The launch of Cardano’s [ADA] much-awaited Mithril mainnet is drawing closer, the protocol recently hinted.
ADA’s price surged as much as 25% following the news of Ripple [XRP] securing a partial victory in its legal battle with the U.S. Securities and Exchange Commission (SEC) on 13 July. But it has dropped quite a few cents since then.
The U.S. District Court of the Southern District of New York ruled in its judgement that the sale of Ripple’s XRP tokens on crypto exchanges and though programmatic sales did not constitute investment contracts; hence, it is not a security in this case. But the court also ruled that the institutional sale of the XRP tokens violated federal securities laws.
Cardano founder Charles Hoskinson praised Ripple CEO Brad Garlinghouse on Twitter.
In a separate tweet, Hoskinson praised the XRP community, writing, “Well done, XRP.”
When the SEC sued Binance [BNB] and Coinbase [BASE] in early June, the regulator included ADA in its newly classified list of securities. Cardano vehemently dismissed the SEC’s claim that ADA can be viewed as a security.
Besides DeFi and cryptocurrencies, another major development that has grabbed public attention is ChatGPT. ChatGPT is an OpenAI-developed large-scale artificial intelligence (AI) language model trained on enormous amounts of text data. This allows the bot to understand and generate responses to complex queries from the user.
It is a language model whose primary purpose is to generate responses like a human. Although it tries to be accurate, the user must verify the information it generates, because the bot is 100% accurate. It merely mimics a human.
This is an important distinction as it forces the prerogative of the user to fact-check and verify what ChatGPT says. However, its training on the basic use of indicators used in technical analysis appeared sound.
The bot can make logical inferences if presented with data from the indicators and can even analyze multiple indicators to make an overall inference.
The chatbot does not have access to live data, such as current market prices of various assets, nor is it aware of developments on the global stage after September 2021. Yet, it was possible to get its prediction on Cardano [ADA] and Bitcoin [BTC] prices in the coming years, and its answer was intriguing.
Taking ChatGPT’s help in devising a basic intra-day strategy
One can come up with an endless array of strategies to trade on various timeframes using a combination of TradingView indicators. The only limitation is the user’s imagination and familiarity with indicators.
It is unlikely that ChatGPT can come up with predictions based on data for the prices of an asset, such as Cardano. The AI model developed by AMBCrypto, on the other hand, can.
I began with a fairly simple task for GPT–Take the RSI and the moving averages and use them together to generate buy and sell signals for intra-day traders. After a few trial requests, the scope was narrowed down. Buy only when the RSI is above 50 and use the Fibonacci numbers 13 and 21 as moving average periods. Here is the response the bot presented:
And the PineScript code for the same.
I tested the strategy ChatGPT came up with on the Cardano chart. Since the point was to use the bot’s help to generate scalp trade signals, I used the 2-minute timeframe. Here are the results –
The strategy is to buy when the RSI is above 50 and the moving averages are bullish. However, we must note that the exact entry and exit criteria are not clear enough.
Hence, we will modify the entry rules and enter when the price has retested either of the moving averages as resistance or support (for short or long positions) and when the RSI fell below (or climbed above) neutral 50.
As for exit, we shall target an R: R of 2:1, so we need to be successful at least 33% of the time to break even, but more on that later.
A demonstration of this began after the bearish crossover on the 2-minute chart late on 31 March. In total, we had at least seven clear trade signals within nine hours, which cumulatively produced +6.25R. This meant that risking 1% per trade would have given a 6.25% return within ten hours of watching the charts.
It must be stated that many more trades were possible based solely on the rules. Since the trend was shifting, they would have been forced to close to breakeven and could be confusing to decipher for the reader.
Moreover, they would cost trading fees and eat into the scalper’s profit, which is another factor that highlights how dangerous scalping can be.
ChatGPT predicts the price movement of ADA
The bot refuses to venture into the business of predicting crypto prices in future years, even as a fun pursuit. To test the capabilities of the bot, I used a jailbreak method a Reddit user posted in the recent past. Using this, we asked ChatGPT what it thought the price of Cardano would be by the end of 2023.
Looking at the daily price chart
ADA’s price has dropped 4% to $0.3125 within a week.
Let us now look at some of the on-chart indicators of Cardano (ADA). While its Relative Strength Index (RSI) stood above the neutral 50-level, its Money Flow Index (MFI) stood below it. It’s On Balance Volume (OBV) also showed a decline.
It seems that for now, we shouldn’t anticipate a rise in ADA’s value.
Source: ADA/USD, TradingView
It’s here that one should note that besides technical skills, a trader’s experience is of great importance in anticipating a price rally.
So, the question is-
What separates a good trader from a bad one?
It is possible to go on and on taking different indicators together, altering and tweaking their input values, and backtesting their signals. However, we shall move toward risk management. Risk management is what separates a trader from a gambler. It also helps undercut the emotion a trader might feel during a trade. Fear almost always arises when the trader has risked more than they can stomach. This can negatively impact profitability.
Back-testing aside, any profitable trader must be able to limit their losses. Each trader is probabilistically bound to run into a streak of losing trades. Some key elements of risk management ChatGPT identified were diversification, position sizing, stop-loss orders, risk-reward ratio, and risk tolerance.
Diversification is necessary because crypto is a highly volatile market. The assets are, for the most part, positively correlated with Bitcoin. This means that investors could look to allocate only a minority of their funds toward crypto-assets, which would be anywhere from 5% to 50%. Having one’s net worth in crypto is highly risky.
Stop-loss orders are orders placed at levels of invalidation of a trade idea. They are automatically executed and are set up in such a way that the trader exits their losing position if the price reaches a predetermined level. This level can be determined by technical analysis.
The capital lost during that trade would ideally be less than 3% of the entire account size. But why? Why shouldn’t one trade by risking a significant chunk of their account size in each trade?
A bad streak in the markets shouldn’t destroy your trading account
The attached chart reveals that a trader with a 30%-win rate has a 100% chance of having a losing streak of 8 trades within a 100-trade sequence. If the trader risked 10% of their starting account size with each trade and lost eight in a row, they would be down by 80%.
The trading system isn’t broken, but the probability is it will spoil your profits. Trading is not a sprint to the finish line but an excruciating marathon where your biggest enemy is yourself–Fear and greed, in particular.
To survive, the amount of capital risked per trade must be able to withstand a losing streak, which will be based on the win rate. Even if the trades you take are amazing with 3:1 or 4:1 risk-to-reward, it doesn’t do a lot of good in protecting your capital when the market seemingly has your number.
Hence, risking only 1%, or 3% per trade would be far more likely to succeed in the long run. The profits might not be quick, but they will be present. And, the emotional side of trading will also likely lose its intensity since each trade won’t make or break you.
Understanding R: R and calculating when a trader is at break-even
Let’s assume we have an account worth $1000. We are determined to lose no more than 1% per trade, which means each losing trade will only cost $10 or 1% of the total size. Meanwhile, our winning trades could make $20 or $30, or any other amount. The ratio of the capital risked to the reward gained if the trade ran to completion is called risk-to-reward, or R: R. Usually, traders target a 3:1 ratio, meaning they are willing to lose 1% per trade but seek to win 3% of their account size.
A trader will likely not be successful 100% of the time. If they are correct about 30% of the time, they could still be profitable. Even a trader with a 5%-win rate could find himself in prof in the long run. A trader who only places 3:1 RR trades will need to be successful (1-(3/(3+1))*100 i.e. 25% of the time only to break even. Similarly, a trader who only wins 5% of the time would need to place only trades with an RR of 20:1. (1-(x/x+1))*100=5, solving for x, we get 20.
If a trader looks for 3R trades and has a sound reason (Based on technical analysis or fundamental analysis, for example) to place that trade, and they are successful with more than 25% of their trades, then they will be a profitable trader.
It can help to maintain a Trade Journal
Pesky algebra aside, how does a trader track their win rate? The most common solution is a trading journal. This is a ledger where a trader can jot down each trade they place and the insights they learn from it. ChatGPT can help create a basic template for this purpose –
In this template, we see the R: R of the trades taken, their success rates, and the trader’s reasons to enter and exit the trade. Traders can also note their emotions to prevent repeating the same mistakes. They can also use the journal to find an edge in the market for yourself.
This means information about what kind of trade works most often for you. Long or short? If long, could the ones where RSI>50 on both M5 and M15 be another factor you want to check before looking to take long positions since this confluence appears to give your trades more success?
Calculating the capital risked per trade
Implementing a journal can answer these and many such questions. Another tool that ChatGPT can aid in creating is a position size calculator. We have already seen R: we can determine R and the success rate through journals. Let’s try to recall the probability chart presented earlier. Even with a 60%-win rate, there is still a 92% probability that one will see a streak of 4 consecutive losing trades within a span of 100 trades.
Therefore, the requirement would be to risk 1% or 3% or something in between for each trade setup. Calculating this can be time-consuming. I asked ChatGPT to come up with some code to help calculate the position size. It obliged and produced the code. The input prompts would have to be account size, leverage used, risk threshold, and stop loss distance.
Let’s assume an account size is $1000, the risk threshold is 5%, stop-loss percentage distance of trade is 6%. The leverage used is 10x. We calculate the initial margin required as:
Margin = (1000 * 0.05) / (0.08 * 10) = $62.5.
For spot traders, the leverage utilized would be 1x.
Exactly how useful is ChatGPT to professional traders?
I asked Mikaela Pisani, ML Lead and Senior Data Scientist at Rootstrap. She is an expert in big data development and artificial intelligence and her response was,
“Traders can use ChatGPT as a tool to get recommendations on the stock market. It is likely to be most useful for beginner traders, enabling them to learn the fundamentals of stock trading from the chatbot. More advanced traders can use it as a tool for gathering insights and making decisions faster, but there are limitations given the output is based on data provided (currently training data is up to 2021).”
As highlighted earlier in the article, the use of the bot in live trading remains curtailed. But what about the bot’s impact on algorithmic trading?
“Aside from data limitations, which are the primary weakness of ChatGPT for traders, the advantage for traders will be an extremely short window of time as the market absorbs these AI tools to improve efficiency of the market via automation and improved outputs of trading algorithms.
In this way, we can view ChatGPT as likely to have a similar impact to the first High Frequency Trading platforms – yielding a potential advantage for early traders but quickly becoming part of the norm of the market.”
Once again, the lack of access to live data meant ChatGPT will only likely be substantially useful to beginner traders. It is likely to positively impact intermediate trades as well. They can use the bot to figure out how to apply multiple indicators and metrics harmoniously and use it to gain a better understanding of the market.
Regarding Cardano (ADA), ChatGPT predicts a bullish price movement by the end of 2023 even as its on-chart indicators show a downtick.
However, it is important to remember though ChatGPT responds to a human; it is not 100% accurate. Diligent traders must observe on-chart indicators to make their investment decisions.
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