Liquidation data can be highly informative

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?

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?

Mean Reversion Strategy Python codes

  • This is not a money game.
  • This is not a manual trading system,
  • This is not a trading system with charts and indicators.
  • This is not a paid solution, this is DGM sleeping solution.
  • This is not an investment program and it is a trading Ai strategy.
  • This is not coins staking nor yield farming or to provide liquidation.
  • This is not TradingView pine scripts trading or webhook complicated settings.
  • This is not financial advise and Do Your Own Research (DYOR). You need only 3 steps!
  • This is not limited to one crypto exchange that you can use. More than one that you can choose.
  • This is not a solution without DGM guidance. I will provide a guidance which cryptocurrencies to trade.
  • This is not MT4/MT5 or EA and has nothing to do with VPS or setup a mini-PC to automate your trades.

Step 1: Setup an Ai Trading System in AWS.

The basic cost for AWS hosting to run Python codes and WordPress hosting is just $5 USD per month only.

Step 2: Setup A Trading Platform by adjusting the setting for both Mean Reversion & Trending Strategies

Mean Reversion Trading Strategy

Step 3: Monitor and Observe the AI Performance Forever

How can you do that?

  1. Create an application programming interface (API) with any exchange platforms (Cryptos or Options).
  2. Paste the API secret keys into the Python codes.
  3. Your job is done! You will make the passive income daily!

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AI Strategies To Become A Millionaire

https://dailygamemoments.com/traderdao
  • This is not a money game.
  • This is not a manual trading system,
  • This is not a trading system with charts and indicators.
  • This is not a paid solution, this is DGM sleeping solution.
  • This is not an investment program and it is a trading Ai strategy.
  • This is not coins staking nor yield farming or to provide liquidation.
  • This is not TradingView pine scripts trading or webhook complicated settings.
  • This is not financial advise and Do Your Own Research (DYOR). You need only 3 steps!
  • This is not limited to one crypto exchange that you can use. More than one that you can choose.
  • This is not a solution without DGM guidance. I will provide a guidance which cryptocurrencies to trade.
  • This is not MT4/MT5 or EA and has nothing to do with VPS or setup a mini-PC to automate your trades.

Step 1: Setup an Ai Trading System

Step 2: Setup A Trading Platform To Earn USDT When You Sleep

Step 3: Marry Them Both Together Forever

How can you do that?

  1. Create an application programming interface (API) with TraderDAO in Bybit exchange (Which is Step 2).
  2. Paste the API secret keys into the Ai Trading system app (Which is Step 1).
  3. Your job is done! You will make the passive income daily from Step 1 and also Step 2. One Trade Two Incomes!
Build An Ai Trading System That Make Money Daily

Identifying Key Structures & Liquidity Zones

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Yahoo Finance with MARKOV Strategy

About DGM

A Regime Switching Model can help identify and switch between different market regimes, such as mean reversion (when prices tend to revert to a mean) and momentum trending (when prices follow a trend). One common approach to model such regimes is using a Markov Regime Switching Model (MRS), where the market can switch between different states (regimes) based on probabilities.

Here’s a guide on how you could implement this:

1. Define the Regimes:

  • Mean Reversion: In this regime, prices fluctuate around a long-term mean. The idea is to buy when prices are below the mean and sell when they are above the mean.
  • Momentum Trending: In this regime, prices tend to follow a trend. The strategy here is to go long during an uptrend and short during a downtrend.

2. Data Preparation:

  • Collect historical price data (e.g., closing prices).
  • Compute indicators that capture mean reversion and momentum behavior. Common indicators include:
    • Mean Reversion: Moving average (MA), Bollinger Bands, Z-Score.
    • Momentum: Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), trend-based indicators.

3. Markov Regime Switching Model:

  • The Markov model assumes that the market can be in one of two or more regimes, with a certain probability of switching between them.
  • The key parameters are the transition probabilities between regimes and the characteristics (e.g., mean, variance) of returns in each regime.

Steps to Implement:

  1. Model the Log Returns: Use log returns of the asset prices to model changes. These will serve as the inputs to the regime switching model.
  2. Define Two Regimes:
    • Regime 1: Mean-reversion behavior.
    • Regime 2: Momentum/trending behavior.
  3. Fit the Model: Use a package like statsmodels in Python to fit the Markov Regime Switching Model. Here’s an example using MarkovAutoregression:

import numpy as np
import pandas as pd
from statsmodels.tsa.regime_switching.markov_regression import MarkovRegression

Example of fitting Markov Regime Switching model to stock returns

Generate log returns from price data

prices = pd.Series([100, 102, 104, 103, 102, 106, 108, 110, 112])
log_returns = np.log(prices / prices.shift(1)).dropna()

Fit Markov Switching model

model = MarkovRegression(log_returns, k_regimes=2, trend=’c’, switching_variance=True)
results = model.fit()

Print summary

print(results.summary())

Predict the regime at each time step

regimes = results.smoothed_marginal_probabilities[0] # Regime probabilities
print(regimes)

This code fits a two-regime Markov switching model to the log returns. The results object contains the parameters and probabilities of being in each regime over time.

4. Interpreting the Results:

  • Regime 1: This could represent the mean-reversion regime, where prices tend to revert to their mean.
  • Regime 2: This could represent the momentum-trending regime, where prices exhibit directional trends.

The model estimates the probability of being in each regime at each time step, allowing you to determine whether the market is in a mean-reversion or momentum-trending state.

5. Strategy Implementation:

  • When in Mean Reversion (Regime 1):
    • If the price is below the mean (using indicators like moving averages), go long.
    • If the price is above the mean, go short.
  • When in Momentum (Regime 2):
    • Buy when the trend is upward and sell when the trend is downward.

6. Backtesting and Optimization:

  • Backtest the strategy by switching between the two regimes based on the predicted probabilities.
  • Fine-tune your indicators, such as the length of the moving averages or momentum indicators, to optimize performance.

Libraries to Consider:

  • statsmodels for regime-switching models.
  • hmmlearn for Hidden Markov Models, which is an alternative approach.

By switching between the two regimes based on the probabilities from the regime-switching model, you can potentially capture the market’s mean-reverting or trending behavior at the right time.

The biggest risk is not taking any risk… In a world that changing really quickly, the only strategy that is guaranteed to fail is not taking risks.” Mark Zuckerberg

In 1993, Buffett spoke to Columbia University’s Business School graduates. Asked about his method for evaluating risk, he said, “Risk comes from not knowing what you’re doing.” This quote reflects Buffett’s investment philosophy, highlighting the crucial role of knowledge and understanding in reducing risk.



Tips:

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The SECRET is to marry between Ai trading strategies and an income generated exchange platform

  • Ai trading strategies

  • An income generated exchange platform

How It Works?


DGM Sleeping + Ai Trading System

https://dailygamemoments.com/traderdao
  • This is not a money game.
  • This is not a manual trading system,
  • This is not a trading system with charts and indicators.
  • This is not a paid solution, this is DGM sleeping solution.
  • This is not an investment program and it is a trading Ai strategy.
  • This is not coins staking nor yield farming or to provide liquidation.
  • This is not TradingView pine scripts trading or webhook complicated settings.
  • This is not financial advise and Do Your Own Research (DYOR). You need only 3 steps!
  • This is not limited to one crypto exchange that you can use. More than one that you can choose.
  • This is not a solution without DGM guidance. I will provide a guidance which cryptocurrencies to trade.
  • This is not MT4/MT5 or EA and has nothing to do with VPS or setup a mini-PC to automate your trades.

Step 1: Setup an Ai Trading System

Step 2: Setup A Trading Platform To Earn USDT When You Sleep

Step 3: Marry Them Both Together Forever

How can you do that?

  1. Create an application programming interface (API) with TraderDAO in Bybit exchange (Which is Step 2).
  2. Paste the API secret keys into the Ai Trading system app (Which is Step 1).
  3. Your job is done! You will make the passive income daily from Step 1 and also Step 2. One Trade Two Incomes!
Build An Ai Trading System That Make Money Daily

Do you want to setup your Casino and start earning interest rates? Follow me to read on 

TraderDAO.ai + TradingView Webhook

https://dailygamemoments.com/traderdao
Follow my foot steps and discover yourself a free AI trading solution !!

There are 3 main components in this AI Trading setup before you can see your own passive income…

  1. TraderDAO.ai, provides a subaccount on Bybit exchange platform to execute trades and earn passive income.
  2. TradingView.to, provides an API platform to integrate with your own crypto exchange accounts (Bybit or Binance) or TraderDAO and by using a webhook in TradingView to trigger trades in TraderDAO. Alternatively, you can also use a free provider such as TheTradveller or a paid provider such as TradeAdapter.
  3. TradingView Pro plan, provides executable and profitable trades based on the strategies given.

TraderDAO is using TradeGDT which is using sharp ratio auto bots to trade in many small time frame and making use of ChatGPT to generate specific formula codes to communicate wanted trades with the TradeGDT.

DYOR before joining the telegram group. I have parked some capital to mine $POT tokens with TraderDAO and at the same time enjoying the passive income from the trades net profit of the AI trading strategies via TheTradveller API and TradingView Webhook URL settings. However, you must subscribe to the TradingView Pro plan and above in order to use the Webhook setting.

There are a few AI trading strategies to select based on your testing of each strategy, such as Tradveller Pivot, Tradveller Momentum, and Statistical Arbitrage Right Leg + Statistical Arbitrage Left Leg strategies. WhatsApp me when you are looking for the detail configuration and option.

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Traders can continue their normal trading activities while simultaneously contributing valuable data to TradeGDT, an advanced AI system that has the potential to transform the trading landscape.

As a reward for their contributions, traders will receive $POT tokens, representing ownership of value on the TraderDAO.

How to earn from TraderDAO?

TraderDAO is a decentralized autonomous organization (DAO) that is designed to provide a decentralized platform for traders to collaborate, share information, and access tools that can help them trade more efficiently. TraderDAO provides several services, including:

  1. Social trading: A platform for traders to share their trading strategies and ideas. Not sure about it right now after joined their telegram group before.
  2. Liquidity pooling: A mechanism for traders to pool their resources and provide liquidity to different markets. Don’t think it is realistic yet.
  3. Analytics: A platform that provides traders with real-time analytics and insights to help them make better trading decisions.

Note: If you are new in Crypto, encourage you to watch these videos here and you can fast track your learning curve by paying for the VIP membership. You can have a choice not to use TraderDAO and replace with your own Bybit account, and you will miss out $POT tokens as an additional income source.

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