Sharpe vs Sortino Ratio | Differences Explained

May 19, 2024May 19, 2024 DGM

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

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

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 ⬇️⬇️⬇️

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Price Action EMA + RSI + Bollinger Bands With Bots Testing

May 18, 2024June 4, 2024 DGM

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()

“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

Tips:

AI Sleeping Income With DGM System

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

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How It Works?

BackTest Sharpe Ratio and Max Draw Down

May 14, 2024May 19, 2024 DGM

It works like a clock daily…

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.

“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

Tips:

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?

CHWY Sell Put Options Again in March 2024

March 21, 2024March 21, 2024 DGM

Analyzing any USA stocks with python codes. We can understand the sentiment much better,,, The Markov analysis process involves defining the likelihood of a future action, given the current state of a variable. Once the probabilities of future actions at each state are determined, a decision tree can be drawn, and the likelihood of a result can be calculated.

To predict the sequence probability and frequency in the past data set. In game theory, a Markov strategy is one that depends only on state variables that summarize the history of the game in one way or another. For instance, a state variable can be the current play in a repeated game, or it can be any interpretation of a recent sequence of play.

Tips:

“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 ⬇️⬇️⬇️

Here To Trade Options From Experts With Best Wins And 100% FREE!!!

Yahoo Finance with MARKOV Strategy

March 16, 2024September 10, 2024 DGM

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:

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.
Define Two Regimes:
- Regime 1: Mean-reversion behavior.
- Regime 2: Momentum/trending behavior.
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

Tips:

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-Sharp-Ratio-March 2024

March 15, 2024March 21, 2024 DGM

Never before this is reviewed by Jim Simon

About DGM

DGM Sharp Ratio = 3.5015 and Winning rate = 73.33596%

“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

Tips:

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?

Why Bitcoin, Ethereum, and Dogecoin Are All Down Big Last November 11,2021

August 16, 2022October 12, 2022 SeanRay

After weeks of almost continuous good news for the cryptocurrency industry, we are seeing a widespread sell-off today. Big cryptocurrencies are down, altcoins are down, and even meme coins are having a rough day. In the last 24 hours, Bitcoin (CRYPTO:BTC) is down 5%, Ethereum (CRYPTO:ETH) is down 1.3% after being down as much as 8%, and Dogecoin (CRYPTO:DOGE) has fallen 2.4% as of 1 p.m. EST. Very few cryptocurrencies are trading higher today and some are down nearly 10% in the last 24 hours. Higher inflation in theory will lead to higher interest rates and investors looking for ways to hedge inflation and higher rates. As a store of value, Bitcoin, in particular, wasseen as a potential place for investors to put money if inflation picks up. That’s why it was up yesterday, but the trend has reversed course today.

Cryptocurrencies have been on a quick run higher over the past month. Bitcoin and Ethereum, in particular, are up over 20% and a pullback is natural at this point. It could simply be that big investors are taking some chips off the table today.

Which Cryptocurrency is the best way to invest in and why?

August 16, 2022August 16, 2022 Natoyyy19

For my opinion, Bitcoin is one of the biggest cryptocurrency that had been used for past years. Bitcoin’s recent surge was the result of a few catalysts, not least of which was the launch of the first Bitcoin-focused exchange-traded fund in the U.S. The Proshares Bitcoin Strategy ETF (ticker: BITO) began trading Oct. 19, with excitement over the continued mainstreaming of the digital asset driving BTC to an all-time high of $66,930 just a day later. Bitcoin’s invention in 2009 changed money forever and opened the door to blockchain-based, decentralized currencies not issued by any nation. Bitcoin’s first-mover status, its hard limit on a maximum circulation of 21 million BTC and its potential to become a store of value on the balance sheets of corporate America – a future which Tesla Inc. (TSLA) first hinted at with a $1.5 billion Bitcoin purchase announced in February.

Fed Chair Jerome Powell on interest rate decision after FOMC meeting

July 28, 2022September 5, 2022 DGM

The latest announcement yesterday that 75 points have been added, the interest is already 2.5%. There are still 2 meetings before the end of the year, that is, the next 2 meetings are mainly about 50 points in September and 50 points in December [total 3.5%], there is even a chance to add 25 points each, a total of 3%.

Fed Chairman Powell: We want to achieve a moderate level of tightening by the end of this year, that is, interest rates in the 3%-3.5% range.

Fed Chair Jerome Powell on interest rate decision after FOMC meeting the BTC/USDT shoot up from 21k to 23k.

Stock CFD Trading Journal No 17 – Daily Game Moments. Everyday Trading For Profits

January 3, 2021 DAILY Game Moments

January 3, 2021 at 03:36PM

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