Sequence of Analysis

1. Let the market stretch
2. Support / Resistance
3. Price Actions
4. MACD / Stochastic
5. Overbought / oversold - two long candle (hourly / 4H / Daily

Saturday, October 5, 2024

Beyond Manipulation: Understanding When Banks Obey the Market

Banks and their AI-driven trading systems are highly sophisticated, but there are certain situations where they cannot manipulate the market and have to follow or “obey” market conditions due to the sheer scale and external forces involved. Here are a few such situations:

### 1. **Major Geopolitical Events**
   Geopolitical events like wars, natural disasters, or major political upheavals (e.g., Brexit or US-China trade tensions) create volatility that is too unpredictable and widespread for banks to control. In these instances, the sheer volume of market participants reacting in real time overwhelms any manipulation attempts. The AI-driven algorithms would instead focus on reacting to and adjusting strategies based on market movements rather than trying to steer them.

   **Example**: During the Brexit vote, the pound’s price plummeted in ways that were beyond any institution's ability to control or manipulate because of the enormity and unpredictability of the event.

### 2. **Central Bank Interventions**
   When central banks intervene in the market (through **interest rate changes**, **quantitative easing**, or direct currency interventions), these actions influence the forex market in a manner that cannot be counteracted or manipulated by individual banks. AI systems at banks must adapt to these moves, not fight against them.

   **Example**: In 2015, the Swiss National Bank (SNB) removed its currency peg with the euro, causing a massive and sudden appreciation of the Swiss franc. This type of central bank intervention was too large and abrupt for banks to manipulate, forcing them to react rather than control.

### 3. **Market Sentiment and Mass Retail Activity**
   At times, overwhelming retail trader sentiment or institutional investor movements can be too large to manipulate. For example, during times of **mass retail trading frenzy** (e.g., social media-driven movements like the GameStop/Reddit trading events in 2021), markets can move in unpredictable directions, and banks are forced to adapt rather than try to steer the entire market.

   **Example**: A large-scale retail sentiment shift might occur if retail traders flood into the market during a specific news event, changing price action faster than banks can anticipate or manipulate.

### 4. **High-Liquidity Markets and Major Currency Pairs**
   In **highly liquid markets**, such as the USD/EUR or USD/JPY pairs, it becomes difficult for any single entity, even large banks, to manipulate price movements. The volume of transactions and diversity of participants means that the market is too large to influence significantly.

   **Example**: Even the biggest players cannot easily manipulate currency pairs like the EUR/USD because of the volume of trade and the number of participants from around the world, from retail traders to hedge funds and governments.

### 5. **Global Financial Crises**
   During systemic financial crises, such as the 2008 financial meltdown, the market becomes so volatile and driven by fear that no single entity can control the market's direction. Banks, along with other institutions, are forced to adapt to chaotic conditions rather than exert influence.

   **Example**: In 2008, the collapse of Lehman Brothers and the ensuing financial crisis caused massive, uncontrollable swings in currencies and other assets. The AI systems in banks would have focused on damage control and mitigating losses rather than manipulating the market.

### 6. **Regulatory and Legal Constraints**
   Banks operate under **regulatory frameworks** that prevent certain types of market manipulation. Governments and regulatory bodies monitor large-scale trading activities, and if banks are found to engage in illegal practices like market manipulation or collusion, they face significant fines or legal action. AI systems must work within these constraints, meaning they cannot manipulate markets in ways that violate regulations.

   **Example**: Many banks have faced lawsuits and regulatory penalties for attempting to manipulate currency benchmarks like LIBOR or forex rates. These fines and legal actions discourage future attempts at direct manipulation.

### 7. **Liquidity Crises or Lack of Counterparties**
   In certain situations, especially during liquidity crunches, there may not be enough counterparties to execute trades. In these cases, even large banks have to wait for suitable liquidity or market conditions to execute their strategies. Trying to manipulate prices during a liquidity crisis could backfire, as thin markets can lead to outsized price moves that even the banks cannot control.

   **Example**: During sudden liquidity shortages, price gaps can occur, and banks may have to hold off trading or be forced to take trades at suboptimal prices. In these situations, manipulation becomes impractical or risky.

---

### Conclusion:
Banks and their AI systems, while powerful, cannot always manipulate the market. They must respect certain conditions, including macroeconomic forces, central bank actions, high liquidity markets, and regulatory environments. In these scenarios, even the most sophisticated trading algorithms have to adapt to the market rather than trying to control it.

Behind the Curtains: How Banks Outmaneuver Retail Traders in Forex Markets

Banks and large financial institutions study the behavior of technical indicators in ways that can create advantages over individual retail traders. Here's how they do it and how they can exploit market dynamics:

### 1. **Advanced Algorithms & High-Frequency Trading (HFT)**
   Banks have access to sophisticated algorithms that scan markets in real-time using technical indicators such as moving averages, MACD, and RSI. Their high-frequency trading (HFT) systems can execute trades in milliseconds, capitalizing on minute market movements. These systems analyze market patterns, including retail traders’ positions and stop-loss levels, and place massive trades that may trigger stop losses or force retail traders to liquidate positions.

   **Example:**
   If a large number of traders are placing stop-loss orders at a specific support level identified by technical analysis, banks may use large orders to push the price below that level, triggering those stop losses and creating a cascade of selling (or buying).

### 2. **Order Flow Data**
   Large institutions can access **order flow data**, which gives them insight into where retail traders have placed their orders. They can see patterns in buy/sell orders, stop losses, and limit orders. Using this data, banks can manipulate prices around key technical indicators like support and resistance, creating false breakouts or breakdowns to capture liquidity.

   **Example:**
   If a currency pair is approaching a major resistance level, banks might temporarily push the price higher (a false breakout), triggering retail buy orders, only to reverse the market and cause retail traders to lose out.

### 3. **Market Liquidity**
   Banks act as market makers, providing liquidity to the markets. They can manipulate prices in thinly traded sessions, causing volatility that disrupts technical indicator signals. For example, banks might move the market through price zones where many retail traders have placed their stop-loss or take-profit orders, known as **stop hunting**.

   **Example:**
   During times of lower liquidity (e.g., after a major news event), banks may trigger both buy and sell stops around a certain price level, exploiting retail traders who are following technical signals without considering underlying liquidity.

### 4. **Exploiting Emotional Reactions**
   Retail traders often rely heavily on widely known technical indicators such as the **Relative Strength Index (RSI)**, **Bollinger Bands**, and **Fibonacci retracements**. Banks know that retail traders tend to behave predictably when these indicators show overbought or oversold conditions. Banks can use this knowledge to create **fake signals** or **whipsaws**, which mislead retail traders.

   **Example:**
   If RSI shows overbought conditions, many retail traders might expect a price drop. Banks might push the price even higher temporarily, trapping retail traders who shorted too early, forcing them to close positions at a loss.

### 5. **Using Long-Term Trends Against Short-Term Traders**
   Banks focus on both **long-term trends** and **fundamental data**. While retail traders might base decisions on short-term technical patterns, banks analyze the macroeconomic environment and geopolitical events. This allows them to anticipate longer-term moves that contradict short-term technical signals, making it difficult for individual traders to trade profitably on brief patterns.

   **Example:**
   While a technical indicator like the **MACD** may suggest a buy signal in the short term, the bank could take the opposite position if long-term macroeconomic data suggests that the currency pair will depreciate over the coming weeks or months.

### 6. **Creating Liquidity Traps**
   Banks may engage in strategies like **liquidity hunting** by artificially creating or breaking support and resistance levels to trigger a rush of buy or sell orders from retail traders. Once retail positions are trapped, banks can reverse the price movement, leaving retail traders exposed.

   **Example:**
   Banks may engineer a scenario where a strong support line is broken, enticing retail traders to enter a sell position, only for the price to rebound sharply, trapping those traders.

### 7. **Geopolitical and News Event Manipulation**
   Banks can predict market reactions to key **geopolitical events** and news releases more accurately than retail traders. They prepare ahead of these events and capitalize on exaggerated market movements that technical indicators may not immediately reflect. Retail traders, reacting to technical indicators, might make trades that banks exploit after an event by understanding the broader context.

---

In conclusion, banks leverage technical indicators, order flow, liquidity data, and fundamental factors to stay ahead of the market and exploit opportunities. Retail traders, who often rely on predictable technical patterns, are at a disadvantage because banks can manipulate prices, anticipate retail behavior, and absorb short-term volatility to capture profits. Understanding how large institutions operate can help traders avoid common pitfalls and develop more robust trading strategies.

Saturday, February 3, 2024

None of the Technical Indicator's strategy will work permanently as it is.

There are none of the technical indicator's strategy will work permanently - no matter what! Therefore, the number one rule still depends on one's intuition based on the technical indicators signal.

1. Combination of MACD and Full Stochastic has the best technical result 
2. Addition with candlestick patterns and chart patterns it make better result
3. Yet intuition is much greater.

Remember that the forex trading platform knows their clients' buy and sell positions - If they work together to harvest those data they know exactly the volume of buy and sell positions which one to wipe out first. In their process of doing this - we can actually take advantage of the tricks.

Saturday, March 6, 2021

Gold XAUUSD Chart Pattern Analysis

Here are similar historical data that happens in the 26- to 01 December 2020

Let's if there is truth in chart patterns of the Gold XAUUSD - and how it behaves in different situation of the MACD indicator in different time frame
Updated - it goes up as expected
Updated 03-Mar-2021 - So history prove itself right once again the Gold wen't up just exactly like historical of similar patterns. Now let's see on the 4H time frame if history is right once again to break the resistance.
Updated 03-Mar-2021 - So it didn't break the resistance and quickly reverse (History goes differently for how much it should go up)
Updated 09-Mar-2021 - It goes further deeper but reverses now the fight change to daily time frame
Updated 10-Mar-2021 - The unexpected upside has occur after it went deeper now it suddenly goes up and break the resistance that should be broken earlier based on historical data. The patterns of fluctuation seems to play on slighly different landscape.
Updated 11-Mar-2021
Updated 11-Mar-2021 - it went up hit the resistance on the daily time frame and quickly reverse
Updated 12-Mar-2021 - The reversal went deep
Updated 13-Mar-2021 - Quick reversal (This is game of support / resistance (not price action or MACD anymore)
Updated 15-Mar-2021 familiar formation
Updated 18-Mar-2021 Painful gradual upside with high active fluctuations - very small scale movement and dangerous. So the prediction was right just that it looks painful and high emotional play
Updated 22-Mar-2021
Updated 23-Mar-2021
Updated 01-April-2021
Updated 02-April-2021 -- So it keeps moving up like BCOUSD historical data and reverse when there is big leap in price.
Updated 05-Apr-2021
Updated 07-Apr-2021
Updated 09-Apr-2021
Updated 13-Apr-2021 --- A different style of trading this time. Based on Ups and Downs without relying too much on indicators
Update 15-Apr-2021
Updated 15-Apr-2021
Updated 20-Apr-2021
Updated 24-Apr-2021
Updated 28-Apr-2021
Updated 28-Apr-2021
Updated 06-May-2021
Updated 11-May-2021
Updated 17-May-2021
Updated 20-May-2021
Updated 21-May-2021
Updated 24-May-2021
Updated 03-Jun-2021 Note: Monthly MACD Down
Updated 04-Jun-2021 Note: Monthly MACD Down
Updated 15-Jun-2021 Note: Monthly MACD down
Updated 18-Jun-2021 Note: Monthly MACD down

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