Geopolitical Risks & Global EventsIntroduction
In today’s interconnected world, financial markets, economies, and even societies are more linked than ever before. A conflict in one part of the globe, a trade dispute between two large economies, or even a natural disaster can ripple across continents within hours. This interconnectedness makes geopolitical risks and global events some of the most critical factors shaping the future of trade, investment, and security.
Geopolitical risks are essentially political, social, or international events that can disrupt economies, destabilize markets, or alter the balance of power between nations. Global events include not just wars or political disputes but also pandemics, climate change, technological revolutions, and financial crises. Together, they form a web of uncertainties that investors, governments, and businesses must constantly navigate.
In this detailed explanation, we will explore:
What geopolitical risks mean.
Types of geopolitical risks.
Examples of major global events that have shaped history.
How these risks impact global markets and businesses.
Strategies for managing and preparing for geopolitical risks.
The future outlook of global risks.
Understanding Geopolitical Risks
At its core, geopolitical risk refers to the possibility that political decisions, conflicts, or instability in one region will have far-reaching effects on the world economy and society.
These risks are not limited to wars. They include:
Tensions between countries (e.g., U.S.-China trade war).
Resource conflicts (e.g., oil supply disruptions in the Middle East).
Terrorism and cyber warfare.
Domestic political instability (e.g., Brexit or protests in Hong Kong).
Pandemics and health emergencies.
Climate change and environmental disasters.
Because the global economy functions like a spider web, pulling one thread can shake the entire structure. For instance, if oil supply routes are disrupted in the Middle East, fuel costs rise globally, impacting transport, manufacturing, and inflation everywhere.
Types of Geopolitical Risks
Geopolitical risks can be classified into several categories:
1. Political Conflicts and Wars
Wars, invasions, or armed clashes between countries disrupt supply chains, displace populations, and create uncertainty in global trade.
Example: Russia’s invasion of Ukraine in 2022 caused massive spikes in oil, natural gas, and wheat prices.
2. Terrorism and Insurgency
Terrorist attacks can destabilize countries and impact global tourism, investment, and trade.
Example: The 9/11 attacks in the U.S. reshaped global security and financial systems, leading to stricter regulations and long wars in Afghanistan and Iraq.
3. Trade Wars and Economic Sanctions
Trade restrictions, tariffs, or sanctions can reshape global supply chains and impact economies.
Example: U.S. sanctions on Iran restricted oil exports, raising energy costs worldwide.
4. Energy and Resource Risks
Control over oil, gas, and rare earth minerals often drives conflict.
Example: OPEC’s decisions on oil output directly affect global energy prices.
5. Cybersecurity Threats
As economies digitize, cyberattacks have become geopolitical weapons.
Example: Alleged state-sponsored cyberattacks on infrastructure, financial institutions, or elections.
6. Domestic Political Instability
Leadership changes, coups, corruption scandals, or protests can destabilize a country.
Example: Brexit in the UK shook European markets and trade relations.
7. Health Crises
Global pandemics affect supply chains, demand patterns, and labor markets.
Example: COVID-19 shut down economies worldwide, sparking recessions and reshaping work and travel.
8. Climate Change and Environmental Risks
Rising sea levels, droughts, and wildfires threaten economies and trigger migration.
Example: Floods in South Asia disrupt agriculture and increase poverty levels.
9. Technological and AI Risks
Technological competition between nations (like the U.S. and China over AI or semiconductors) creates tensions.
10. Financial and Debt Crises
A collapse in one economy can spread globally due to interlinked markets.
Example: The 2008 Global Financial Crisis started in the U.S. but spread across the globe.
Historical Examples of Global Events and Their Impacts
1. World Wars (1914–1945)
World War I and II reshaped borders, destroyed economies, and created new power centers.
The U.S. emerged as a superpower, while Europe rebuilt under the Marshall Plan.
2. The Cold War (1947–1991)
Political and military rivalry between the U.S. and USSR divided the world into capitalist and communist blocs.
Led to proxy wars (Vietnam, Afghanistan) and nuclear arms races.
3. Oil Crises of the 1970s
OPEC’s oil embargo in 1973 caused a global energy shock, highlighting dependence on Middle Eastern oil.
Prices of fuel skyrocketed, triggering inflation and recession in many countries.
4. 9/11 Terrorist Attacks (2001)
Led to wars in Afghanistan and Iraq.
Global security tightened, impacting air travel and financial flows.
5. Global Financial Crisis (2008)
Collapse of U.S. housing bubble triggered bank failures worldwide.
Governments spent trillions in bailouts to save financial systems.
6. COVID-19 Pandemic (2020–2022)
Shrank global GDP, disrupted trade, and accelerated digital transformation.
Highlighted the fragility of healthcare systems and supply chains.
7. Russia-Ukraine War (2022–Present)
Energy prices surged due to sanctions on Russia.
Food shortages arose as Ukraine is a major grain exporter.
NATO and EU politics reshaped.
How Geopolitical Risks Affect the World
1. Impact on Global Markets
Wars and instability cause stock markets to fall as investors seek safe assets like gold and U.S. treasuries.
Example: During the Russia-Ukraine war, European stocks plunged while gold prices rose.
2. Impact on Businesses
Companies face disrupted supply chains, higher costs, and market uncertainty.
Example: Apple and other tech firms restructured supply chains away from China during U.S.-China trade tensions.
3. Impact on Energy and Commodities
Energy supply shocks raise costs across industries.
Example: Gas shortages in Europe after sanctions on Russia increased manufacturing costs.
4. Impact on Currencies
Political uncertainty often weakens local currencies.
Example: Turkish lira collapsed due to domestic political instability and inflation.
5. Impact on Investors
Investors shift to "safe havens" like gold, U.S. dollar, or Swiss franc during crises.
6. Impact on People and Society
Migration, job losses, poverty, and social unrest often follow.
Refugee crises from wars in Syria and Ukraine reshaped Europe’s demographics.
Strategies to Manage Geopolitical Risks
For Governments:
Diversify energy sources to avoid overdependence.
Build strong alliances for economic and security stability.
Invest in cybersecurity as modern warfare shifts online.
Maintain economic buffers like reserves to absorb shocks.
For Businesses:
Diversify supply chains across regions.
Adopt risk management strategies such as insurance.
Monitor geopolitical developments actively.
Develop flexible business models to adapt quickly.
For Investors:
Invest in safe-haven assets during uncertainty.
Diversify portfolios across regions and asset classes.
Use hedging tools (like options and futures) against volatility.
Future Outlook of Geopolitical Risks
The future will likely see greater volatility due to several overlapping factors:
U.S.-China rivalry: Competition in technology, trade, and influence will dominate geopolitics.
Climate-related risks: Extreme weather events will create new economic and humanitarian challenges.
Rise of cyber wars: Digital infrastructure will become a prime target in conflicts.
Shifting alliances: Emerging economies like India, Brazil, and African nations will play a larger role.
Energy transition: The shift from fossil fuels to renewables may trigger resource competition.
AI and technology governance: Nations will compete over dominance in AI, quantum computing, and space.
Conclusion
Geopolitical risks and global events are unavoidable forces shaping the modern world. From wars to pandemics, from energy crises to cyberattacks, their impact is felt everywhere — in stock markets, businesses, and even in people’s daily lives.
While these risks cannot be eliminated, they can be managed and mitigated through foresight, diversification, and resilience planning. For governments, businesses, and investors, understanding the global risk landscape is no longer optional — it is essential for survival and growth.
In the future, the world will remain uncertain, but those who prepare for geopolitical shocks will be better positioned to thrive in a rapidly changing environment.
Chart Patterns
Cryptocurrency & Digital Assets1. Introduction
In the past decade, finance has seen a revolution that goes beyond banks, stock markets, and traditional currencies. This revolution is called cryptocurrency and digital assets. What started as a niche experiment with Bitcoin in 2009 has now become a global phenomenon worth trillions of dollars. Cryptocurrencies, non-fungible tokens (NFTs), central bank digital currencies (CBDCs), and blockchain-based assets are redefining money, ownership, and trust in the digital era.
To understand this world, we need to cover not only the technical foundation but also the real-world applications, benefits, challenges, and risks. Let’s explore.
2. What Are Digital Assets?
At the core, a digital asset is anything of value stored electronically. This can include documents, music, art, or data. But in financial terms, digital assets refer to assets that exist purely in digital form and can be owned, transferred, or traded.
Examples:
Cryptocurrencies (Bitcoin, Ethereum)
Stablecoins (USDT, USDC)
Security tokens (digital representation of real-world securities)
NFTs (unique digital collectibles/art)
Central Bank Digital Currencies (CBDCs)
Digital assets are usually recorded and verified using blockchain technology, which ensures transparency, immutability, and decentralization.
3. What is Cryptocurrency?
A cryptocurrency is a type of digital asset designed to work as a medium of exchange, store of value, or unit of account. It is secured by cryptography, making it difficult to counterfeit or double-spend.
Key Features:
Decentralization – Not controlled by a single authority like banks or governments.
Blockchain-based – Transactions are recorded on a distributed ledger.
Cryptographic Security – Ensures authenticity and prevents fraud.
Peer-to-Peer Transactions – People can send money directly without intermediaries.
Global & Borderless – Works across countries with internet access.
4. The Origin of Cryptocurrencies
The story begins in 2008 when an anonymous person or group known as Satoshi Nakamoto released a whitepaper:
“Bitcoin: A Peer-to-Peer Electronic Cash System.”
The idea was to create money outside of government control, relying on cryptography and decentralized networks.
In 2009, Bitcoin was launched. It introduced blockchain technology as a transparent ledger, enabling trust without banks.
From there:
2015: Ethereum introduced smart contracts.
2017–2018: ICO (Initial Coin Offering) boom.
2020–2021: Rise of DeFi (Decentralized Finance) and NFTs.
2022–2023: Market corrections, regulations, and institutional adoption.
2024 onward: Growth of CBDCs, tokenization, and AI integration.
5. How Cryptocurrencies Work
To understand cryptocurrencies, let’s break down the components:
a) Blockchain Technology
A blockchain is a decentralized digital ledger that records all transactions.
Each block contains transaction data, a timestamp, and a cryptographic hash.
Once added, blocks cannot be altered (immutability).
b) Mining & Consensus Mechanisms
Proof of Work (PoW): Used by Bitcoin. Miners solve puzzles to validate transactions.
Proof of Stake (PoS): Used by Ethereum 2.0. Validators stake coins to secure the network.
Other mechanisms: Delegated Proof of Stake, Proof of Authority, etc.
c) Wallets & Keys
To own cryptocurrency, you need a digital wallet.
Wallets use private keys (your password to access funds) and public keys (your address to receive funds).
d) Transactions
When you send Bitcoin, your transaction is broadcasted to the network.
Miners/validators verify and record it on the blockchain.
Once confirmed, it becomes permanent.
6. Types of Cryptocurrencies
Bitcoin (BTC):
First cryptocurrency, digital gold.
Mainly used as a store of value.
Ethereum (ETH):
Introduced smart contracts and decentralized applications (dApps).
Backbone of DeFi and NFTs.
Stablecoins (USDT, USDC, DAI):
Pegged to stable assets like the US dollar.
Reduce volatility, widely used in trading.
Altcoins (Litecoin, Ripple, Cardano, Solana, etc.):
Offer various improvements or innovations over Bitcoin/Ethereum.
Utility Tokens:
Used within specific platforms (e.g., Binance Coin, Chainlink).
Security Tokens:
Represent ownership in real assets (stocks, real estate).
Non-Fungible Tokens (NFTs):
Unique digital items (art, music, in-game assets).
7. Non-Fungible Tokens (NFTs)
NFTs became mainstream in 2021 when digital art sold for millions.
Unlike cryptocurrencies (fungible, interchangeable), NFTs are unique and indivisible.
Examples:
Digital artwork (Beeple’s $69 million sale)
Collectibles (NBA Top Shot)
In-game items (Axie Infinity)
Music rights & virtual real estate
NFTs represent a revolution in digital ownership.
8. Decentralized Finance (DeFi)
DeFi is a financial ecosystem built on blockchain, without intermediaries like banks.
Key elements:
Lending & Borrowing Platforms (Aave, Compound)
Decentralized Exchanges (DEXs) (Uniswap, PancakeSwap)
Yield Farming & Liquidity Mining
Synthetic Assets & Derivatives
Benefits:
Open to anyone with internet.
Transparent and programmable.
Higher returns compared to traditional banking.
9. Central Bank Digital Currencies (CBDCs)
Governments are developing their own digital money, called CBDCs.
Unlike cryptocurrencies, CBDCs are centralized and backed by national banks.
Examples:
China’s Digital Yuan (e-CNY)
India’s Digital Rupee (pilot launched by RBI)
European Union exploring Digital Euro
CBDCs aim to combine the efficiency of digital assets with the trust of government money.
10. Advantages of Cryptocurrencies & Digital Assets
Decentralization – Reduced dependency on banks/governments.
Fast & Cheap Transactions – Cross-border payments in seconds.
Financial Inclusion – Access for unbanked populations.
Transparency – Blockchain records are public and verifiable.
Ownership Control – You truly own your assets (self-custody).
Innovation & Programmability – Smart contracts enable new business models.
Global Access – Works anywhere with internet.
Potential for High Returns – Many investors see massive growth.
Conclusion
Cryptocurrencies and digital assets are more than just speculative investments—they represent a new paradigm for money, ownership, and trust in the digital age. While risks exist, the opportunities for innovation, financial inclusion, and global economic transformation are immense.
From Bitcoin’s vision of decentralized money to NFTs redefining art and CBDCs reshaping government-issued currency, the world of digital assets is evolving rapidly. We are witnessing a once-in-a-generation shift that could impact how humans trade, invest, and interact for decades to come.
Divergence and Convergence: How to Read Market SignalsThe cryptocurrency market, like any financial market, is full of paradoxes. Price can rise, yet the strength of the trend is already weakening. Indicators may show that the move is “running on fumes,” but most traders keep buying at the top or selling at the bottom. The result is always the same: emotional trading and chaos instead of system and consistency.
The main problem is that most participants only look at price. But price is just the tip of the iceberg. Beneath it lie volumes, momentum, trader sentiment, and recurring statistical patterns. This is where divergence and convergence come into play — signals that often warn of a trend change long before it becomes obvious.
What are Divergence and Convergence
Divergence occurs when the price makes new highs or lows, but a momentum indicator (such as RSI or MACD) shows the opposite — weakening strength. It’s a signal that the trend is losing energy and the probability of reversal is rising.
Convergence is the opposite. The price updates a low, but the indicator shows higher readings. This suggests sellers are losing steam and buyers may soon regain control.
On the chart, these may look like small details, but for an attentive trader, they mark turning points — the very beginnings of shifts that later become obvious to everyone else.
Why These Signals Matter
Imagine Bitcoin climbing from $105,000 to $118,000. Everyone is euphoric, and newcomers rush to open longs, hoping for more upside. Meanwhile, RSI is already showing divergence: price is up, momentum is down. For a careful trader, that’s a red flag.
Moments like this help avoid buying at the peak and prepare for an incoming correction. More importantly, divergences not only give exit signals but also highlight potential reversal zones — places where traders can plan new entries in the opposite direction.
How to Read Divergence and Convergence
Compare price highs/lows with the indicator. If price rises but the indicator falls — it’s divergence.
Check the context. A single signal on the indicator means little. Support/resistance levels, volumes, and candlestick structure matter.
Be patient. Divergence can form over several candles, and the market often makes one last push before turning.
Combine tools. Use divergence alongside TP/SL zones and trendlines to improve accuracy.
Common Mistakes
Many beginners make the same error: they see divergence and instantly trade against the trend. That’s wrong. Divergence isn’t a “buy/sell button,” it’s a warning. It says: “Be cautious, momentum is fading.” The actual reversal must still be confirmed by price structure and volumes.
Another mistake is ignoring timeframe. Divergence on a 5-minute chart may only play out for a few dollars, but on a 4H or daily chart, the move could be massive.
Building it Into a System
This is the crucial part. An indicator alone won’t make a trader successful. Divergence and convergence need to be part of a system where:
- entry and exit zones are pre-defined,
- profit targets are clearly marked,
- risk is limited by stop-losses,
- and decisions are made without emotions, based on structure.
This is where algorithms and automation prove invaluable. An automated model spots divergence earlier than the eye, flags conditions for a probable trend shift, and guides the trade step by step.
Why It Works
Markets move in cycles, and history repeats. Divergence and convergence are not magic, but a reflection of market physics: momentum fades, energy runs out, and no trend lasts forever. Ignoring these signals means trading blind.
Integrating them into a structured process means having a map of potential scenarios ahead of time. It doesn’t guarantee perfection, but it eliminates guesswork and replaces it with probabilities and discipline.
Conclusion
Divergence and convergence are market warnings for those who pay attention. They help traders exit on time, avoid entering at peaks, and prepare for reversals. Most importantly, they train discipline and patience — the qualities that separate long-term survivors from those who get washed out.
In a world where emotions break strategies, systematic analysis provides the edge. Automation, technical tools, and the ability to read market structure turn chaos into a structured process. For traders seeking to look deeper than just price, divergence and convergence are signals worth learning to read as carefully as a book.
Best Trading Confirmation. Learn 95% Accurate Entry Signal
I have analyzed 1300 forecasts and signals that I shared on TradingView last year and found 95% accurate trading confirmation.
In this article, we will discuss multiple types of confirmations and their winning rate on Forex, Gold, Indexes, Crypto & Commodities.
First, let me introduce you to the types of analysis that I provided.
1 - Structure based forecast
I have shared more than 55 trading setup with key levels analysis:
Where the price is approaching a key daily horizontal support and resistance.
Here is the example of such a post.
Test of a key horizontal or vertical support/resistance turned out to be a poor trading signal.
Total accuracy of structure based forecasts is 38%.
Please, note that if we consider the market trend in our calculations,
the trend-following structure based setup will be 42% accurate, while a performance of a counter trend setup drops to 35%
2 - Structure breakout based forecast
I analyzed and posted 73 posts with a key structure breakout as a confirmation on a daily.
Above is the example of a such a forecast.
Key levels breakout turned out to be a strong bullish or bearish confirmation with 59% accuracy.
Trend direction did not affect the efficiency of a key structure breakout that much, with a 60% accuracy of a trend following setup versus 57% of counter trend.
3 - Structure based forecast with a single intraday confirmation
I shared more than 500 setups with a test of a key structure on a daily and a single price action based bullish or bearish confirmation on a 4h/1h time frame.
My intraday confirmation is a formation of a price action pattern with a consequent breakout of its neckline/trend line in the projected direction.
Please, check the example of such a signal.
Just a single intraday confirmation dramatically increases the accuracy of a structure based setup.
Average winning rate is 66%.
4 - Structure based forecast with multiple intraday confirmations
I spotted and posted 200+ forecasts with a test of a key structure on a daily and multiple price action based bullish or bearish confirmations on a 4h/1h time frame.
Multiple confirmations imply the formation of multiple price action patterns on 4/1h t.f.
Here is the example of such a setup on EURGBP.
Two or more confirmations on a key structure increase the average winning rate to 72%.
Among multiple confirmations, I found a 95% accurate bearish signal:
The market should be in a bearish trend.
The price should test a key daily structure resistance.
The market should form a rising wedge pattern on a 4h/1h time frames and the highs of the wedge should strictly test the key structure and should not violate them.
After a test of structure, the price should form a bearish price action pattern on the highs of the wedge.
Above is a setup with the best trading confirmation.
A bearish breakout of the neckline of the pattern and a support of the wedge was a 95% accurate trading signal last year.
Of course, there are various confirmations, depending on a trading style. The ones that I shared with you are structure/price action based.
And I am truly impressed by their accuracy.
❤️Please, support my work with like, thank you!❤️
I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
Can Yen Futures Push Higher? Inverted H&S Breakout in Focus1. Introduction
Japanese Yen Futures (6J) and Micro Yen Futures (MJY) are showing a promising technical setup that traders are watching closely. On the daily chart, an inverted Head and Shoulders pattern has formed, suggesting a potential reversal from recent weakness. The neckline lies around 0.006850, and if prices sustain a breakout above this level, the upside projection aligns neatly with a UFO resistance zone near 0.007100.
Adding weight to this bullish case, the MACD histogram is diverging positively, with higher lows forming while price action recorded lower lows. This bullish divergence suggests underlying momentum could support the completion of the pattern and drive Yen Futures higher in the sessions ahead.
2. Understanding the Inverted Head & Shoulders Pattern
The inverted Head & Shoulders (H&S) is a widely recognized reversal formation that often signals the end of a bearish trend. It is composed of three troughs: the left shoulder, the head (the deepest low), and the right shoulder, which is typically shallower. The neckline acts as the key breakout level, and once broken, the projected price target is measured from the head to the neckline, then projected upward.
In the case of Japanese Yen Futures, the neckline sits around 0.006850. A confirmed break above this price would validate the pattern, projecting a target toward 0.007100.
3. The Role of MACD Divergence
Momentum indicators could provide early clues about the strength of a potential breakout. In this case, the MACD histogram is showing bullish divergence—price made lower lows, while the histogram made higher lows. This divergence signals that selling pressure may be weakening, even as price was still falling.
Such conditions could potentially precede significant reversals, and when they align with a clear price pattern like the inverted Head & Shoulders, the probability of follow-through may increase. Traders monitoring this confluence will be looking at the neckline breakout above 0.006850 as the technical trigger that confirms it.
4. Contract Specs: Yen Futures vs. Micro Yen Futures
Understanding contract specifications helps traders size positions correctly and manage risk efficiently.
o Japanese Yen Futures (6J)
Contract Unit: ¥12,500,000
Minimum Tick: 0.0000005 per JPY = $6.25 per contract
Initial Margin (approximate, subject to change): ~$3,100
Popular with institutional traders due to larger notional exposure.
o Micro JPY/USD Futures (MJY)
Contract Unit: ¥1,250,000 (1/10th of standard 6J contract)
Minimum Tick: 0.000001 per JPY = $1.25 per contract
Initial Margin (approximate, subject to change): ~$310
Provides accessibility for retail traders and allows more granular risk management.
Both contracts track the same underlying, but the Micro contract offers flexibility for traders with smaller accounts or those looking to fine-tune position sizes.
5. Trade Plan & Stop Loss Options
With the inverted Head & Shoulders pattern taking shape, the trade bias turns long above the neckline breakout at 0.006850. The upside objective aligns with the resistance around 0.007100, providing a clearly defined target.
Two possible stop-loss placements can be considered:
o Below the Right Shoulder
Provides a valid protection but may offer a weaker Reward-to-Risk (R:R) ratio depending on the right shoulder height.
Useful for conservative traders looking to minimize drawdowns.
o Mathematically Below the Neckline
Positioned far enough to allow for retests of the neckline while aiming for a 3:1 R:R ratio.
Provides a balance between protection and potential profitability.
This approach ensures flexibility, letting traders choose between tighter risk control or a more favorable reward profile.
6. Risk Management Considerations
No pattern or indicator guarantees success, making risk management the cornerstone of any futures strategy. A few key principles stand out:
Always use a stop loss: Prevents small losses from escalating into significant drawdowns.
Avoid undefined risk exposure: Futures are leveraged products; unprotected trades can lead to large, rapid losses.
Precision in entries and exits: Reduces emotional decision-making and improves consistency.
Position sizing matters: Adjusting the number of contracts ensures risk stays proportional to account size.
Diversification and hedging: Yen futures can be used as a hedge against equity or bond market volatility, but should not necessarily replace broader risk controls.
In this context, choosing the stop-loss level carefully and sticking to the pre-defined trade plan is more important than the pattern itself.
7. Conclusion & Forward View
Japanese Yen Futures (6J) and Micro JPY/USD Futures (MJY) are at a critical juncture. The inverted Head & Shoulders on the daily chart, supported by a bullish MACD divergence, highlights a potential reversal in progress. A breakout above the neckline at 0.006850 opens the door for an advance toward the 0.007100 UFO resistance zone.
While the setup looks constructive, it is crucial to recognize that even strong patterns can fail. This is why risk management—through proper stop-loss placement and careful position sizing—remains the most important aspect of any trading plan.
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.
3 Actionable FX Strategies — With Real Trade Examples👋 Below are three practical strategies you can plug into your playbook today:
1. swing reversals (80+ pips), 2) short-term scalps (20–40 pips), and 3) the London range breakout (≈40 pips). Each section includes rules of engagement, risk management, and three real-market case studies on EURUSD and GBPUSD with conservative stops.
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🔁 Strategy 1 — 4H Swing Reversals (Target: 80–120 pips)
Setup 🧩
• Identify exhaustion into a higher-timeframe S/R zone (4H/Day).
• Look for a reversal signal (engulfing/pin bar, momentum shift, or divergence) and a confirmation close.
• Conservative stop: beyond the swing extreme or ~1× ATR(14) on the entry timeframe.
• Take-profit: next HTF level or ≥ 1.8R, aiming for 80+ pips.
Case study A — EURUSD long (Jackson Hole boost) 📈
• When: Aug 22, 2025, NY session after Powell; EURUSD pushed above 1.1700 on broad USD weakness.
• Plan: After a 4H close back above 1.1700, buy a retest ~1.1705.
• Stop: 1.1650 (≈55 pips).
• Target: 1.1790 (≈85 pips).
Case study B — GBPUSD short (post-CPI fade) 📉
• When: May 21, 2025, UK CPI spike ran to 1.34695 then faded.
• Plan: After a 15–30m lower high below 1.3460, sell break of 1.3435.
• Stop: 1.3490 (≈55 pips).
• Target: 1.3345 (≈90 pips).
Case study C — EURUSD short (overextended pullback) 🔻
• When: Jul 1, 2025, EURUSD briefly poked above 1.1800 then eased.
• Plan: Sell 1.1775 after a 1H bearish engulfing.
• Stop: 1.1825 (≈50 pips).
• Target: 1.1690 (≈85 pips).
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⚡ Strategy 2 — Short-Term Scalping (Target: 20–40 pips)
Setup 🧩
• Trade during high liquidity (London open or London/NY overlap).
• Use 1–5m charts: micro S/R + round numbers, quick momentum bursts.
• Conservative stop: 8–15 pips (just beyond the micro structure).
• Take-profit: 20–40 pips or to next intraday level.
Case study D — EURUSD scalp long (pre-Jackson Hole range) ⏱️
• When: Aug 21, 2025, Europe a.m.; EURUSD near 1.1650.
• Plan: Buy break-and-retest 1.1665.
• Stop: 1.1652 (≈13 pips).
• Target: 1.1687 (≈22 pips).
Case study E — GBPUSD scalp long (soft US CPI pop) 💥
• When: May 13, 2025, post-US CPI tone lifted risk; GBPUSD ~1.3226.
• Plan: Buy 1.3218 → 1.3242 after higher-low.
• Stop: 1.3208 (≈10 pips).
• Target: +24 pips.
Case study F — EURUSD scalp long (grind to 1.09) 🚀
• When: Mar 11, 2025, London morning; EURUSD nudged to 1.0890 / kissed 1.0900.
• Plan: Buy 1.0885 on retest.
• Stop: 1.0875 (≈10 pips).
• Target: 1.0905 (≈20 pips).
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🕘 Strategy 3 — London Range Breakout (Target: ~40 pips)
Setup 🧩
• Mark the Asian/Late-Asia range before 08:00 London.
• Trade the first clean break/close outside the box.
• Entry: stop order beyond the box high/low.
• Conservative stop: opposite side of the box or box size + buffer (≤40–50 pips).
• Take-profit: ~40 pips (scale at 20 pips).
Case study G — GBPUSD upside break (calm pre-CPI session) 📦➡️📈
• When: Mar 25, 2025, London a.m.; GBPUSD drifted toward 1.2950.
• Box: 05:00–08:00 London ~22 pips.
• Plan: Buy box high +3 pips (≈1.2953).
• Stop: 1.2930 (≈23 pips).
• Target: 1.2993 (≈40 pips).
Case study H — EURUSD downside break (trend day toward 1.09) 📦➡️📉
• When: May 12, 2025, EURUSD bias turned lower and eyed the 1.09 handle.
• Box: 05:00–08:00 London ~28 pips.
• Plan: Sell box low −3 pips (≈1.0978).
• Stop: 1.1008 (≈30 pips).
• Target: 1.0938 (≈40 pips).
Case study I — GBPUSD downside break (inflation-week nerves) 📦➡️🔻
• When: Aug 12, 2025, London a.m.; GBPUSD softened from a two-week high.
• Box: 05:00–08:00 London ~24 pips.
• Plan: Sell box low −2 pips (≈1.3446).
• Stop: 1.3472 (≈26 pips).
• Target: 1.3406 (≈40 pips).
________________________________________
🛡️ Risk Management (applies to all three)
• Risk small per trade (e.g., 0.5–1%).
• Stops beyond structure: previous swing/box edge or ATR-based to avoid noise.
• News filter: avoid fresh entries seconds before major economic data.
________________________________________
🧰 Quick Checklists
Swing reversal (4H) ✅
🎯 Level picked • 📉 Reversal signal • 🛑 Stop beyond swing/ATR • 📐 ≥1.8R • 📰 No imminent shock
Scalp (1–5m) ✅
⏱️ Active session • 🔍 Micro S/R & round numbers • 🛑 8–15 pip stop • 🎯 20–40 pips • ✂️ Partial at +10–15
London breakout ✅
🕗 Box 05:00–08:00 • 📦 Reasonable width • 🚀 First break/close • 🛑 Stop other side • 🎯 ≈40 pips
________________________________________
⚠️ Final word
These examples show how setups map onto real market context. Adapt entries/levels to your feed and spreads. Nothing here is financial advice—test and size appropriately.
________________________________________
Boom and Crash Strategy on tradingview – Smart Money ConceptTrading Boom and Crash indices can be exciting, but also very challenging. These synthetic assets are designed with volatility in mind. Boom creates sudden upward spikes, while Crash produces sharp downward spikes. For most traders, these spikes feel random, but when you understand market structure and timing, they actually make sense.
In this post, I want to share a detailed Boom and Crash trading strategy based on smart money concepts (SMC). This is not about chasing every spike or relying on heavy indicators. Instead, it’s about learning how the market moves, spotting liquidity traps, and waiting for the right confirmations before entering.
Why Boom and Crash Are Different
Unlike forex pairs or crypto assets, Boom and Crash follow an internal synthetic engine created by Deriv. This means:
They run 24/7 without downtime.
There are no external fundamentals moving them — only programmed volatility.
Spikes are built into their behavior.
Because of this, traditional technical analysis alone often leads to frustration. Many traders try to scalp spikes randomly and end up losing accounts. What works better is combining price action with smart money concepts to create rules for when and where to trade.
Core Elements of the Strategy
Here’s the step-by-step structure of the strategy explained in my video:
1. Liquidity Grab
Markets often move to take out stop-loss clusters before reversing. On Boom and Crash, this is even clearer — you’ll see price sweep recent highs or lows with a sudden spike. That’s your signal that the market is preparing to move the other way.
2. Supply and Demand Zones
Instead of chasing every candle, mark out zones where price previously moved aggressively. These are institutional footprints. When price comes back to test these zones, you prepare for entries.
3. Fractal Confirmation
Don’t enter immediately when price touches your zone. Wait for confirmation — such as a smaller structure break, rejection wick, or micro liquidity grab. This reduces false entries.
4. 1-Minute and 5-Minute Setups
The Boom and Crash 1-minute strategy is for scalpers who want quick profits, but I recommend checking the 5-minute chart for context. Using both keeps you aligned with short-term opportunities while respecting the bigger picture.
5. Best Times to Trade
Timing matters. Even though Boom and Crash are open 24/7, volatility has cycles. Trading during low-volume windows (when fewer spikes are engineered) often produces smoother moves and cleaner setups.
Example Setup
Imagine Boom 1000 is consolidating near a previous high. Suddenly, it spikes above that high, grabbing liquidity. Instead of buying the spike, you mark the supply zone left behind. When price returns to test that zone, you wait for confirmation (a break of structure on the 1-minute chart). That’s your entry for a short, riding the move down safely.
This method works because you’re trading with the market’s intention, not against it.
Risk Management
No strategy works without discipline. For Boom and Crash especially, lot size and stop loss make the difference between growing an account and blowing one.
Risk no more than 2% per trade.
Always set a stop loss, even if it’s mental.
Take profits at clear liquidity pools instead of holding forever.
Remember, consistency matters more than catching every big spike.
Why This Strategy Works
The beauty of this strategy is that it simplifies trading Boom and Crash. Instead of chasing random spikes, you’re reading the “story” of the market: where liquidity is, where institutions are positioned, and when the reversal is most likely.
It also gives confidence. Many traders hesitate to enter because Boom and Crash look unpredictable. With this method, you have rules:
Wait for liquidity grab.
Mark supply/demand.
Confirm with structure.
Enter with controlled risk.
My Journey With Boom & Crash
When I first started with Boom and Crash, I made the same mistakes most traders do. I tried scalping every spike, opening too many positions, and hoping luck would carry me. Accounts got blown faster than they were funded.
It wasn’t until I studied price action and smart money concepts that things changed. I realized Boom and Crash don’t need dozens of indicators. They just need patience, timing, and a structured plan.
This strategy is the result of testing, failing, refining, and testing again. Now it’s the backbone of how I approach synthetic indices.
Key Takeaways
Don’t chase every spike — let the market grab liquidity first.
Focus on supply and demand zones for cleaner entries.
Use 1-minute for scalps, 5-minute for context.
Trade during stable sessions for less noise.
Protect your account with strict risk management.
Final Thoughts
Boom and Crash can either be a trader’s nightmare or a powerful opportunity. It all depends on how you approach them. With a structured strategy based on smart money concepts, you don’t have to guess — you simply wait for the market to show its hand.
If you’re serious about trading these indices, I encourage you to watch the full video breakdown. It walks through chart examples, entry setups, and risk management in detail.
How Institutions Trade with Smart Money ConceptMost traders lose because they don’t understand how the big players (banks & institutions) actually move the markets.
Institutions don’t rely on RSI, MACD, or retail indicators — they move billions with Smart Money Concept (SMC), targeting retail stop losses and fueling big moves.
In this video, I break down:
✅ Market Structure – how institutions decide direction
✅ Liquidity Grabs – stop hunts that trap retail traders
✅ Order Blocks & Fair Value Gaps – where banks enter positions
✅ Step-by-step Institutional Playbook you can follow
💡 Key Idea:
Institutions create the moves retail traders chase. By following market structure, liquidity pools, and order blocks, you can trade WITH the smart money — not against it.
📊 Example Inside the Video:
Real chart breakdown (XAUUSD & EURUSD)
Spotting liquidity pools (equal highs/lows)
Entry after market structure shift
Risk-to-reward setup like institutions
If you want to stop trading like retail and start trading like the banks, this is for you.
📌 Hashtags (for reach):
#SmartMoneyConcept #ForexTrading #FrankFx #LiquidityGrab #OrderBlock #SMCStrategy #TradingView
Just Because It’s Big Doesn’t Mean It’s SmartJPY Call Spread Breakdown: Bullish Signal — Or Just Obvious FOMO?
A new vertical call spread appeared in JPY options yesterday (per CME Globex data):
Long 0.0069 Call
Short 0.007025 Call
🎯 Target: 0.007025 — upside continuation play.
Open interest increased at both strikes → new position, not a roll.
Size? Relatively large for JPY (based on systematic observations).
⏰ When Was It Opened?
9:45 AM CT — after yesterday’s sharp rally in JPY futures.
In fact — right at the top of the move.
📌 Not before the move.
But after the impulse, on momentum.
🔍 Combining Flow + Chart Context:
Price had already spiked up.
The spread bets on further upside .
🧠 Key Takeaways:
✅ Sentiment: Bullish
❌ Predictive value: Low — nearly zero
Why?
The setup is too obvious.
No evidence of insider-like timing.
If this had been placed before the move — yes, it would matter.
But opening at the peak? That’s not edge — it’s FOMO dressed as strategy.
🚫 Will I go long JPY futures based on this?
No.
Not because I doubt the move.
But because this isn’t smart money behavior — it’s trend-chasing.
🎯 Final Lesson:
Not every large options trade is a signal.
Always ask:
When was it placed?
Why here?
Who’s behind it?
🔍 True edge isn’t in the trade itself — it’s in the context around it.
Liquidity Grab Strategy | Smart Money ConceptHave you ever had your stop loss hunted before price moved in your direction?
That’s called a Liquidity Grab — one of the most powerful setups in Smart Money Concept (SMC).
In this video, I break down:
What Liquidity Grab really means 📊
How institutions use stop hunts to fuel big moves 🏦
Step-by-step guide to trade liquidity grabs profitably
Real chart example on XAUUSD with 1:5 Risk-Reward setup 💰
📌 Why Watch This Video?
Stop chasing false breakouts 🚫
Learn to spot liquidity pools (double tops/bottoms) ✅
Understand confirmation entries after the grab 🎯
Trade with Smart Money, not against it ⚡
🔗 Watch Full Video Here: Liquidity Grab Strategy | Smart Money Concept
📈 Chart Highlight (From Video)
Equal highs formed → liquidity pool created
Price spiked above → retail stops hunted
Market reversed with momentum → clean entry after structure shift
This is exactly how institutions move the market. Knowing this gives you the edge most retail traders miss.
⚡ Key Takeaway
Liquidity Grabs are not manipulation against you — they’re opportunities.
Flip the script: enter with institutions, not against them.
📌 Tags
#SmartMoneyConcept #LiquidityGrab #ForexTrading #XAUUSD #SMC #SupplyAndDemand
EUR/GBP, EUR/USD, NZD/USD, Video of my trades last weekMy second video explaining my trades for last week 18-22nd August. I have been trading for years but just started publishing my trades. Hopefully this will keep me more disciplined and someone might learn something. If you have any questions send me a message here or on X and enjoy the weekend.
Smart Money Concepts LuxAlgo: Trade Like InstitutionsMost traders lose money because they buy and sell randomly. Smart Money Concepts (SMC) changes that by focusing on how big institutions actually move the market — using order blocks, liquidity grabs, supply & demand, and fair value gaps.
Now, imagine combining SMC with LuxAlgo’s Smart Money Concepts indicator on TradingView. This tool automatically marks out order blocks, liquidity levels, and imbalances, making it much easier to spot high-probability setups.
🔹 Key Points Covered in My Video
What Smart Money Concepts really mean
How LuxAlgo highlights order blocks, liquidity sweeps & FVGs
Step-by-step trade confirmation using SMC + LuxAlgo
Real chart examples for forex, gold, and indices
If you’re tired of trading blind and want to understand the market like institutions do, this video is for you.
👉 Watch the full video here
🔔 Don’t forget to subscribe to my channel FrankFx for more trading tutorials and SMC strategies.
#SmartMoneyConcepts #LuxAlgo #Forex #XAUUSD #OrderBlocks #Liquidity #FairValueGap #FrankFx
Mastering trendbreaks - How to trade it?In this guide will the trendbreaks be discussed. The following subjects will be explained:
- What is a trend?
- What is a bearish trendline break?
- What is a bullish trendline break?
- How to trade a trendbreak?
- Example
What is a trend?
A trend is the backbone of price action in any market. It represents the general direction in which price is moving over a sustained period of time. When price is consistently creating higher highs and higher lows, the market is considered to be in an uptrend. This behavior shows that buyers are in control and are willing to keep paying higher prices with each wave. On the other hand, when price continues to make lower highs and lower lows, the market is in a downtrend. This shows that sellers dominate the market and buyers are unable to push price above previous levels. Understanding trends is essential because it gives traders a framework for anticipating what is most likely to happen next, rather than guessing in random price action.
What is a bearish trendline break?
A bearish trendline break takes place when an established uptrend begins to lose momentum. In an uptrend, price usually respects a rising trendline, bouncing off it multiple times as buyers defend the bullish structure. Eventually, there comes a point when the market can no longer sustain this strength. Price breaks down through the rising trendline, signaling potential weakness. However, the true confirmation of a bearish shift only happens once the market also breaks below the most recent higher low. This is the key moment where structure changes. What was once a sequence of higher highs and higher lows now transforms into lower highs and lower lows, showing that sellers are gaining control. Without this structural shift, the break of the trendline alone might just be a temporary pullback or a false signal.
What is a bullish trendline break?
A bullish trendline break is the mirror image. In a downtrend, price respects a falling trendline as it consistently makes lower highs and lower lows. Each rally upward fails to break past previous highs, confirming sellers’ control. Eventually, price surges and breaks above the falling trendline. Just like with a bearish break, this initial move is not enough on its own. The true sign of reversal comes when price also breaks above the most recent lower high. This action destroys the existing bearish structure, which relied on lower highs to remain valid. Once that lower high is broken, the market shows that buyers have taken back control and a potential uptrend may begin.
How to trade a trendbreak?
For a valid trendbreak, three conditions must come together. First, the price must break the trendline itself, either rising or falling depending on the direction of the trend. Secondly, the breakout needs to be with strong volume. lastly, the price must also break the most recent higher low in an uptrend or lower high in a downtrend. Without this structural break, what looks like a reversal may only be a correction before the market resumes in its original direction. This distinction is crucial because many traders enter too early on a simple trendline break, only to get caught when the market snaps back into the trend. The combination of both the trendline break and the structural break provides much stronger confirmation.
Trading the trendbreak is where discipline and patience make the difference between success and failure. When the structure has been broken, it is tempting to enter immediately in the direction of the new move. But the higher-probability entry usually comes from waiting. Price often pulls back after a break, returning to retest the broken level. This retest can take different forms. Sometimes price simply returns to the broken higher low or lower high and uses it as support or resistance. Other times, price fills what traders call a fair value gap (fvg), which is an imbalance left on the chart when price moves too quickly in one direction without much trading in between. By waiting for this retest, a trader enters at a better price, with a tighter stop loss and greater profit potential.
Example
For example, imagine the market in an uptrend. Price respects a rising trendline until it finally breaks through it. Shortly after, the market breaks below the most recent higher low, confirming the bearish trendbreak. Instead of selling right at the break, the disciplined trader waits. Price pulls back upward to retest the broken higher low, which now acts as resistance. At that moment, the trader sells with a stop loss just above the retest level and targets the next support or previous swing low. This provides a controlled risk and larger potential reward.
The same logic applies to a bullish trendbreak. Price in a downtrend breaks above the falling trendline, then pushes higher to break a lower high, flipping the structure bullish. Price later dips back down to retest the broken lower high or fills a fair value gap. When it holds and begins to rise again, the trader enters long, with a stop below the retest and a target at the next resistance level.
By combining awareness of trendlines, structural shifts, and retest opportunities, traders can filter out false signals and position themselves to catch the early stages of new trends. The trendbreak is not just about spotting the first sign of weakness or strength, but about confirming that the underlying structure has truly changed. This approach gives a trader clarity, consistency, and confidence in execution, making trendbreaks one of the most powerful tools for price action trading.
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Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
Thanks for your support. If you enjoyed this analysis, make sure to follow me so you don't miss the next one. And if you found it helpful, feel free to drop a like 👍 and leave a comment 💬, I’d love to hear your thoughts!
Dow Theory: Unlocking Market Trends for Consistent ProfitsDow Theory is the foundation of modern technical analysis. Developed by Charles H. Dow in the late 19th century, this theory asserts that the market reflects all information and price movements always follow identifiable trends. To this day, Dow Theory remains a "compass" for traders in understanding price behavior.
6 Core Principles of Dow Theory:
The Market Reflects All
Price includes all information: news, expectations, psychology, and economic data. Therefore, the chart is the most reliable source of information.
The Market Has 3 Trends
Primary Trend: Lasts for several months to years.
Secondary Trend: Adjustments within the primary trend, usually lasting a few weeks.
Minor Trend: Fluctuates over a few days, less significant.
The Primary Trend Has 3 Phases
Accumulation: Smart investors quietly buy.
Public Participation: Large capital flows in, and the trend becomes clear.
Distribution: Large institutions begin to offload, preparing for reversal.
Indices Must Confirm Each Other
Dow used the industrial and railroad indices; today, this means trends are only valid when multiple markets/inter-markets confirm the same direction.
Volume Confirms the Trend
In an uptrend, volume should increase when the price rises and decrease during corrections. The opposite is true for downtrends.
Trends Continue Until Clear Reversal Signals Appear
Traders shouldn’t try to pick bottoms or tops, but rather follow the trend until there's confirmation of a change.
Practical Significance for Traders:
Helps identify the main trend to follow the big money.
Aids in risk management by avoiding trading against the trend.
Provides a comprehensive view: price, volume, and market phases.
Trading Imbalances: How to Use Fair Value GapsDifficulty: 🐳🐳🐋🐋🐋 (Novice+)
This article is designed for traders who want to understand Fair Value Gaps (FVGs) in a simple, practical way — without drowning in complex Smart Money Concepts terminology.
🔵 INTRODUCTION
If you’ve studied Smart Money Concepts (SMC), you’ve likely come across Fair Value Gaps (FVGs). For many, the concept feels overcomplicated. In reality, an FVG is just an imbalance in price — a spot where the market moved so fast that it didn’t fully trade both sides.
🔑When price leaves a gap behind, it often comes back later to “rebalance.” This gives traders powerful zones for entries, exits, and target setting.
🔵 WHAT IS A FAIR VALUE GAP?
A Fair Value Gap is formed over three candles :
Candle 1: The first move (anchor).
Candle 2: The big impulsive candle (the imbalance).
Candle 3: The follow-up candle.
The gap exists when the high of Candle 1 is below the low of Candle 3 (in a bullish case). This leaves an “untraded zone” inside Candle 2.
Think of it as a skipped step. Price rushed through so quickly, there wasn’t enough time to trade at fair value.
🔵 WHY DOES PRICE RETURN TO FVGs?
Markets seek balance. When an imbalance forms, algorithms and institutional flows often revisit the gap to collect liquidity and rebalance orders.
This doesn’t mean every FVG gets filled instantly — some remain open for days or even weeks. But many serve as magnets for price.
🔑Key point: An FVG is not a magic level. It’s a clue about where inefficiency sits.
🔵 HOW TO TRADE FVGS SIMPLY
1️⃣ Mark the Zone
Identify the three-candle imbalance. Highlight the gap inside Candle 2.
2️⃣ Wait for Return
Don’t chase the impulsive candle. Instead, wait for price to retrace into the FVG zone.
3️⃣ Trade the Reaction
Bullish FVG → wait for price to dip into the zone and show bullish reaction
Bearish FVG → wait for price to retest zone and reject downward
Stops are usually placed beyond the gap, targets set toward the next liquidity pool or swing level.
🔵 EXAMPLE SCENARIO
A strong bullish candle leaves an imbalance.
Price continues higher, but a day later revisits the gap.
At bullish rejection candles form with increasing volume.
Entry taken, stop below gap, target at next swing high.
🔵 TIPS FOR ADVANCED TRADERS
Higher timeframe FVGs are stronger and attract price longer.
Not every gap fills — filter with trend direction.
Combine with OBs (Order Blocks) or liquidity zones for more precision.
Ignore small random gaps in low-volume markets.
🔵 CONCLUSION
Fair Value Gaps don’t need to be mysterious. They’re simply imbalances in the auction process. By waiting for price to return and react, traders can build structured entries with defined risk.
🔑Instead of overcomplicating SMC concepts, think of FVGs as footprints of urgency — and opportunities for balance.
Do you already trade FVGs, or is this your first time hearing about them? Share your setups below!
Exploring Supply and Demand in Financial MarketsIn this video, I discuss the concept of supply and demand and its relevance in today’s markets. Price behavior is often shaped by areas where buying and selling pressures are concentrated, and recognizing these dynamics can provide valuable insights into market movement.
📌 Key Highlights
The role of supply and demand in market structure
How institutional activity shapes price zones
Practical examples from recent charts
Why these concepts remain central to market analysis
This video is designed for traders and investors who want a deeper understanding of how markets respond to imbalances between buyers and sellers.
🔖 Hashtags for Reach
#SupplyAndDemand #MarketAnalysis #TradingView #Forex #Investing #FinancialMarkets #PriceAction
What Are Autoregressive Models in Trading?What Are Autoregressive Models in Trading?
Autoregressive (AR) models help traders analyse market movements by identifying statistical relationships in historical price data. These models assume that past values influence current prices, making them useful for spotting trends and price behaviour. This article explores “What is autoregression?”, how AR models function, their role in trading, and how traders apply them to market analysis.
What Is an Autoregressive Model?
Autoregressive (AR) models are statistical tools that can be used in numerous spheres, including market prices, weather, and traffic conditions. They analyse market movements by using past price data to understand current trends. The autoregressive definition refers to a model where each value in a time series depends on previous values plus an error term.
The number of previous values considered is called the “lag order,” denoted as AR(p), where ‘p’ represents the number of lags. In an autoregressive model example, an AR(1) model looks at just the previous value to estimate the current one, while an AR(3) model considers the last three. In trading, the key idea is that if historical prices show a consistent pattern—whether trending or reverting to a mean—an AR model can help identify that structure.
This approach differs from other time series models. Moving averages (MA) smooth out fluctuations by averaging past prices, while autoregressive integrated moving averages (ARIMA) combine both approaches and adjust for trends. AR models, however, focus purely on the statistical relationship between past and present values, making them particularly useful in markets where past behaviour has a clear influence on future movements.
Traders use an autoregressive process to explore trends, momentum, and potential reversals in markets that exhibit persistent patterns. However, their effectiveness depends on market conditions and the assumption that past relationships remain relevant—something that isn’t always guaranteed, especially in volatile or news-driven environments.
How Autoregressive Models Work in Trading
Traders use AR models to examine how past prices influence current movements. An autoregressive model trading strategy often involves assessing whether an asset’s price exhibits momentum or mean reversion tendencies. For example, if an AR(1) model shows that today’s price is strongly influenced by yesterday’s price, it may suggest a continuation bias—meaning traders could expect trends to persist in the short term.
In contrast, if an AR(2) or AR(3) model highlights a tendency for prices to move back toward an average after a few periods, it could indicate mean reversion. This is particularly relevant in range-bound markets where prices frequently return to support and resistance levels.
The number of past values included in an AR model is a key decision. Too few lags might miss relevant patterns, while too many can add unnecessary complexity. Traders typically determine the appropriate lag length by evaluating past data and statistical criteria like the Akaike Information Criterion (AIC).
AR models are more popular in markets where historical relationships hold for extended periods. It’s common to use autoregressive models for trading forex, equities, and commodities, especially in detecting short-term trends or cycles. While they aren’t predictive tools, they provide a structured way to analyse price behaviour, offering traders a statistical foundation for evaluating market movements.
Stationarity and Its Role in AR Models
For an autoregressive time series model to work, the data must be stationary. This means the statistical properties of the time series—such as its mean, variance, and autocorrelation—remain constant over time. If a dataset is non-stationary, meaning its trends, volatility, or relationships shift unpredictably, the AR model's analysis can become unreliable.
Why Stationarity Matters
The autoregressive model, meaning it assumes a consistent statistical structure, can struggle with shifting market conditions if stationarity is not ensured. If a time series is non-stationary, it might show an upward or downward drift, meaning price relationships aren’t consistent over time. This makes it difficult to analyse patterns. For example, a stock experiencing long-term growth won’t have a stable mean, which can distort AR-based analysis.
Testing for Stationarity
Traders often check for stationarity using statistical tests like the Augmented Dickey-Fuller (ADF) test. This test helps determine whether a time series has a unit root—a key characteristic of non-stationary data. If the test suggests a unit root is present, traders may need to adjust the data before using an AR model.
Transforming Data to Stationarity
When data is non-stationary, traders often apply transformations to stabilise it and convert it to an autoregressive model time series. Differencing is a common method, where they subtract the previous value from the current value to remove trends. Log transformations can also reduce the impact of volatility. Once stationarity is achieved, an AR model is believed to be more effective to analyse price movements.
Using an Autoregressive Model in Practice
Understanding how autoregressive models work is one thing—actually applying them in trading is another. These models are primarily used in quantitative strategies, where traders rely on statistical methods rather than gut feelings or news events. While AR models aren’t a complete trading strategy on their own, they can provide valuable insights when used correctly.
Building an AR Model
The first step in using an AR model is preparing the data. Traders typically start with a time series dataset—such as daily closing prices—and ensure it is stationary. If the data shows trends or changing volatility, they may apply differencing or log transformations to stabilise it.
Once the data is ready, the next step is determining the lag order—how many past values should be included in an AR(p) model. This is done through statistical tests like the Akaike Information Criterion (AIC) or Partial Autocorrelation Function (PACF), which help identify how far back price movements remain relevant. For instance, an AR1 model considers only the previous price point, while an AR3 model incorporates the last three observations. Choosing too few lags might miss important relationships, while too many can overcomplicate the model.
After selecting the lag order, traders fit the AR model using statistical software such as Python’s statsmodels or R’s forecast package. The model estimates how past prices influence current ones, producing a set of coefficients that define these relationships. The trader then analyses these results to determine if the model aligns with market behaviour.
Applying AR Models to Trading
Once built, an AR model provides insights into how past price behaviour influences future movement. For example:
- If an AR(1) model shows a strong positive coefficient, it suggests that today’s price is closely linked to yesterday’s, reinforcing a short-term trend.
- If an AR(2) or AR(3) model suggests a return toward a long-term mean, it may indicate a market where price cycles are present.
Traders use these insights in different ways. Some apply AR models to analyse short-term market momentum, while others use them to examine mean-reverting assets like certain forex pairs or commodities. They can also compare AR-based analysis with other indicators like moving averages or Bollinger Bands to refine their decision-making process.
Autoregressive models are also used in machine learning for time series forecasting, helping algorithms detect patterns in sequential data. In trading, autoregressive model machine learning techniques can refine models by dynamically adjusting lag parameters, improving adaptability to changing market conditions and reducing reliance on fixed assumptions.
ARIMA: Extending AR Models
While AR models work well on stationary data, many financial time series contain trends or seasonality that a basic AR model can’t handle. This is a scenario where Autoregressive Integrated Moving Average (ARIMA) models become useful. ARIMA combines AR components with moving averages (MA) and differencing (I for “integrated”) to account for non-stationary behaviour.
For example, if a stock price has an upward drift, an AR model alone won’t be sufficient. An ARIMA model can first remove the trend through differencing, and then apply AR and MA components to analyse underlying patterns. This makes ARIMA more flexible for complex market environments.
Challenges and Considerations When Using AR Models
Autoregressive models can be useful for analysing price movements, but they come with limitations that traders should consider. Financial markets are complex, and historical price patterns don’t always repeat in the same way. Understanding where AR models fall short might help traders apply them more effectively.
Overfitting and Choosing the Right Lag Order
One of the biggest challenges in using AR models is selecting the right lag order. Including too many past values can lead to overfitting, where the model becomes overly sensitive to historical fluctuations that may not be relevant going forward. Overfitting can create misleading analysis, making the model seem accurate in hindsight but ineffective in real-time market conditions. Traders typically balance complexity with statistical tests like the Akaike Information Criterion (AIC) to determine an optimal lag length.
Market Noise and Unexpected Events
AR forecasting assumes that past price relationships remain relatively consistent. However, financial markets are influenced by a wide range of external factors—economic reports, central bank decisions, and geopolitical events—that models based purely on past prices cannot account for. A market that has historically followed a trend can abruptly reverse due to news or institutional flows, reducing the usefulness of AR-based analysis.
Data Quality and Stationarity
The reliability of an AR model depends on the quality of the data used. Non-stationary data, sudden regime changes, or structural shifts in the market can distort results. Traders often need to check for stationarity and adjust their approach when market conditions change, ensuring that their models remain relevant rather than assuming past relationships always hold.
The Bottom Line
Autoregressive models offer traders a statistical approach to analysing price movements, helping them identify trends and market behaviour based on historical data. While they are not standalone trading signals, they can be valuable when combined with other analytical tools.
FAQ
What Is an Autoregressive Model?
An autoregressive (AR) model is a type of statistical model that analyses time series data by expressing a variable as a function of its past values. It assumes that past observations influence current values, making it useful for identifying patterns in sequential data.
What Is an Autoregressive Model in Finance?
In finance, AR models are used to analyse price movements by examining historical data. Traders apply them to identify trends, momentum, or mean-reverting behaviour in assets like stocks, forex, and commodities. AR models help quantify how past price changes relate to current movements.
What Is an Autoregressive Model for Stock Analysis?
AR models in stock analysis assess price patterns by using historical data to determine potential relationships between past and present values. They can highlight statistical trends but do not account for external market drivers like news or economic events.
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from Rice to Robots, evolution of TA The History and Origin of Technical Analysis
Every chart we study today. Every candlestick, moving average, or RSI indicator is built on centuries of market wisdom. While many believe technical analysis began with Charles Dow in the 1800s, its origins reach much further back, to Amsterdam’s bustling spice markets in the 1600s and Japan’s rice exchanges in the 1700s.
Let’s take a journey through time and see how technical analysis evolved into the powerful tool traders and investors use today.
17th Century: The First Signs of Charting
1. Dutch East India Company Traders (1602)
The Dutch East India Company, established in Amsterdam in 1602, became the first publicly traded company. Its shares were bought and sold on the world’s first stock exchange, the Amsterdam Stock Exchange. Early traders began tracking price fluctuations in simple graphical forms — the very first steps toward technical analysis.
2. Joseph de la Vega (1650–1692)
A Spanish diamond merchant and philosopher, Joseph de la Vega, authored Confusión de Confusiones (1688), the earliest known book on stock markets. He described investor behavior, speculative patterns, and even outlined concepts resembling modern puts, calls, and pools. His insights captured both the psychology of markets and the primitive beginnings of technical analysis.
18th Century: Japan’s Candlestick Revolution
Homma Munehisa (1724–1803)
In Osaka’s Dōjima Rice Exchange, Japanese rice merchant Homma Munehisa created what remains one of the most widely used charting methods in history: the Japanese Candlestick (then called Sakata Charts).
His book The Fountain of Gold – The Three Monkey Record of Money detailed not only price charts but also market psychology, emotions, and crowd behavior. Today, candlestick patterns remain a cornerstone of technical analysis worldwide.
Late 19th & Early 20th Century: The Modern Foundations
Charles Dow (1851–1902)
Often called the father of modern technical analysis, Charles Dow co-founded Dow Jones & Company and The Wall Street Journal in 1889. His market observations led to:
The Dow Jones Industrial Average and Transportation Average
The Dow Theory, which identified three types of trends: primary, secondary, and minor.
Dow believed markets reflect the overall health of the economy, and his work inspired generations of analysts, including William Hamilton, Robert Rhea, George Schaefer, and Richard Russell.
Ralph Nelson Elliott (1871–1948)
Building on Dow’s ideas, Elliott studied 75 years of stock market data and developed the Elliott Wave Theory, arguing that markets move in recurring wave patterns driven by crowd psychology. In March 1935, he famously predicted a market bottom and the Dow Jones indeed hit its lowest point the following day, cementing his theory’s credibility.
20th Century: The Rise of Indicators
The computer era supercharged technical analysis. Mathematically driven technical indicators were developed to analyze price, volume, and momentum on a scale that manual charting could never achieve.
Example: RSI (Relative Strength Index)
Developed by J. Welles Wilder Jr. in 1978, RSI measures the speed and magnitude of price changes on a scale of 0–100.
Above 70 = Overbought (potential sell signal)
Below 30 = Oversold (potential buy signal)
Other popular indicators soon followed, such as Moving Averages, MACD, and Bollinger Bands, giving traders an expanding toolbox to forecast market movements.
21st Century: From Charts to Algorithms and AI
Today, technical analysis has evolved far beyond hand-drawn charts:
Algorithmic Trading: Automated systems use indicators and strategies to execute trades at lightning speed.
AI Trading Bots: Artificial intelligence combines both technical and fundamental analysis, processing massive datasets to generate signals and even execute trades.
Platforms like TradingView: Empower traders worldwide to build custom indicators, test strategies and share insights, democratizing access to advanced market tools.
nerdy thoughts
From Amsterdam’s first stock traders to Osaka’s candlestick pioneers, from Charles Dow’s theories to AI-powered trading bots, technical analysis has always been about one thing: decoding price to understand human behavior in markets.
It’s a discipline born from centuries of observation, innovation, and adaptation, one that continues to evolve every day.
“Life is a moving, breathing thing. We have to be willing to constantly evolve. Perfection is constant transformation.”
put together by: Pako Phutietsile ( @currencynerd )
courtesy of : @TradingView
this is inspired by a publication i once posted this is the revamped edition...
Why Markets Never Move in a Straight LineHello,
Financial markets, by their very nature, do not move in a straight line. Prices fluctuate, trends develop, and corrections occur along the way. While it is tempting to expect that an upward rally will continue indefinitely, the reality is that markets require pauses and pullbacks to remain healthy. As shown in the chart above markets will always pull back (taking breathers as they move up).
One of the primary reasons markets correct is profit-taking. Early investors, who entered positions at lower prices, often choose to lock in gains once prices rise to attractive levels. Their selling creates temporary downward pressure, leading to corrections. This cycle of entry, accumulation, and profit-taking is not a sign of weakness, but rather a natural rhythm of market activity.
Corrections also serve a vital purpose: they prevent markets from overheating. Extended rallies without pauses often create unsustainable valuations, increasing the risk of a sharp reversal. By allowing prices to retrace, corrections provide opportunities for new investors to enter at fairer levels and for existing investors to add to their positions more strategically.
History consistently shows that long-term market growth is built on a series of advances punctuated by corrections. Even in strong bull markets, prices rarely move in a linear fashion. Instead, they climb higher through a stair-step pattern—rising, correcting, consolidating, and then resuming their upward momentum.
For investors, this means corrections should not always be viewed with fear. Instead, they can be seen as opportunities. As Warren Buffett often reminds us, the key is not to follow the crowd into overbought territory but to wait patiently for value.
Recognizing that they cannot move in a straight line equips investors with patience and perspective—two of the most valuable traits in successful investing.
Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
Double Bottom followed by Higher LowTom Bulkowski analysed 1000s,s of chart patterns. The double bottom pattern showed that many of the patterns performed 50% of the time.
However, the double bottom defined by 2 valleys holding on a parallel support level followed by a Higher low confirms the trend shifting from bearish to Bullish. This pattern occurs frequently at the bottom of a downtrend, and Bulkowski found the % of failure rate of this pattern to be very low. He ranked this pattern a 3 on the scale of powerful trading tools.
1,064-Day Crypto Cycle coming.. Oct 06 2025Are We Nearing a Macro Turning Point?
Markets may look chaotic on the surface, but zoom out far enough and a rhythm begins to emerge. For Bitcoin and the broader crypto market, one of the most compelling patterns traders track is the 1,064-day cycle, a rough cadence of boom and bust that has repeated across multiple market eras.
With October 2025 approaching, many analysts are asking: Is another turning point on the horizon?
Why 1,064 Days?
The number isn’t arbitrary. Crypto markets, especially Bitcoin, have displayed a recurring rhythm tied loosely to halvings, liquidity cycles, and investor psychology. Roughly every 1,064 days (about 2.9 years), Bitcoin seems to align with a macro peak or trough.
Cycle 1 (2011–2014): BTC surged from a few dollars to over $1,000 before collapsing in late 2013.
Cycle 2 (2014–2017): The next expansion drove prices to $20,000 by December 2017 — almost exactly 1,064 days later.
Cycle 3 (2018–2021): From the 2018 bear bottom, Bitcoin reached $69,000 in November 2021 — again within the 1,064-day window.
The cycle doesn’t work like clockwork, but the cadence is eerily consistent, suggesting that investor flows, halvings, and liquidity injections may move in long, repeating arcs.
Mapping Today’s Position
If we anchor the most recent cycle to the November 2021 peak, the 1,064-day marker points us toward October 2025.
This timeline aligns uncomfortably well with two forces:
Halving Lag Effect – Historically, the real bull accelerations occur 12–18 months after a halving event (the next one being April 2024). That would put late 2025 squarely in the “froth” zone.
Liquidity Rotation – Global central banks are currently balancing inflation with growth concerns. By late 2025, markets may expect easing, a perfect storm for risk-on assets like crypto.
What the Charts Suggest?
Looking at long-term Bitcoin charts, cycle expansions follow a similar arc:
A steep bull phase fueled by retail and institutional adoption.
A distribution top marked by extreme leverage, retail euphoria, and inflows into speculative altcoins.
A macro correction that wipes out 70–85% of value before a new base forms.
If history rhymes, the 2025 cycle top could be the most significant yet, not just in terms of price, but in market maturity. Institutional ETFs, regulatory frameworks, and global adoption add layers of credibility that were absent in past cycles.
Why Traders Should Care
Cycle mapping is not about prediction with surgical precision, it’s about framing risk and opportunity.
For long-term investors: Understanding that late 2025 could coincide with a major top helps avoid FOMO and plan exits with discipline.
For swing traders: These cycles offer context for positioning. Bull legs tend to accelerate in the 6–12 months before the cycle peak.
For macro thinkers: If crypto follows this cycle, it could front-run global liquidity shifts, making it a leading indicator for risk appetite.
nerdy thoughts : The Clock Is Ticking
The 1,064-day cycle isn’t prophecy. But its consistency across three full eras of crypto history makes it hard to dismiss. As October 2025 approaches, traders would do well to watch for echoes of past patterns: accelerating inflows, leverage buildup, and sentiment peaking.
Because in crypto, time doesn’t just pass, it compounds into cycles. And those cycles often whisper what comes next.
put together by: @currencynerd
courtesy of : @TradingView
Ultimate Guide to Master: Rejection BlocksRejection Blocks (ICT Concept) – Complete Guide
1. What is a Rejection Block?
A rejection block is a special type of price level that forms when the market attempts to push through but gets denied and reverses. Unlike a traditional order block, which represents accumulation or distribution by institutions, a rejection block shows a failed attempt to continue in one direction. It is a footprint of rejection and often becomes a strong reaction zone in the future.
There are two types:
Bullish Rejection Block:
Forms from a bearish candle whose low is taken out, but price fails to continue lower and closes back above. The low of that candle becomes the key level.
Bearish Rejection Block:
Forms from a bullish candle whose high is breached, but price fails to continue higher and closes back inside. The high of that candle becomes the key level.
These levels can act as hidden support or resistance and often serve as high-probability entry points when combined with market structure.
2. How to Spot a Valid Rejection Block
To correctly identify rejection blocks, you need to look for:
1. Clear Attempt Beyond a Candle
Price must trade beyond the high or low of a prior candle, suggesting continuation.
2. Failure and Return
After breaching the level, price fails and closes back inside the candle’s body.
3. Liquidity Context
A rejection block is more powerful if the wick that caused it swept liquidity (equal highs/lows or a previous key level).
4. Higher Timeframe Confluence
The best rejection blocks line up with higher timeframe bias (for example, spotting a bearish rejection block inside a 4H premium zone during a downtrend).
3. How to Trade Rejection Blocks
Trading them involves waiting for price to come back to the rejection block level and using it as an entry or reaction zone.
Bullish Setup:
When price trades below a bearish candle, fails, and closes higher, mark the low of that candle. On a retracement, price often retests that level as support.
Bearish Setup:
When price trades above a bullish candle, fails, and closes lower, mark the high of that candle. On a retracement, price often retests that level as resistance.
Entry Technique:
You can enter "blindly" when you're understanding the confluences. But to begin with do this Instead, when price returns to the rejection block, drop to a lower timeframe and look for confirmation such as:
* Fair Value Gap (FVG) entries.
* Market Structure Shift (MSS).
* Liquidity sweeps into the level.
Stop Loss Placement:
Always place stops beyond the rejection candle itself (above the high for bearish RB, below the low for bullish RB).
4. Practical Examples and Market Context
Rejection blocks work best when they appear in the following situations:
Liquidity Sweeps:
After equal highs or equal lows are taken out, a rejection block often marks the failure point.
Inside Premium/Discount Zones:
In a bearish bias, look for bearish RBs in premium pricing. In a bullish bias, look for bullish RBs in discount pricing.
During Consolidation Breakouts:
If the market fakes a breakout and closes back inside, the rejection block often becomes the level to fade the fake move.
For example, if BTC takes out a prior daily high, prints a rejection block, and then closes back inside, the odds of reversal are high, especially if price was already in premium territory.
5. Combining Rejection Blocks with ICT Concepts
To increase accuracy, always combine RBs with ICT’s other tools:
Fair Value Gaps:
If a rejection block aligns with an FVG, it adds strength to the level.
Market Structure Shifts:
A rejection block is more powerful if followed by displacement and an MSS.
CISD Pattern:
A rejection block often forms right after the “Stop Hunt” part of the CISD sequence, serving as a clean entry.
Liquidity Pools:
Look for RBs near equal highs/lows, old highs/lows, or session liquidity (London/New York).
Conclusion
Rejection blocks are subtle but highly effective levels that show where the market tried to extend but failed, leaving behind a hidden form of support or resistance. By themselves they are useful, but when combined with ICT concepts like liquidity sweeps, MSS, and FVGs, they become powerful entry tools. The key is to always wait for price to return and confirm the level before entering, and to only trade them in alignment with higher timeframe bias.
Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
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