave me a similar figure of around $4.03. OH ALSO, one last thing... Algorand is currently in a Bear Flag, the target is around $0.223-0.226 to Buy the bounce. It will go lower around Christmas time, but if you look at the 1.414 level (square root of 2) of the Bear Flag, it also reaches the same point around $0.1618!
by reiiss7
6
Outlook for the Year and the Years to ComeTheoretically, the price of oil should keep going higher as the finite resource is being vehemently overused. Yet, somewhat paradoxically, the advent of alternative energy could produce the opposite effect. Between those two dynamics lie the supply-and-demand pump of the oil states, tweaking the price higher and lower as it fits the pockets of the developed world. The chart shows that oil rose from the ashes from 2016, which coincided ever since with the rise of the markets to the point of hyperinflation last year. Now as the economy is falling down, oil took an adverse course, partly due to the war in Ukraine, but largely due to the status of inflation. Politically and economically, the outcome doesn't seem to change soon. Mathematically, this is also confirmed. The orange line in the chart shows the bisecting trend line which was crossed decisively this year, marking it more likely that prices would stay on the higher levels for some years to come. Regarding Fibonacci levels, the higher point for this year seems to be around 140, to be surrounded by a relative ease in pricing, provided that nothing substantial happens at the macro political level.
O
by MontyMacht
Tesla 22'-23' Forecast (Fibonacci Analysis)Base Case: US Equities are experiencing broad base revaluations due to excess demand in the markets from the 2020 Stimulus. As a result, current markets are survival of the fittest & higher interest rate environments do not suit equities. I believe the markets are currently pricing in the highly anticipated two 50bps (FEDS FUNDS RATE) on June 14-15 & July 26-27 giving stocks room for the final bull rally (End of June - Q1 23') to begin the inevitable bear market, or "hard-landing". (Q2 23 - TBD). I believe the risks of a recession in 2023 are 7/10. Idea: (Long) Entry Price: ~$700.00 Entry Date: ~June 24, 2022 (Mid-to-late) Price Target(s): $1,150.00, $1,250.00 Date Target(s): Mid to Late August, Q4 22' - Q1 23' (Short) Entry Price: $1,150.00, $1,250.00 Entry Date: Mid to Late August, Q4 22' - Q1 23' Price Target: ~$660.00 Date Target: ~June 26, 2023 (Mid-to-late)
NASDAQ:TSLA
by brandonmattis