Category: DAILY

DAILY

DGM WSOT 2024 Has Ended With Badges

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Oct 1, 2024, 10AM UTC โ€“ Oct 31, 2024, 10AM UTC

DGM in Bybit WSOT 2024

Your WSOT (World Series of Trading) stats showcase an impressive individual trading record within the Squad Showdown category:

  • Trading Volume Ranking: You’re ranked at 34,391 out of a substantial pool of participants, positioning you in the upper half of competitors.
  • Trading Volume: Youโ€™ve amassed a trading volume of $31,634 USD. This demonstrates both your activity and commitment in the competition, with a significant volume relative to other traders.

This WSOT event by Bybit is known for its high-stakes trading competitions, where thousands of traders globally compete. Your ranking and volume reflect your engagement and strategic trading efforts in this fiercely competitive environment. It’s also worth noting that the event promises a substantial prize pool of $10,000,000, making it an attractive and highly competitive platform for skilled traders like yourself.

You achieved a solid profit of $295, placing you in the top tier of participants with a ranking of 2,400 out of a competitive field of 70,000 traders. This accomplishment highlights your skill in navigating the market and securing gains, positioning you in the upper 3.5% of all competitors. Your strategy is clearly effective and demonstrates a strong understanding of trading dynamics within a large participant pool.

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Incredible Drone Display is Worldโ€™s Biggest Ever – Guinness World Records

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https://youtu.be/LpaSXwpKzGk

Congratulations to Zhujian Planning Media (Jilin) Co., Ltd. for breaking the record for the largest image formed by multirotor/drones. Can you guess how many drones were used? At Guinness World Records we want to show that everyone in the world is the best at something, and weโ€™re here to measure it! Whether youโ€™ve got the stretchiest skin, know the worldโ€™s smallest dog or want to create the largest human dominoes chain we want to hear about it. Here on the Guinness World Records YouTube channel we want to showcase incredible talent. If you’re looking for videos featuring the world’s tallest, shortest, fastest, longest, oldest and most incredible things on the planet, you’re in the right place.

ใ€ๆทฑๅœณใ€‘ๅยทไธ€ ็„กไบบๆฉŸ่กจๆผ”้›†้Œฆใ€‚่—่ก“ไธŽ็ง‘ๆŠ€็š„ๅฎŒ็พŽ็ตๅˆ๏ผŒไธ€ๅ ดๅ…‰ๅฝฑ็››ๅฎด๏ผ
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Solana (SOL) and AR Development Currently

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Solana Trading Approach Update

The latest trading approaches for Solana (SOL) in October 2024 highlight its strong price momentum and positive outlook, driven by technical and fundamental factors.

  1. Price Momentum: Solana is currently trading around $144, maintaining a solid upward trajectory with strong support levels at $140. The 50-day EMA is at $144.48, suggesting that a breakout above this level could lead to further bullish movement towards $160 and potentially $200 by the end of October. The Relative Strength Index (RSI) indicates neutral momentum, providing room for growth if buyer interest increases.
  2. Technical Patterns: A symmetrical triangle formation on both daily and weekly charts suggests a potential breakout. If Solana can surpass the $155 resistance level, it is likely to target $180 and possibly $200, making this a critical price zone to watch. Conversely, failure to break above this could lead to a retracement toward $120.
  3. Long-Term Outlook: Analysts are optimistic about Solana’s continued upward trend, supported by its strong technical foundations. Extended Fibonacci retracement levels show key price targets, with the possibility of reaching aspirational levels like $213 or even higher, depending on market conditionsโ€‹.

For traders, monitoring the breakout points and volume surges could provide good entry points for both short-term and long-term strategies. Keep an eye on resistance levels and support from indicators like EMAs and RSI for confirmation of the next significant move.

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๐ŸฅœNever eat PEANUTS with this ๐Ÿฅœ you will get CANCER and MEMORY LOSS!

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How Aflatoxins (้ป„ๆ›ฒ้œ‰็ด ) Cause Liver Cancer?

๐ŸฅœNever eat PEANUTS with this ๐Ÿฅœ you will get CANCER and MEMORY LOSS!

Aflatoxins are a group of toxins produced by certain molds, primarily Aspergillus flavus and Aspergillus parasiticus, which grow in soil, decaying vegetation, grains, and nuts. These toxins are known to contaminate food supplies, particularly in warm and humid regions. Aflatoxin exposure is a major health concern because of its potent carcinogenic effects, particularly related to liver cancer.

How Aflatoxins Cause Liver Cancer:

  1. Liver Damage and Carcinogenesis:
    • When aflatoxins are ingested, the liver metabolizes them into reactive compounds, including aflatoxin B1 (AFB1), which is the most toxic.
    • Aflatoxin B1 gets converted into a reactive form by enzymes in the liver. This form can bind to DNA, causing mutations in the p53 tumor suppressor gene. This leads to uncontrolled cell growth and the development of hepatocellular carcinoma (HCC), a common form of liver cancer.
  2. Synergy with Hepatitis:
    • The risk of liver cancer is significantly higher when aflatoxin exposure is combined with chronic hepatitis B (HBV) or C (HCV) infection. Aflatoxin-induced DNA damage, coupled with viral infection, accelerates cancer development.
  3. Foods at Risk:
    • Foods like maize (corn), peanuts, tree nuts, and grains are particularly susceptible to aflatoxin contamination, especially in regions where storage practices are poor, leading to mold growth.

Prevention:

  • Regulation and Monitoring: Many countries regulate aflatoxin levels in food and animal feed to reduce the risk of contamination.
  • Improved Storage: Keeping crops dry and storing them in proper conditions can help prevent aflatoxin-producing molds from growing.
  • Detoxification Methods: Some efforts are being made to develop food treatments to detoxify aflatoxins, but these are not widely available.

Long-term exposure to high levels of aflatoxin is strongly associated with liver cancer, particularly in areas with high rates of aflatoxin contamination and hepatitis B infection.

ๆ˜“็”Ÿ้ปƒ้บดๆฏ’็ด 
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Liquidation data can be highly informative

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I Decoded The Liquidity & Manipulation Algorithm In Day Trading
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Liquidation data can be highly informative when combined with metrics like volume, open interest, or funding rate. These three indicators help provide a clearer picture of market behavior and trader sentiment. Here’s how each works with liquidation data:

1. Volume and Liquidation:

  • Volume represents the total number of contracts or shares traded during a specific period.
  • Liquidations combined with high volume can signal a significant market move, as many traders are being forced out of their positions. For example, a spike in long liquidations combined with high selling volume often indicates a strong downward price movement, and vice versa for short liquidations.
  • Key Insight: When liquidations happen in high-volume conditions, it suggests strong conviction in the direction of the move.

2. Open Interest and Liquidation:

  • Open Interest (OI) represents the total number of outstanding contracts (positions) in a market.
  • Decreases in OI after large liquidations suggest positions are being closed, potentially marking the end of a strong move (e.g., a final shakeout).
  • Increases in OI with liquidations may indicate that new positions are being opened, often by more aggressive participants looking to capitalize on the forced liquidations of others.
  • Key Insight: A combination of high liquidations and falling OI could signal the exhaustion of a trend.

3. Funding Rate and Liquidation:

  • Funding Rate incentivizes traders to take long or short positions based on market imbalance.
  • Liquidations in the context of extreme funding rates are often a precursor to reversals. For example, if funding rates are extremely positive (indicating longs are paying shorts), and then a large number of long liquidations occur, it might signal that overly leveraged longs are being forced to exit, potentially triggering a downward move.
  • Key Insight: Funding rates help identify the positioning of the majority of traders. If liquidations occur with extremely skewed funding rates, it often means the market was over-leveraged in one direction, which could result in a price correction.

Summary of How They Work Together:

  • Volume + Liquidations: High liquidations with high volume = strong price moves with conviction.
  • Open Interest + Liquidations: High liquidations with falling OI = potential trend exhaustion; with rising OI = fresh participants entering.
  • Funding Rate + Liquidations: High liquidations with extreme funding rates = potential market reversal due to overly leveraged positions being wiped out.

Each of these indicators adds valuable context to liquidation data, and using them together can help you make more informed trading decisions.

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How to use Funding Rate in Algo Trading?

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How to use Funding Rate in Algo Trading?
Alt Coin 8 Hours Funding Rate Chart

1. Mean Reversion Strategy

  • How it works: The funding rate can indicate market sentiment and extreme positions. If funding rates are very high or low, it suggests that one side of the market (long or short) is overleveraged. A mean reversion strategy involves entering positions expecting the funding rate to revert to more normal levels.
  • Goal: Bet against extreme funding rates, profiting from their eventual reversion as market sentiment stabilizes.

2. Market Sentiment Analysis

  • How it works: Funding rates can be used as a proxy for sentiment. High positive funding rates indicate bullish sentiment, while negative rates indicate bearish sentiment. Algorithms can use this information to adjust their trading strategies in line with prevailing market trends or take contrarian positions.
  • Goal: Capitalize on market momentum or sentiment shifts based on funding rate extremes.

3. Directional Trading Strategy

Goal: Align trades with the prevailing market direction as indicated by the funding rate or use it to enter contrarian positions when rates are extreme.

How it works: If the funding rate is positive and increasing, it suggests that long positions are dominating the market, while a negative and decreasing rate suggests shorts are prevailing. This information can be used in trend-following strategies.

import pandas as pd
import numpy as np
import random

Simulate funding rate data (Replace this with actual API data)

def simulate_funding_rate_data(n=100):
dates = pd.date_range(end=pd.Timestamp.now(), periods=n, freq=’H’)
funding_rates = np.random.normal(0, 0.01, n) # Random funding rates between -1% to +1%
return pd.DataFrame({‘date’: dates, ‘funding_rate’: funding_rates})

Generate funding rate data

df = simulate_funding_rate_data()

Define trading strategies

class FundingRateStrategy:

def __init__(self, funding_rate_data, upper_threshold=0.005, lower_threshold=-0.005):
    self.funding_rate_data = funding_rate_data
    self.upper_threshold = upper_threshold
    self.lower_threshold = lower_threshold

# 1. Mean Reversion Strategy
def mean_reversion_strategy(self):
    """
    Mean reversion based on extreme funding rates. Enter contrarian positions when funding rates
    are too high (short) or too low (long).
    """
    signals = []
    for index, row in self.funding_rate_data.iterrows():
        if row['funding_rate'] > self.upper_threshold:
            signals.append('Sell')  # High funding rate -> Overleveraged long positions -> Short
        elif row['funding_rate'] < self.lower_threshold:
            signals.append('Buy')   # Low funding rate -> Overleveraged short positions -> Long
        else:
            signals.append('Hold')  # No trade signal
    self.funding_rate_data['mean_reversion_signal'] = signals

# 2. Market Sentiment Analysis
def market_sentiment_analysis(self):
    """
    Market sentiment analysis based on funding rate. Positive funding rates indicate bullish sentiment,
    negative funding rates indicate bearish sentiment.
    """
    sentiment = []
    for index, row in self.funding_rate_data.iterrows():
        if row['funding_rate'] > 0:
            sentiment.append('Bullish')
        else:
            sentiment.append('Bearish')
    self.funding_rate_data['market_sentiment'] = sentiment

# 3. Directional Trading Strategy
def directional_trading_strategy(self):
    """
    Trade based on the direction of the funding rate. If the funding rate is increasing, it indicates a 
    bullish trend, otherwise, a bearish trend.
    """
    signals = []
    previous_rate = None
    for index, row in self.funding_rate_data.iterrows():
        if previous_rate is None:
            signals.append('Hold')
        elif row['funding_rate'] > previous_rate:
            signals.append('Buy')   # Funding rate is increasing, suggesting bullish trend
        elif row['funding_rate'] < previous_rate:
            signals.append('Sell')  # Funding rate is decreasing, suggesting bearish trend
        else:
            signals.append('Hold')
        previous_rate = row['funding_rate']
    self.funding_rate_data['directional_signal'] = signals

def run_strategies(self):
    """
    Run all strategies and generate trade signals.
    """
    self.mean_reversion_strategy()
    self.market_sentiment_analysis()
    self.directional_trading_strategy()
    return self.funding_rate_data

Instantiate the strategy class and run the strategies

strategy = FundingRateStrategy(funding_rate_data=df)
results = strategy.run_strategies()

Show the result with trade signals

print(results[[‘date’, ‘funding_rate’, ‘mean_reversion_signal’, ‘market_sentiment’, ‘directional_signal’]])

Funding Rate Trading Strategy. How to use Funding Rates?
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My Coding Journey

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This story is about how i started coding and how i started a game called “SkyBright Journey”. At this moment i am still working on the game and and adding features, before i tell you how i started “SkyBright Journey” let’s go back a few years ago when i first started coding.

How i started with coding

When i first started coding it was fun i could make games, clones of popular websites (Youtube, Facebook, Tiktok) and at the time i thought coding was easy until i got errors, missing code, code that couldn’t be used and accidentally deleting the wrong code needing to restart, that was when i started to get frustrated, sad and angry but now i think it will just slow me down and will make me want to quit and i did quit a lot of games and it end up in the trash bin in the past.

At the time i was still using HTML, CSS, Javascript. Then when i found out about python i thought it would be as hard or harder than Java but because the programming language had some English words it was easier to understand and i got less errors.

Then one day i came across a tutorial about making a platformer game with python and pygame, i didn’t know what was pygame and got curious so i click the video and that’s when i found out about pygame which is a free, open-source Python library that helps developers create multimedia applications and video games, in short it’s the platform you use to show the python code and does the hard stuff for you.

The very first game i made with pygame and python was a simple space doge game where you just have to doge asteroids and it was pretty simple though i did run into some errors i manage to make the game in 3-4 days which to me is quite quick and that’s how i started with coding and python.

Now that you know roughly how i started, i’ll tell you how i started on the game “SkyBright Journey”.

“SkyBright Journey” and how it started

The idea of “SkyBright Journey” was actually from a youtube python tutorial made by “Tech With Tim”.

Where in the video he made a python tutorial of making a platformer game with pygame, after watching and completing the code i felt like this game could be better and my goal for this game was to make it to a simple platformer game to a full on adventure game.

The very first thing i did was make a list of features and ideas i have for the game (for example: Menu screen, New maps, etc) i like to write down my ideas for games to not make the process messy so when you are making the game or just finished it and you got an idea for the game it will be hard to put it in because you have to find where to put that piece of code in like maybe a 300-400 long code which might mess up the entire code.

Speaking of messing your entire code it is very very important to make a backup version of your main code because if you mess up the main code you can always go to your backup version of your main code and copy that then paste it in your main code so you don’t have restart the whole project.

This also helps you to not be too worried about messing up and it lowers the chances of you giving up on the project because you lost all of your code.

After writing all of my ideas and features i want to put in the game i started to think about the storyline and name of the game, i prefer to think about the storyline first then name because the name of the story is dependent on the story line like if you called a cute, calming farming game “Little Nightmares” it would just sound weird and not fit with the whole game itself.

After writing the storyline, name and ideas i have for the game i started on the code itself, now i won’t tell you how to do the code you will have to make that path on your own but you can practice with tutorial about the coding language you are using right now.

The Storyline of “SkyBright Journey”

In a peaceful world where there are 5 Sky Shards (Sky Shards are pieces of a star that fell a long time ago) Earth, Water, Wind, Fire and Light.) Keeping the world at peace but after 563 years of being safe in the hands of their keepers, a trickster stole the 5 Sky Shards making corps rot, forests start catching on fire, earthquakes starts, tsunamis sink islands and tornados leave houses and farms demolished.

The whole world was in chaos till you a brave adventure sets off to find and bring back the 5 Sky Shards, after packing and saying goodbye to your friends and family you set off but on the way you meet a girl who wants to join you on the quest of finding and bringing back the 5 Sky Shards though you aren’t really sure if you should let her go with you since it would be a dangerous journey after the girl started to follow you around and begging you to bring her you finally give in let let her join.

The girl introduces herself as Pippin and says that she actually knows where the Sky Shards like the back of her hand. After Pippin gives you a map she made of where the Sky Shards are, when you ask her how she knows this she replies that her father was one of the keepers of the Sky Shards (keepers of the Sky Shards are people or beings that protect the Sky Shards).

After getting the Earth Shard from a boobie traped temple, you and Pippin rest at a nearby village. The next day the village chief learns about your adventure to get back the 5 Sky Shards and he gives you food and a sword to you and Pippin for the journey, after saying you and Pippin say our thanks and goodbyes we set off to get the next Sky Shard.

The Water Shard: Guarded by the Water beast in a sunken temple

The Wind Shard: is at the top of a tall mountain and you have to climb the mountain to the very top to get the Wind which is on a cloud

The Fire Shard: is in a boiling hot volcano that the you must jump over the lava to get the Fire Shard

The Light Shard: the most hardest out of all of the shards is the Light shard this you have to walk through a hot desert but the shard is actually in the temple you see at the beginning of the desert and if you take down the picture of the Light goddess in the temple you will see the Light shard behind it.

After getting all of the shards you and Pippin rest for the night but in the middle of the night you are awoken by a sound and you see Pippin stealing the Shards, you confront Pippin asking her why she is betrayed you but she just laughs saying that you were a fool you trust her and reveals that she actually the person who stole the Sky Shards and hid them.

Pippin runs away with. the 5 Sky Shards and you run after her, you jump over obstacles and when you finally catch up to Pippin she summons the Shadow Shard that was a Shard everyone dislike and soon became an outcast so it teamed up with Pippin to get revenge.

When Pippin summons the Shadow Shard it becomes a human and starts fighting you, after defeating the Shadow Shard you catch Pippin and get back the 5 Sky Shards, you come back with not only the 5 Sky Shards but the person who stole them.

You come back as a hero and you even get a meeting with the king, you leave with a wiser mind that even the most kind, sweet and trustworthy people can stab you in the back, after you got back the 5 Sky Shard the world comes back to peace.

The End

The Smart Money Concepts by DGM

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Smart Money Concepts (SMC) is a trading strategy that focuses on analyzing the behavior of institutional investors, such as banks and hedge funds, to gain insights into price movements in the financial markets. These institutional players, often referred to as “smart money,” have significant capital and the ability to influence price action. SMC aims to help retail traders understand how these large players operate, with the goal of following their moves to improve trading success.

SMC uses specific market structure principles to identify potential entry and exit points, such as:

  1. Market Structure: Understanding trends, swing highs, and swing lows to determine market direction.
  2. Liquidity: Institutions often target liquidity zones, such as stop-loss clusters, to execute large orders.
  3. Order Blocks: These are price ranges where institutions accumulate positions before causing a significant price move. They can act as support or resistance zones.
  4. Fair Value Gaps: Gaps left in price when the market moves quickly in one direction. These gaps often get “filled” as the price returns to those levels.
  5. Equal Highs/Equal Lows: Institutional traders target these levels to trap retail traders and induce liquidity hunts.

SMC is commonly combined with other technical indicators or confluences, like the Relative Strength Index (RSI), moving averages, or supply and demand zones, to fine-tune entries and exits. Do you plan to implement this strategy in your trading system?

That’s great! Implementing Smart Money Concepts (SMC) can enhance your trading by focusing on institutional moves and key market structure points.

To integrate SMC into your system, you may want to define key concepts such as:

  • Swing Highs/Lows: For identifying market structure.
  • Order Blocks: Zones of accumulation and distribution.
  • Liquidity Zones: Areas where retail traders set stop-losses, often targeted by smart money.
  • Fair Value Gaps: Price imbalances that often get revisited.

Given your existing system that detects buy and sell signals based on specific confluences (e.g., Swing Lows, Bearish OB, and Equal Lows for buys), you could enhance it by:

  1. Adding liquidity zones: Detecting where large liquidity might be resting (e.g., around equal highs/lows).
  2. Identifying order blocks: Marking areas of consolidation or sharp movement that could indicate institutional buying or selling.
  3. Tracking fair value gaps: Implementing logic to identify price gaps and potential retracement areas.

How would you like to approach the coding or strategy adaptation? Would you like suggestions for integrating any specific SMC principles with your current setup?

To integrate SMC principles with your existing setup, here’s how you can incorporate key concepts like liquidity zones, order blocks, and fair value gaps into your current detect_buy_sell_signals method. I’ll provide some pseudocode and ideas for how to adapt your strategy:

1. Swing Highs and Lows (Market Structure)

Since you are already using Swing Highs and Lows, you can improve their detection to mark trend reversals. For example:

pythonCopy codedef detect_swing_high_low(prices):
    # Assuming prices is a list of price points
    swing_highs = []
    swing_lows = []

    for i in range(1, len(prices) - 1):
        if prices[i] > prices[i-1] and prices[i] > prices[i+1]:
            swing_highs.append((i, prices[i]))  # Swing High
        elif prices[i] < prices[i-1] and prices[i] < prices[i+1]:
            swing_lows.append((i, prices[i]))  # Swing Low

    return swing_highs, swing_lows

2. Order Blocks (OB)

Order Blocks are areas of consolidation before a price movement. You can detect them by identifying periods of small price range followed by large moves:

pythonCopy codedef detect_order_blocks(prices, threshold=0.01):
    order_blocks = []
    
    for i in range(3, len(prices)):
        # Check for a small consolidation area (low volatility)
        if max(prices[i-3:i+1]) - min(prices[i-3:i+1]) < threshold:
            order_blocks.append((i, prices[i]))  # Mark this as an order block

    return order_blocks

3. Liquidity Zones

Liquidity zones are where stop-loss clusters are placed, often around equal highs/lows. Here’s a way to detect potential liquidity pools:

pythonCopy codedef detect_liquidity_zones(swing_highs, swing_lows, price_buffer=0.001):
    liquidity_zones = []
    
    # Liquidity zones around equal highs/lows
    for i in range(1, len(swing_highs)):
        if abs(swing_highs[i][1] - swing_highs[i-1][1]) < price_buffer:
            liquidity_zones.append(('high', swing_highs[i][1]))  # Equal highs
        
    for i in range(1, len(swing_lows)):
        if abs(swing_lows[i][1] - swing_lows[i-1][1]) < price_buffer:
            liquidity_zones.append(('low', swing_lows[i][1]))  # Equal lows

    return liquidity_zones

4. Fair Value Gaps (FVG)

Fair Value Gaps occur when there’s an imbalance in price action. You can detect these gaps when there’s a large difference between candles:

pythonCopy codedef detect_fair_value_gaps(klines, gap_threshold=0.01):
    fair_value_gaps = []

    for i in range(2, len(klines)):
        # Compare previous candle's close and current candle's open for gap detection
        previous_close = klines[i-1]['close']
        current_open = klines[i]['open']

        if abs(current_open - previous_close) > gap_threshold:
            fair_value_gaps.append((i, klines[i]['open'], klines[i-1]['close']))

    return fair_value_gaps

5. Combining SMC Elements in detect_buy_sell_signals

You can now enhance your existing detect_buy_sell_signals method to include these SMC features. For example:

pythonCopy codedef detect_buy_sell_signals(prices, klines):
    swing_highs, swing_lows = detect_swing_high_low(prices)
    order_blocks = detect_order_blocks(prices)
    liquidity_zones = detect_liquidity_zones(swing_highs, swing_lows)
    fair_value_gaps = detect_fair_value_gaps(klines)

    buy_signals = []
    sell_signals = []

    # Example logic for buy signals
    for low in swing_lows:
        if low in liquidity_zones and any(ob[1] < prices[low[0]] for ob in order_blocks):
            buy_signals.append(low)  # Buy signal at liquidity + OB confluence

    # Example logic for sell signals
    for high in swing_highs:
        if high in liquidity_zones and any(ob[1] > prices[high[0]] for ob in order_blocks):
            sell_signals.append(high)  # Sell signal at liquidity + OB confluence

    return buy_signals, sell_signals

Next Steps

  • Backtesting: You should test these concepts with historical data to fine-tune thresholds like order block consolidation ranges, gap detection limits, and liquidity zone buffers.
  • Visualization: Plotting these levels (order blocks, liquidity zones, fair value gaps) on charts will help in seeing how they align with price movements.

Would you like to focus on one of these aspects in more detail or get help with integrating them into your existing system?

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How to detect Diabetes at home?

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่ก€็ณ–้ซ˜ไบ†็šฎ่‚คๅ…ˆ็Ÿฅ “When blood sugar is high, the skin knows first.“

When blood sugar levels rise (hyperglycemia), the skin can often be one of the first parts of the body to show signs of imbalance. Here are some common skin issues related to high blood sugar:

  1. Dry Skin: High blood sugar can lead to dehydration, making the skin dry, rough, and sometimes itchy.
  2. Skin Infections: Elevated blood sugar weakens the immune system, making the skin more prone to infections. Bacterial infections and fungal infections (such as athleteโ€™s foot or jock itch) are more common in people with unstable blood sugar levels.
  3. Skin Discoloration: A condition called acanthosis nigricans can cause dark, thick patches of skin, especially in folds like the neck, armpits, or elbows. This is often a sign of insulin resistance.
  4. Slow Wound Healing: High blood sugar can impair circulation, particularly in the limbs, making it harder for wounds to heal properly.
  5. Red or Inflamed Skin: Some people may experience inflammation or redness due to poor blood circulation caused by high blood sugar.
  6. Small Skin Bumps or Rashes: Skin changes related to diabetes can sometimes cause red or brownish bumps or patches on the skin.

If you notice any of these symptoms, especially along with other signs like increased thirst, frequent urination, or fatigue, it’s a good idea to check your blood sugar and seek medical advice.

Diabetes is a chronic health condition that affects how your body turns food into energy. There are three main types:

  1. Type 1 Diabetes: An autoimmune condition where the body’s immune system attacks insulin-producing cells in the pancreas. People with Type 1 diabetes need to take insulin regularly.
  2. Type 2 Diabetes: A condition where the body becomes resistant to insulin or doesn’t produce enough insulin. It is often associated with lifestyle factors like obesity and inactivity, though genetics also play a role.
  3. Gestational Diabetes: This occurs during pregnancy and typically goes away after childbirth, but it increases the mother’s risk of developing Type 2 diabetes later in life.

Managing diabetes often involves monitoring blood sugar levels, maintaining a healthy diet, regular exercise, and in some cases, medication or insulin injections.

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How to detect potential cancer at home?

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How Aflatoxins (้ป„ๆ›ฒ้œ‰็ด ) Cause Liver Cancer?

็™Œ็ป†่ƒžไนŸๆœ‰ๅคฉๆ•Œ๏ผŒ่‚ฟ็˜คๅŒป็”Ÿๅ…ฌ่ฎค็š„3็งๆŠ—็™Œ้ฃŸ็‰ฉ๏ผŒๆ€ๆญปไฝ“ๅ†…็™Œ็ป†่ƒž๏ผŒๆฏๅคฉๆข็€ๅƒ๏ผŒๆŠ—็™Œๆœ‰ๅฅ‡ๆ•ˆ๏ผ

Aflatoxins are a group of toxins produced by certain molds, primarily Aspergillus flavus and Aspergillus parasiticus, which grow in soil, decaying vegetation, grains, and nuts. These toxins are known to contaminate food supplies, particularly in warm and humid regions. Aflatoxin exposure is a major health concern because of its potent carcinogenic effects, particularly related to liver cancer.

How Aflatoxins Cause Liver Cancer:

  1. Liver Damage and Carcinogenesis:
    • When aflatoxins are ingested, the liver metabolizes them into reactive compounds, including aflatoxin B1 (AFB1), which is the most toxic.
    • Aflatoxin B1 gets converted into a reactive form by enzymes in the liver. This form can bind to DNA, causing mutations in the p53 tumor suppressor gene. This leads to uncontrolled cell growth and the development of hepatocellular carcinoma (HCC), a common form of liver cancer.
  2. Synergy with Hepatitis:
    • The risk of liver cancer is significantly higher when aflatoxin exposure is combined with chronic hepatitis B (HBV) or C (HCV) infection. Aflatoxin-induced DNA damage, coupled with viral infection, accelerates cancer development.
  3. Foods at Risk:
    • Foods like maize (corn), peanuts, tree nuts, and grains are particularly susceptible to aflatoxin contamination, especially in regions where storage practices are poor, leading to mold growth.

Prevention:

  • Regulation and Monitoring: Many countries regulate aflatoxin levels in food and animal feed to reduce the risk of contamination.
  • Improved Storage: Keeping crops dry and storing them in proper conditions can help prevent aflatoxin-producing molds from growing.
  • Detoxification Methods: Some efforts are being made to develop food treatments to detoxify aflatoxins, but these are not widely available.

Long-term exposure to high levels of aflatoxin is strongly associated with liver cancer, particularly in areas with high rates of aflatoxin contamination and hepatitis B infection.

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