Category: Futures Trading

Futures Trading like XRP/USD

Liquidation data can be highly informative

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I Decoded The Liquidity & Manipulation Algorithm In Day Trading
DGM Payment - SOLUSDT SMC 30m

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?
Futures and Options Trading Strategies

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Here is an example of what an affiliate URL will look like after you have signed up for your affiliate link:

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Mean Reversion Strategy Python codes

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  • This is not coins staking nor yield farming or to provide liquidation.
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Mean Reversion Trading Strategy

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Sharpe vs Sortino Ratio | Differences Explained

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Which time frame performing the best result after comparing the sharpe vs sortino ratio?

@DailyGameMoments

Test EMA Strategy 1:

Test SMA Strategy 2:

DGM Class Start Date and Time now is 2024-05-19 14:18:50.497258
START: 30m SOLUSDT OHLC-data from 2020-11-11 00:00:00 until 2024-05-19
DGM Class Start Date and Time now is 2024-05-19 14:22:57.747713
START: 30m SOLUSDT OHLC-data from 2020-11-11 00:00:00 until 2024-05-19
Open Time Open High Low Close
0 2020-11-10 16:00:00 2.1840 2.2528 2.1839 2.2484
1 2020-11-10 16:30:00 2.2484 2.2485 2.2304 2.2409
2 2020-11-10 17:00:00 2.2395 2.2995 2.2255 2.2790
3 2020-11-10 17:30:00 2.2790 2.2869 2.2438 2.2554
4 2020-11-10 18:00:00 2.2518 2.2554 2.2111 2.2111
.. … … … … …
659 2024-05-19 04:00:00 174.3200 174.4100 173.3100 173.5900
660 2024-05-19 04:30:00 173.5900 174.5000 173.3500 174.5000
661 2024-05-19 05:00:00 174.5000 175.5000 174.3600 174.8500
662 2024-05-19 05:30:00 174.8600 174.9400 173.8600 174.0700
663 2024-05-19 06:00:00 174.0700 174.3400 173.9200 174.1400

[61664 rows x 5 columns]
END: v3/klines 30m SOLUSDT DB Processed.

START DB Processing: >>> Data from GetDataRequested table.
Mean Price: 62.08541965814739
Standard Deviation: 58.721715230338866
v3/klines Upper Percentage Threshold: 179.52885011882512
v3/klines Lower Percentage Threshold: -55.358010802530345
Open Time High Low Close
0 2020-11-10 16:00:00 2.2528 2.1839 2.2484
1 2020-11-10 16:30:00 2.2485 2.2304 2.2409
2 2020-11-10 17:00:00 2.2995 2.2255 2.2790
3 2020-11-10 17:30:00 2.2869 2.2438 2.2554
4 2020-11-10 18:00:00 2.2554 2.2111 2.2111
GetDataProcess Trade Message = None
GetDataProcess Trade Price (Qty: 0.05) = 0
GetDataProcess Data processed and saved to QuantTrending table.
END: v3/klines 30m SOLUSDT GetDataProcess Completed.

START DB Processing: >>> Data from QuantTrending table.
BackTestSharpRatioMDD: >>> Processed the file QuantTrending.csv.
Warning: NaN values detected in ‘Previous_Close’. Attempting to recalculate ‘Daily_PnL’.
Warning: NaN or Infinite values detected in ‘Daily_PnL’ after recomputation. Cleaning required.
Info: Previous_Close Close Daily_PnL Cumm_PnL
count 61663.000000 61663.000000 61663.000000 61663.000000
mean 62.083602 62.086390 0.001621 63.921336
std 58.720934 58.722173 0.007704 26.552746
min 1.101800 1.101800 -0.178662 0.000000
25% 20.810000 20.810000 0.000000 49.072912
50% 33.951000 33.960000 0.000000 72.006780
75% 97.620000 97.620000 0.000000 85.162826
max 258.440000 258.440000 0.200871 99.982746

*> DGM Sharp Ratio = 4.02
**> DGM Maximum Drawdown: 17.87%
***> DGM Peak Profit: 9998.27%
****> DGM Drawdown from Peak: 0.00%
*****>> Created SOLUSDT-QuantSharpRatioMDD-30m.csv for Equity Curve.

Test LWMA Strategy 3:

In summary:

fastEMA:30 slowEMA:50 SOLUSDT SL=2.5, TP=1.8, return=12.76% in 1h.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.6, TP=2.1, return=4.73% in 30m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.6, TP=2.1, return=4.30% in 30m.
fastWMA:10 slowWMA:34 SOLUSDT SL=1.6, TP=2.1, return=13.84% in 30m.
Beyond the Sharpe Ratio: Unveiling Effective Trading Strategy Assessment

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“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

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Price Action EMA + RSI + Bollinger Bands With Bots Testing

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Which time frame performing the best result after some back testing?

fastEMA:30 slowEMA:50 SOLUSDT SL=2.5, TP=1.8, return=13.29% in 1h.
fastEMA:30 slowEMA:50 SOLUSDT SL=2.5, TP=1.3, return=4.96% in 30m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.2, TP=1.1, return=-1.01% in 15m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.5, TP=2.5, return=-0.42% in 5m.
fastEMA:30 slowEMA:50 SOLUSDT SL=1.1, TP=1.4, return=0.66% in 4h.
fastEMA:20 slowEMA:40 SOLUSDT SL=2.4, TP=2.2, return=6.70% in 1h.
fastEMA:50 slowEMA:100 SOLUSDT SL=2.0, TP=2.5, return=8.82% in 1h.

fastEMA:30 slowEMA:50 ETHUSDT SL=2.1, TP=2.4, return=10.72% in 1h.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.8, TP=1.3, return=1.06% in 30m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.1, TP=1.7, return=0.10% in 15m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.3, TP=2.5, return=0.69% in 5m.
fastEMA:30 slowEMA:50 ETHUSDT SL=1.0, TP=1.3, return=3.97% in 4h.
fastEMA:20 slowEMA:40 ETHUSDT SL=2.2, TP=2.4, return=9.95% in 1h.
fastEMA:50 slowEMA:100 ETHUSDT SL=1.3, TP=2.5, return=-2.24% in 1h.

def count_opened_trades():
    api_instance = c.API(access_token)
    config = api_instance.config
    api_key = config.get(“api_key_bybit”)
    api_secret = config.get(“api_secret_bybit”)
    session = HTTP(testnet=False, api_key=api_key, api_secret=api_secret)
    try:
        data = session.get_positions(category=”linear”, symbol=Symbol)
        size = data[‘result’][‘list’][0][‘size’]
        return float(size)
    except Exception as e:
        print(f”DGM Failed to get {Symbol} position: {e}”)
        return

def ema_signal(df, current_candle, backcandles):  
    df_slice = df.reset_index().copy()
    start = max(0, current_candle – backcandles)
    end = current_candle + 1
    relevant_rows = df_slice.iloc[start:end]
   
    # Check if all EMA_fast values are below EMA_slow values (buy signal)
    if (relevant_rows[‘EMA_fast’] < relevant_rows[‘EMA_slow’]).all():
        return 1
    # Check if all EMA_fast values are above EMA_slow values (sell signal)
    elif (relevant_rows[‘EMA_fast’] > relevant_rows[‘EMA_slow’]).all():
        return -1
    else:
        return 0
   
def total_signal(df, current_candle, backcandles):
    if isinstance(current_candle, pd.Timestamp):
        current_candle = df.index.get_loc(current_candle)

    ema_signal_result = ema_signal(df, current_candle, backcandles)

    candle_open_price = df[‘Open’].iloc[current_candle]
    bbl = df[‘BBL_15_1.5’].iloc[current_candle]
    bbu = df[‘BBU_15_1.5’].iloc[current_candle]

    if ema_signal_result == 1 and candle_open_price <= bbl:
        return 1
    if ema_signal_result == -1 and candle_open_price >= bbu:
        return -1
    return 0

def get_candles(symbol, interval, lookback):
    url = f”{API_URLv3}{library}?symbol={symbol}&interval={interval}&limit={lookback}”
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()

        if not data:
            print(f”No data received from {API_URLv3}{library} API.”)
            return None

        df = pd.DataFrame(data, columns=[
            ‘open_time’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’,
            ‘close_time’, ‘quote_asset_volume’, ‘number_of_trades’,
            ‘taker_buy_base_asset_volume’, ‘taker_buy_quote_asset_volume’, ‘ignore’
        ])
        df[‘open_time’] = pd.to_datetime(df[‘open_time’], unit=’ms’)
        df.set_index(‘open_time’, inplace=True)
        df.rename(columns={‘open’: ‘Open’, ‘high’: ‘High’, ‘low’: ‘Low’, ‘close’: ‘Close’}, inplace=True)

        df[[‘Open’, ‘High’, ‘Low’, ‘Close’]] = df[[‘Open’, ‘High’, ‘Low’, ‘Close’]].astype(float)
        return df[[‘Open’, ‘High’, ‘Low’, ‘Close’]]
    except requests.RequestException as e:
        print(f”{symbol} Request failed: {e}”)
    except Exception as e:
        print(f”Failed to process {symbol} data: {e}”)
    return None

def get_candles_frame(lookback):
    candles = get_candles(Symbol, Interval, lookback)

    if candles is None:
        print(f”Failed to retrieve {Interval} {Symbol} candle data.”)
        return None

    dfstream = candles.copy()

    dfstream[‘ATR’] = ta.atr(dfstream[‘High’], dfstream[‘Low’], dfstream[‘Close’], length=7)
    dfstream[‘EMA_fast’] = ta.ema(dfstream[‘Close’], length=30)
    dfstream[‘EMA_slow’] = ta.ema(dfstream[‘Close’], length=50)
    dfstream[‘RSI’] = ta.rsi(dfstream[‘Close’], length=10)
    my_bbands = ta.bbands(dfstream[‘Close’], length=15, std=1.5)
    dfstream = dfstream.join(my_bbands)
    if not isinstance(dfstream.index, pd.DatetimeIndex):
        dfstream.index = pd.to_datetime(dfstream.index)

    dfstream[‘TotalSignal’] = dfstream.apply(lambda row: total_signal(dfstream, row.name, 7), axis=1)
    dfstream.to_csv(‘dfstream.csv’)
    return dfstream

def optimization():
    slatrcoef = 0
    TPSLRatio_coef = 0

    dfstream = get_candles_frame(lookback)
    if dfstream is None:
        print(f”No candle data for {Symbol} fitting optimization job.”)
        return

    def SIGNAL():
        return dfstream[‘TotalSignal’]

    class MyStrat(Strategy):
        mysize = 3000
        slcoef = 1.3
        TPSLRatio = 2.5

        def init(self):
            self.signal1 = self.I(SIGNAL)

        def next(self):
            slatr = self.slcoef * self.data.ATR[-1]
            TPSLRatio = self.TPSLRatio

            if self.signal1[-1] == 2 and len(self.trades) == 0:
                sl1 = self.data.Close[-1] – slatr
                tp1 = self.data.Close[-1] + slatr * TPSLRatio
                self.buy(sl=sl1, tp=tp1, size=self.mysize)

            elif self.signal1[-1] == 1 and len(self.trades) == 0:
                sl1 = self.data.Close[-1] + slatr
                tp1 = self.data.Close[-1] – slatr * TPSLRatio
                self.sell(sl=sl1, tp=tp1, size=self.mysize)

    bt = Backtest(dfstream, MyStrat, cash=100000, margin=0.01, commission=0.00055)
    stats, heatmap = bt.optimize(slcoef=[i/10 for i in range(10, 26)],
                                 TPSLRatio=[i/10 for i in range(10, 26)],
                                 maximize=’Return [%]’, max_tries=300,
                                 random_state=0,
                                 return_heatmap=True)
    print(stats)
   
    slatrcoef = stats[“_strategy”].slcoef
    TPSLRatio_coef = stats[“_strategy”].TPSLRatio
    print(f”{Symbol} SL = {slatrcoef}, TP = {TPSLRatio_coef}, expected return, {stats[‘Return [%]’]:.2f}% in {Interval} interval.\n”)
   
    with open(“fitting_data_file.txt”, “a”) as file:
        file.write(f”{Symbol} SL = {slatrcoef}, TP = {TPSLRatio_coef}, expected return, {stats[‘Return [%]’]:.2f}% in {Interval} interval.\n”)
    return slatrcoef, TPSLRatio_coef

def trading_job():
    dfstream = get_candles_frame(lookback)
    if dfstream is None:
        print(f”No {Symbol} candle data for trading job.”)
        return

    signal = total_signal(dfstream, len(dfstream) – 1, 7)



    # now = datetime.now()
    # if now.weekday() == 0 and now.hour < 7 and now.minute < 5:  # Monday before 07:05
    slatrcoef, TPSLRatio_coef = optimization()
    print(f”Optimize SL = {slatrcoef}, and TP = {TPSLRatio_coef}.”)

    slatr = slatrcoef * dfstream[‘ATR’].iloc[-1]
    TPSLRatio = TPSLRatio_coef
    max_spread = 16e-5

    last_candle = get_candles(Symbol, Interval, 1).iloc[-1]
    candle_open_bid = float(last_candle[‘Open’])
    candle_open_ask = candle_open_bid
    spread = candle_open_ask – candle_open_bid

    SLBuy = candle_open_bid – slatr – spread
    SLSell = candle_open_ask + slatr + spread

    TPBuy = candle_open_ask + slatr * TPSLRatio + spread
    TPSell = candle_open_bid – slatr * TPSLRatio – spread

    print(“SLBuy = “, SLBuy)
    print(“SLSell = “, SLSell)
    print(“TPBuy  = “, TPBuy )
    print(“TPSell = “, TPSell)
   
    # # Sell
    # if signal == -1 and count_opened_trades() == 0.0 and spread < max_spread:
    #     print(“Sell Signal Found…”)
    #     trade_crypto = c.TradeCrypto(EXCHANGE, Symbol, ‘sell’)
    #     message, MyTradePrice = trade_crypto.TradeQty(quantity)
    #     print(message)
    #     with open(“trading_data_file.txt”, “a”) as file:
    #         file.write(f”SL = {SLSell}, TP = {TPSell}, Trade Price = {MyTradePrice}\n”)

    # # Buy
    # elif signal == 1 and count_opened_trades() == 0.0 and spread < max_spread:
    #     print(“Buy Signal Found…”)
    #     trade_crypto = c.TradeCrypto(EXCHANGE, Symbol, ‘buy’)
    #     message, MyTradePrice = trade_crypto.TradeQty(quantity)
    #     print(message)
    #     with open(“trading_data_file.txt”, “a”) as file:
    #         file.write(f”SL = {SLBuy}, TP = {TPBuy}, Trade Price = {MyTradePrice}\n”)


if __name__ == “__main__”:
    optimization()

# scheduler = BlockingScheduler()
# scheduler.add_job(trading_job, ‘cron’, day_of_week=’mon-fri’, hour=’07-18′, minute=’1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56′, timezone=’Asia/Beirut’, misfire_grace_time=15)
# scheduler.start()

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.

“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

Elite Quant Trading
Log in

Tips:

Despite of the crypto dump recently on all the alt coins after SEC announcement to sue Binance and Coinbase. Guess what? My Ai Trading Strategies are making shit ton of USDT from the crazy markets. Well there is a secret and cannot tell you unless…Anyway, I have given you the formula to copy and it is up to you to trade manually with stress and sleepless nights or ride on the trend of Ai trading today ⬇️⬇️⬇️

AI Sleeping Income With DGM System

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?

BackTest Sharpe Ratio and Max Draw Down

DGM

It works like a clock daily…

DGM Sharp Ratio = 3.5

2024-05-14 09:01:09,378 – INFO – DGM Start Date and Time now is 2024-05-14 09:01:09.378769
2024-05-14 09:01:09,880 – INFO – All data downloaded successfully in GetDataRequest.
2024-05-14 09:01:09,993 – INFO – Exported FROM 2020-11-11 TO 2024-05-14 Data saved to DB.
2024-05-14 09:01:10,401 – INFO – File QuantTrending.csv has been successfully deleted.
2024-05-14 09:01:10,401 – INFO – >>*>> ETHUSDT-QuantSharpRatioMDD-1d.csv for Equity Curve.
2024-05-14 09:01:10,401 – INFO – BackTest Trade Message = None
2024-05-14 09:01:10,401 – INFO – BackTest Trade Price (Qty: 0.05) = 0
DGM Class Start Date and Time now is 2024-05-14 09:01:09.380486
START: 1d ETHUSDT OHLC-data from 2020-11-11 00:00:00 until 2024-05-14
Open Time Open High Low Close
0 2020-11-11 450.34 476.25 449.28 463.09
1 2020-11-12 463.09 470.00 451.20 462.39
2 2020-11-13 462.48 478.01 457.12 476.43
3 2020-11-14 476.42 477.47 452.00 460.89
4 2020-11-15 460.90 462.89 440.19 448.08
.. … … … … …
276 2024-05-10 3036.24 3053.89 2878.03 2909.99
277 2024-05-11 2909.98 2945.67 2886.46 2912.45
278 2024-05-12 2912.45 2955.20 2901.17 2929.29
279 2024-05-13 2929.30 2996.40 2864.76 2950.99
280 2024-05-14 2950.99 2958.76 2937.51 2944.75

[1281 rows x 5 columns]
END: v3/klines 1d ETHUSDT DB Processed.

START DB Processing: >>> Data from GetDataRequested table.
Mean Price: 2208.228266978923
Standard Deviation: 914.490172178236
v3/klines Upper Percentage Threshold: 4037.208611335395
v3/klines Lower Percentage Threshold: 379.2479226224509
Open Time High Low Close
0 2020-11-11 476.25 449.28 463.09
1 2020-11-12 470.00 451.20 462.39
2 2020-11-13 478.01 457.12 476.43
3 2020-11-14 477.47 452.00 460.89
4 2020-11-15 462.89 440.19 448.08
GetDataProcess Trade Message = None
GetDataProcess Trade Price (Qty: 0.05) = 0
GetDataProcess Data processed and saved to QuantTrending table.
END: v3/klines 1d ETHUSDT GetDataProcess Completed.

START DB Processing: >>> Data from QuantTrending table.
BackTestSharpRatioMDD: >>> Processed the file QuantTrending.csv.
Warning: NaN values detected in ‘Previous_Close’. Attempting to recalculate ‘Daily_PnL’.
Warning: NaN or Infinite values detected in ‘Daily_PnL’ after recomputation. Cleaning required.
Info: Previous_Close Close Daily_PnL Cumm_PnL
count 1280.000000 1280.000000 1280.000000 1280.000000
mean 2207.652859 2209.591656 0.014513 11.073642
std 914.972973 913.902063 0.038246 5.210313
min 448.080000 448.080000 -0.263329 0.000000
25% 1598.405000 1599.225000 -0.000000 7.088900
50% 1899.325000 1900.090000 -0.000000 12.360589
75% 2910.605000 2914.057500 0.031202 15.726248
max 4807.980000 4807.980000 0.277372 18.590075

*> DGM Sharp Ratio = 7.25
**> DGM Maximum Drawdown: 28.47%
***> DGM Peak Profit: 1859.01%
****> DGM Drawdown from Peak: 1.40%
*****>> Created ETHUSDT-QuantSharpRatioMDD-1d.csv for Equity Curve.

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.

“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

Elite Quant Trading
Log in

Tips:

Despite of the crypto dump recently on all the alt coins after SEC announcement to sue Binance and Coinbase. Guess what? My Ai Trading Strategies are making shit ton of USDT from the crazy markets. Well there is a secret and cannot tell you unless…Anyway, I have given you the formula to copy and it is up to you to trade manually with stress and sleepless nights or ride on the trend of Ai trading today ⬇️⬇️⬇️

AI Sleeping Income With DGM System

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?

AI Strategies To Become A Millionaire

DGM
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

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

DGM-Sharp-Ratio-March 2024

DGM

Never before this is reviewed by Jim Simon

About DGM
DGM Sharp Ratio = 3.5
DGM Sharp Ratio = 3.5

DGM Sharp Ratio = 3.5015 and Winning rate = 73.33596%

Equity-Curve-March-2024

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.

“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

Elite Quant Trading
Log in

Tips:

Despite of the crypto dump recently on all the alt coins after SEC announcement to sue Binance and Coinbase. Guess what? My Ai Trading Strategies are making shit ton of USDT from the crazy markets. Well there is a secret and cannot tell you unless…Anyway, I have given you the formula to copy and it is up to you to trade manually with stress and sleepless nights or ride on the trend of Ai trading today ⬇️⬇️⬇️

AI Sleeping Income With DGM System

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

DGM
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