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Algorithmic vs. Quantitative Trading: Which Path Should You TakeI’ve always wondered why anyone would stick to traditional trading methods when algorithms and mathematical models could do all the heavy lifting. I started questioning everything: • Why do so many mentors still swear by discretionary trading when algorithms could handle all the heavy lifting? • Do they really have solid proof of their “own” success, or is it just talk? • Or are they keeping things complex and discretionary on purpose, to confuse people and keep them as members longer? • Why deal with the stress of emotions and decisions when an algorithm can take care of it all? • Imagine how much further ahead you could be if you stopped wasting time on manual trades and instead focused on market research and developing your own models. When I first got into trading, I thought Algorithmic Trading and Quantitative Trading were basically the same thing. But as I dug deeper, I realized they’re two completely different worlds. Algorithmic Trading: It’s simple – you set the rules and the algorithm executes the trades. No more sitting in front of the screen “controlling your emotions” and trying to manage every little detail. Instead, you let the algorithm handle it, based on the rules you’ve set. It frees up your time to focus on other things rather than staring at price charts all day. But here’s the thing – it’s not perfect. You’ll still need to test the rules to make sure the data and results you’re getting aren’t overfitted or just random. Quantitative Trading: A whole different level. It’s not just about executing trades; it’s about understanding the data and math behind market movements. You analyze historical price, economic, and political data, using math and machine learning to predict the future. But it can be complex – techniques like Deep Learning can turn it into a serious challenge. The upside? This is the most reliable way to trade, and it’s exactly what over 80% of hedge funds do. They rely on quant models to minimize risk and to outperform the market. So, which path should you choose? Quantitative Trading can feel overwhelming at first, I recommend starting with the basics. Begin with Pine Script coding in TradingView—start building a foundation with simple strategies and indicators. As you grow more confident, start coding your own ideas into rules and refining your approach to eventually automated your trading strategy. TradingView is a great tool for this, and I’d highly suggest grabbing the Premium plan. This will give you access to more data and features to make your learning journey smoother. Dive into the Pine Script documentation , and begin bringing your ideas to life. I promise, the more you focus on this, the better and more independent you’ll become in trading. Every day, aim to get just 1% better. To Your success, Moein
COINBASE:ETHUSDEducation
by Fractalyst
1
$BNB LONG. Bossco Algo caught every $BNB bullrun. BINANCE:BNBUSDT long entry has been in play. Bossco Algo caught every BINANCE:BNBUSDT bullrun. Pity that TV took down my old post since it referenced an outside URL where entries are called in real time ... Model Architecture: • 1,000+ hours of quantitative research. • 1,000+ machine hours of backtesting & forward testing. • Based on pure price action, zero bias, zero emotions (see methods tested 👇) • Long & Short, Execution on 4H timeframe All methods tested: Why share? • It's my model, so I get the model signals first. I'll already be positioned in my longs, so I don't really care if you enter or not. Hedge fund PMs literally have dinners where they talk their own book after positioning. • Signals are on a high timeframe on liquid assets, so you should be able to get in at the same price. You can't stop hunt me, because I don't post stop losses. I will never give away the code or the techniques selected . No one gives away proprietary quant models that actually work. Please don't ask. I don't plan on ever making signal access paid, since I want a public record of proof that the signals are real. I make my money through trading, not scam discords or courses. Model output is for research purposes only. Not financial advice.
Long
by BosscoQuant
$LINKUSDT LONG. Bossco Algo caught every $LINK bullrun BINANCE:LINKUSDT long entry has been in play. Bossco Algo caught every BINANCE:LINKUSDT bullrun. Pity that TV took down my old post since it referenced an outside URL where entries are called in real time ... Model Architecture: • 1,000+ hours of quantitative research. • 1,000+ machine hours of backtesting & forward testing. • Based on pure price action, zero bias, zero emotions (see methods tested 👇) • Long & Short, Execution on 4H timeframe All methods tested: Why share? • It's my model, so I get the model signals first. I'll already be positioned in my longs, so I don't really care if you enter or not. Hedge fund PMs literally have dinners where they talk their own book after positioning. • Signals are on a high timeframe on liquid assets, so you should be able to get in at the same price. You can't stop hunt me, because I don't post stop losses. I will never give away the code or the techniques selected . No one gives away proprietary quant models that actually work. Please don't ask. I don't plan on ever making signal access paid, since I want a public record of proof that the signals are real. I make my money through trading, not scam discords or courses. Model output is for research purposes only. Not financial advice.
Long
by BosscoQuant
$ETH LONG. Bossco Algo caught every $ETH bullrun. BINANCE:ETHUSDT long entry has been in play. Bossco Algo caught every BINANCE:ETHUSDT bullrun. Pity that TV took down my old post since it referenced an outside URL where entries are called in real time ... Model Architecture: • 1,000+ hours of quantitative research. • 1,000+ machine hours of backtesting & forward testing. • Based on pure price action, zero bias, zero emotions (see methods tested 👇) • Long & Short, Execution on 4H timeframe All methods tested: Why share? • It's my model, so I get the model signals first. I'll already be positioned in my longs, so I don't really care if you enter or not. Hedge fund PMs literally have dinners where they talk their own book after positioning. • Signals are on a high timeframe on liquid assets, so you should be able to get in at the same price. You can't stop hunt me, because I don't post stop losses. I will never give away the code or the techniques selected . No one gives away proprietary quant models that actually work. Please don't ask. I don't plan on ever making signal access paid, since I want a public record of proof that the signals are real. I make my money through trading, not scam discords or courses. Model output is for research purposes only. Not financial advice.
Long
by BosscoQuant
$DOGE LONG. Bossco Algo captured every $DOGE bullrun. BINANCE:DOGEUSDT long entry has been in play. Bossco Algo caught every BINANCE:DOGEUSDT bullrun. Pity that TV took down my old post since it referenced an outside URL where entries are called in real time ... Model Architecture: • 1,000+ hours of quantitative research. • 1,000+ machine hours of backtesting & forward testing. • Based on pure price action, zero bias, zero emotions (see methods tested 👇) • Long & Short, Execution on 4H timeframe All methods tested: Why share? • It's my model, so I get the model signals first. I'll already be positioned in my longs, so I don't really care if you enter or not. Hedge fund PMs literally have dinners where they talk their own book after positioning. • Signals are on a high timeframe on liquid assets, so you should be able to get in at the same price. You can't stop hunt me, because I don't post stop losses. I will never give away the code or the techniques selected . No one gives away proprietary quant models that actually work. Please don't ask. I don't plan on ever making signal access paid, since I want a public record of proof that the signals are real. I make my money through trading, not scam discords or courses. Model output is for research purposes only. Not financial advice.
Long
by BosscoQuant
$SOL LONG in play. Bossco Algo captured every $SOL bullrun. BINANCE:SOLUSDT long entry has been in play. Bossco Algo caught every BINANCE:SOLUSDT bullrun. Pity that TV took down my old post since it referenced an outside URL where entries are called in real time ... Model Architecture: • 1,000+ hours of quantitative research. • 1,000+ machine hours of backtesting & forward testing. • Based on pure price action, zero bias, zero emotions (see methods tested 👇) • Long & Short, Execution on 4H timeframe All methods tested: Why share? • It's my model, so I get the model signals first. I'll already be positioned in my longs, so I don't really care if you enter or not. Hedge fund PMs literally have dinners where they talk their own book after positioning. • Signals are on a high timeframe on liquid assets, so you should be able to get in at the same price. You can't stop hunt me, because I don't post stop losses. I will never give away the code or the techniques selected . No one gives away proprietary quant models that actually work. Please don't ask. I don't plan on ever making signal access paid, since I want a public record of proof that the signals are real. I make my money through trading, not scam discords or courses. Model output is for research purposes only. Not financial advice.
Long
by BosscoQuant
1
thenexxtrade Alpha portfolio Annualized Return : 17.9% Sharpe Ratio : 1.27 Sortino Ratio : 2.67 Max DD : -11.4% Volatility : 10.5% In October 2023, we want to update you on our current investment strategy. We've been maintaining an 80% cash position over the past few months, a decision you can verify by visiting our Twitter account. Our approach involves rebalancing on a monthly basis, steering clear of shorter time frames that often resemble gambling. We're integrating the returns generated by our proprietary system, compounding our gains with the alpha it produces. Watch this space. We will bark when it time to allocate 100%. Those who said don't time the market are those without the Alpha.
AMEX:SPY
by nexxtrade
Understanding the Profitability of Trading.In the trading world, there are various methods to make money. However, in the fundamental approach to trading itself to earn money, we need to know that it has to be when you win bigger and lose smaller. Then, you might think about whether you should focus on winning more often or bigger. This article explores how to assess the profitability of the trading system with a simple formula. Winning Rate and Risk-Reward Ratio Two crucial factors affecting a trading signal's profitability are the winning rate and the risk-reward ratio. The winning rate is the percentage of trades that turn a profit. A high winning rate can mean steady profits, but it doesn't guarantee overall profitability. The risk-reward ratio is the relationship between potential gains and losses in a trade. A higher risk-reward ratio can lead to bigger profits, but it also demands a higher rate of correctness to stay profitable. How They Impact Profitability Let's use a simple example to understand how these factors affect profitability. Imagine a trading signal with a 60% winning rate and a risk-reward ratio of 2:1. This means for every $1 risked, the potential profit is $2. If applied to 100 trades with a $100 risk per trade, the total risk is $10,000. Winning 60 trades would yield $12,000 in profit while losing 40 would result in a $4,000 loss. The net profit in this case would be $8,000. This illustrates how the winning rate and risk-reward ratio impact a trading signal's profitability. Evaluating Signal Effectiveness To gauge the effectiveness of a trading signal, you need to consider a few factors. Firstly, backtest the signal using historical data to assess its performance(Minimum 100 trading signals). Ensure the backtesting period covers various market conditions. Secondly, forward test the signal using real-time data to evaluate its live performance. Finally, assess the signal's consistency and reliability over time(Real-time experience is different from backtesting). Signals that consistently generate profits are more reliable. Key Aspects for Profitability When assessing a trading signal's profitability, focus on a high winning rate and a favorable risk-reward ratio. The signal should adhere to sound trading principles, avoiding subjective factors for consistency and reliability. The formula for Winning Rate and Risk-Reward Ratio Here are simple formulas to calculate the winning rate and risk-reward ratio: Winning rate = (Number of winning trades / Total number of trades) x 100 Risk-reward ratio = (Average profit per trade / Average loss per trade) The higher these values are, the greater the potential for earning money through trading. However, having high values in isolation isn't enough for profitable trading. Let's delve into a few examples to fully grasp this concept: Example 1: Consider Person A , who has a stellar winning rate of 90%, yet a risk-reward ratio of a meager 0.1. Now let's ask, will this individual amass wealth or rack up losses? With his current risk-reward ratio, when luck favors, Person A walks away with a hefty $1,000. However, when faced with defeat, he incurs a significant loss of $10,000! Their trading pattern? Win 9 times with a total earnings of $9,000, only to lose a much larger sum of $10,000 later. While this strategy may appear profitable in the short-run, it's unsustainable in the long-run, often resulting in accumulating losses. Example 2: Now, let's consider another scenario. Person B , with a winning rate of only 10% but a remarkable risk-reward ratio of 8. The same question arises - will this person amass wealth or rack up losses? With their risk-reward ratio, when placed in favorable circumstances, Person B earns an impressive $8,000. Conversely, a bad day costs them a $1,000 loss. Now, their trading pattern may seem paradoxical. They win once, earning a massive $8,000, then lose 9 times in a row for a total loss of $9,000. Despite the high-risk reward ratio, the low winning rate fails to buoy their profits. Ultimately making it a non-profitable strategy. Determining Profitability To assess whether you can make money based solely on the winning rate and risk-reward ratio, consider a formula called the required winning rate. It's defined as: Required Winning Rate = 100 / (Risk-Reward Ratio + 1) Let's look at two examples: Person A has a 90% winning rate and a risk-reward ratio of 0.1. The required winning rate is 100 / (0.1 + 1) = 90.91%. Person A needs to maintain a winning rate of 90.91% to break even. Person B has a 10% winning rate and a risk-reward ratio of 8. The required winning rate is 100 / (8 + 1) = 11.1%. Person B must achieve an 11.1% winning rate to avoid losses. This formula helps determine the minimum winning rate needed to profit based on a strategy's risk-reward ratio. It emphasizes that both factors are critical for trading success. If the required winning rate isn't met, it may be wise to pause and further study the markets. Assessing the profitability of a trading signal is essential for successful trading. Consider the winning rate, and risk-reward ratio, evaluate the signal's effectiveness, and emphasize key aspects. Use the required winning rate formula to understand the minimum winning rate needed for profitability. By making informed decisions, traders can improve their chances of consistent profits over time. Follow and Boost for your financial success ! Write your thoughts in the comment section.
by Wealthy_Stats
How much should the order amount be in quantitative trading ?First, you need to determine how your strategy calculates the order quantity, which can be based on: 1. Quantity of shares 2. Amount of money 3. Percentage This article elaborates on the points of using "Fixed Order Amount" . The amount of margin required for a trade depends on your risk tolerance. Using "BOT | Trend" as an example, In the backtested performance, a fixed "initial capital leveraged by 1x" is used as the order amount for each trade, with a maximum drawdown of 25%, meaning the assets decrease by 25% from the "peak performance point" to the subsequent lowest point (1000 ➡️ 750). Therefore, there are two key points to note here: * The amount of margin required should consider “How much risk you can bear? ” Assuming you currently have 1000 to operate "BOT | Trend," and you can tolerate a maximum loss of 500 (-50%), then the total amount of each trade (margin * leverage) can be set as 2000, and so on. Example: Now you have 2000, and you can tolerate a maximum loss of 400 (-20%), then the total amount of each trade (margin * leverage) is 1600. Practice: Now you have 5000, and you can tolerate a maximum loss of 2000 (-40%), then the total amount of each trade (margin * leverage) is ______ (Hint: What is 25% of 2000?). * Timing to start running quantitative trading. Running a "trend-following" quantitative trading strategy should not start during a continuous profitable period but rather when the strategy incurs losses (relative low point of equity). This is because for trend strategies, sideways market conditions can cause the strategy to go long at highs and short at lows, resulting in a depletion of funds during this period. Starting during a continuous profitable period is likely to encounter fund depletion right after entering because markets alternate between trending and ranging phases. Answer: 8000
Education
by khlin712
ETHUSDT Trading Plan---X--- : Potential reversal square : demand and supply The analysis is based on ORDERFLOW. The trend structure in H4 is bull , so we are going to entry long positions before becoming bear . We could identify some demand and supply zones by orderblock regarded as a large-scale buy or sell zone . It was mentioned above , the trend is a going up , therefore we will hardly entry short positions , ALWAYS FOLLOW THE TREND . There are 2 opportunities to trade Long : 1675.74 SL : 1653.23 TP : 1886.46 RR=9.36 Long : 1632.18 SL : 1610.31 TP : 1886.46 RR=11.63 if price goes to the yellow line before reach our limit orders, the trades should be cancelled .
Long
by khlin712
Quant: Sleepy 😴Quant is feeling a bit sleepy lately and doesn't want to leave the pink target zone - well, let's hope he wakes up soon to climb above the resistance at $228.30 to finish the pink wave . If our beloved coin continues to hit the Snooze-button and sinks further into the pink zone, our alternative scenario will be activated, as soon as the course drops below the support line at $94.08. In that case, Quant should sink to the lower area of the zone to complete the pink wave alt. .
BINANCE:QNTUSDLong
by MarketIntel
A Macro Nerd, Technical Analyst, and a Quant walk into a Bar.And all you get is this chart and no punchline. Not any ordinary chart because this chart has everything you will ever need to actively manage investments in any liquidity environment. 1. You need a 20D moving average with 2 Std Deviation Bollinger Bands. 2. Add 3 of my new Delta/Gamma Indicators locking in on 3 of the markets biggest index hedges. 3. Add a tad bit of technical analysis Just look at the history of the FED in 2020. Then again in Fall 2018 And again in 2016 Total Gamma Exposure for the market has been stuck to zero gamma the past 2 days consolidating / distributing. That means distribution for the days overall move will be smaller < 1. The 2 smaller Equity Funds (EF) are negative gamma and the big EF is positive gamma. The exact same scenario occurred in June when JHEQX flipped positive gamma on the 2 smaller EFs. Volatility Compression with Short Squeeze Potential between now and CPI on the 13th. With Elons financing pulling out of the Twitter deal, I assume Musk is selling TSLA each day again. After AM/EU selloff, small short squeezes up or slightly down to end the day. When he is done, I expect we’ll get a brief pop up to and maybe over the 20 Day moving average which was not tested yet. This kind of price action is described well with Diamond Pattern. Really hard to call this CPI, but I'm going to lean towards the FED CALL/SECOND LEG DOWN trend line down to hold for now until Jerome Powell Pivots on future rate hikes.
SP:SPX
by SPYvsGME
3