Bitcoin and Crypto Market Updates (May 8th)

Bitcoin and Crypto Market Updates (May 8th) 512 288 Crypto Rand Group

Bitcoin Network Broken & Binance is blowing up?

Typical Monday morning stuff from crypto… So let’s dive in.

So bitcoin is facing some congestion issues and this seems to be driven by BRC-20 Tokens. A BRC20 token is basically a Bitcoin equivalent of an ERC20 token – So yes, memecoins and other tokens are being launched on the Bitcoin network… and this is causing some congestion.

Right now the BRC20 market cap consists of 14,000 tokens and has an MC of nearly 1Bn$ https://brc-20.io/

Most of these transactions are happening on a service called ‘’Unisat’’ – they seem to dominate the BRC20 space for now. https://unisat.io/

The total volume in this market over the last 24 hours stands at 60M$ – So still very small.

Im still reviewing the buying/minting process to get into some of this stuff so I’ll put an update out this afternoon but from what I gather so far it’s a costly process (around $400 minimum entry and this is paid in mint fees)

So this is why we’re seeing network congestion and it’s a concern but at the same time it is driving usage and mining fees are rising so this incentivises more mining = stronger / more decentralisation.

Now onto Binance – The massive 180k Bitcoin in ‘’withdrawals’’ I keep seeing on my Twitter feed is simply just binance moving wallets and pushing out Bitcoin lightning more aggressively to help accommodate transactions during high periods of congestion.

PEPE Memecoin reached a $1.8 Billion market cap in just 3 weeks

Pepecoin (PEPE), a cryptocurrency memecoin has experienced an almost 5,000,000% rise in the past few weeks, making it one of the most remarkable increases in altcoin history. However, PEPE tokens are down nearly 45% since setting a peak of $0.00000431 on Friday, reaching a market capitalization of $1.8 billion just over three weeks since issuance in mid-April. This decline is possibly due to traders taking profits on their positions or using advanced trading strategies, and they may be converting these profits into ether (ETH), which has hit an all-time high in deposits to exchanges since November 2021. Some traders have converted small investments into generational fortunes, making millions in just a few days after investing in Pepe coins.

Binance halt Bitcoin withdrawals… twice in 12 hours

Binance, a leading cryptocurrency exchange, temporarily suspended Bitcoin withdrawals for the second time in 12 hours due to a backlog of pending transactions. Binance explained that its set fees did not anticipate the recent surge in Bitcoin network gas fees, causing pending transactions. The exchange updated its status to indicate it was replacing the pending withdrawal transactions with higher fees to be picked up by mining pools. Binance also reported significant Bitcoin net negative outflows on May 7 but claimed it was just moving Bitcoin between its hot and cold wallets. The price of Bitcoin has fallen about 3.5% from its weekly high of over $29,700 as of May 6.

SEC have 10-day deadline to respond to Coinbase

Coinbase has won a legal victory in its bid to force the Securities and Exchange Commission (SEC) to provide clear rules on which digital assets are securities and how securities laws apply to them. The Third Circuit of the US Court of Appeals has given the SEC 10 days to respond to a rulemaking petition by the cryptocurrency exchange, which was filed in July 2022. Coinbase had taken the SEC to court after the agency failed to respond to the request. The exchange argued the lack of rules made it impossible for the crypto market to operate efficiently.

North Carolina ban CBDCs

The North Carolina House of Representatives has passed a bill that would prohibit state government entities from accepting payments made in central bank digital currencies (CBDCs). The bill passed its second reading unanimously, with two absentee votes. North Carolina now joins Florida in opposing CBDCs, and the US Department of the Treasury is currently outlining the technology required to develop a digital dollar. However, proponents argue that anti-CBDC legislation may unintentionally harm innovation in digital assets, namely Bitcoin. The bill must now pass a state Senate vote before it can be signed into law by Governor Roy Cooper.

The world’s richest man owns NFTs

Bernard Arnault, the world’s richest man and CEO of LVMH, has revealed that he owns a collection of NFTs despite exhibiting a “tepid” attitude towards the technology in the past. Arnault’s sons have shown considerably greater interest in NFTs, with Alexandre Arnault once touting a jewel-laden Cryptopunk Tiffany pendant and brother Frédéric Arnault, CEO of Tag Heuer, showing off his enthusiasm for Web3 tech with NFT display functionality on the luxury watch brand. Arnault’s own NFT collection was revealed by his former advisor and chief digital officer of LVMH, Ian Rogers, who said Arnault had shown him his OpenSea page.

Unlock the Power of AI to Improve Your Trading

Artificial intelligence (AI) has been a game-changer in many industries, and trading is no exception. AI can analyze vast amounts of data and identify patterns and trends that humans might miss, making it a powerful tool for traders. In this article, we’ll explore how you can use AI to improve your trading and even a few steps on how to implement a trading bot that follows a trading strategy and respects risk management.

Using AI to Analyze Data

One of the most significant advantages of AI in trading is its ability to analyze large amounts of data and identify patterns and trends. By using AI algorithms, traders can process vast amounts of information from multiple sources, including news articles, social media, and financial statements. This information can be used to predict market movements and identify potential trading opportunities.

For example, AI-powered trading bots can use natural language processing (NLP) algorithms to analyze news articles and social media posts to detect sentiment and identify market-moving events. These bots can then make trades automatically based on this information, what is known as algo trading.

Implementing a Trading Bot with Risk Management

To get started with implementing a trading bot, you’ll need to define your trading strategy and risk management plan. Your trading strategy should include the entry and exit criteria for your trades, while your risk management plan should define how much capital you’re willing to risk on each trade.

Once you have defined your trading strategy and risk management plan, you can use an open-source AI engine like TensorFlow or PyTorch to train your trading bot. You’ll need to provide your bot with historical data to learn from, which you can get from public APIs like Alpha Vantage.

Next, you’ll need to program your bot with your trading rules and risk management plan. Your bot should be programmed to enter trades automatically based on your trading strategy and to exit trades if they reach your predetermined risk level. You can use Python and the Backtrader library to code your bot and incorporate your risk management plan.

Here’s an example of how to implement a stop-loss order in your trading bot using Python and the Backtrader library:

class MyStrategy(bt.Strategy):
    def _init_(self):
        self.stop_loss = 0.95 # set stop loss at 5% loss
        self.position_size = 0.1 # allocate 10% of account balance to each trade
    def next(self):
        if self.data.close[0] < self.data.close[-1] * self.stop_loss:
           self.sell(size=self.position_size)

In this example, the MyStrategy class defines a stop loss level of 5% loss (self.stop_loss = 0.95) and a position size of 10% of the account balance (self.position_size = 0.1). In the next method, the code checks whether the current price is below the stop loss level (if self.data.close[0] < self.data.close[-1] * self.stop_loss) and sells the asset if it is (self.sell(size=self.position_size)).

Backtesting and Optimization

After you’ve implemented your trading bot, you’ll need to backtest it using historical data to evaluate its performance. Backtesting involves running your bot through historical data to see how it performs under different market conditions.

You can use the Backtrader library to backtest your bot and evaluate performance metrics such as the total return, Sharpe ratio, and maximum drawdown. These metrics will help you identify areas for improvement and optimize your bot for better performance.

Conclusion

In conclusion, AI can be a powerful tool for traders looking to improve their performance and efficiency. By using AI to analyze data and even defining your trading strategy and risk management plan, programming your bot with your rules, and backtesting and optimizing its performance, you can create a powerful tool that can help you achieve your trading goals.

In summary, AI can help traders make better decisions analyzing relevant data, identifying trends and patterns, and automating trades. By following the steps outlined in this article, you can see the potential of creating a trading bot that follows your trading strategy and risk management plan, improving your chances of success in the market.

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