7 Ways to Optimize Your Trading Strategy Like a ProYou’ve got a trading strategy—great. But if you think that’s where the work ends, think again. A good strategy is like a sports car: It’s fast, fun, and dangerous… unless you keep it tuned and under control. And given how volatile modern trading is, yesterday’s strategy can quickly become tomorrow’s account-drainer. So, how do you keep your trading strategy sharp and in profit mode? Let’s dive into seven ways to fine-tune your setup like a pro.
1️⃣ Backtest Like Your Profits Depend on It (Spoiler: They Do)
Before you let your strategy loose in the wild, backtest it against historical data. It’s not enough to say, “This looks good.” Run the numbers. Find out how it performs over different time frames, market conditions, and asset classes — stocks , crypto , forex , and more. If you’re not backtesting, you run the risk of trading blind — use the sea of charting tools and instruments around here, slap them on previous price action and see how they do.
💡 Pro Tip : Make sure to backtest with realistic conditions. Don’t cheat with perfect hindsight—markets aren’t that kind.
2️⃣ Optimize for Risk, Not Just Reward
Everyone gets starry-eyed over profits, but the best traders obsess over risk management. Adjust your strategy to keep risk in check. Ask yourself: How much are you willing to lose per trade? What’s your win-loss ratio? A strategy that promises massive returns but ignores risk is more like a ticking time bomb than a way to pull in long-term profits.
💡 Pro Tip : Use a risk-reward ratio of at least 2:1. It’s simple: risk $1 to make $2, and you’ve got a buffer against losses. Want to go big? Use 5:1 or why not even 15:1? Learn all about it in our Asymmetric Risk Reward Idea.
3️⃣ Diversify Your Strategy Across Markets
If you’re only trading one asset or market, you’re asking for trouble (sooner or later). Markets move in cycles, and your strategy might crush it in one but flop in another. Spread your strategy across different markets to smooth out the rough patches.
💡 Pro Tip : Don’t confuse this with over-trading. You’re diversifying, not chasing every pop.
4️⃣ Fine-Tune Your Time Frames
Your strategy might be solid on the 1-hour chart but struggle on the 5-minute or daily. Different time frames bring different opportunities and risks. Test your strategy across multiple time frames to see where it shines and where it stumbles.
💡 Pro Tip : Day traders? Shorten those time frames. Swing traders? Stretch ’em out. Find the sweet spot that aligns with your trading style.
5️⃣ Stay Agile with Market Conditions
No strategy is perfect for every market condition. What works in a trending market could blow up in a range-bound one. Optimize your strategy to adapt to volatility, volume, and trend shifts. Pay attention to news events , central bank meetings, and earnings reports — they can flip the script fast.
💡 Pro Tip : Learn to identify when your strategy isn’t working and take a step back. Not every day is a trading day.
6️⃣ Incorporate Multiple Indicators (But Don’t Go Overboard)
More indicators = more profits, right? Wrong. It’s easy to fall into the trap of loading up your charts with a dozen indicators until you’re drowning in lines and signals. Keep it simple — combine 3 to 5 complementary indicators that confirm your strategy’s signals, and ditch the rest.
💡 Pro Tip : Use one indicator for trend confirmation and another for entry/exit timing.
7️⃣ Keep Tweaking, But Don’t Blow Out of Proportion
Here’s the rub: There’s a fine line between optimization and over-optimization. Adjusting your strategy too much based on past data can lead to overfitting — your strategy works perfectly on historical data but crashes in live markets. Keep tweaking, but always test those tweaks in live conditions to make sure they hold up.
💡 Pro Tip : Keep a trading journal to track your tweaks. If you don’t track it, you won’t know what’s working and what’s not. Get familiar with the attributes of a successful trading plan with one of our top-performing Ideas: What’s in a Trading Plan?
💎 Bonus: Never Stop Learning
The market’s constantly changing and your strategy needs to change with it. Keep studying, keep testing, and keep learning. The moment you think you’ve mastered the market is the moment it humbles you.
Optimizing a trading strategy isn’t a one-and-done deal—it’s an ongoing process. By fine-tuning, testing, and staying flexible, you can keep your strategy sharp, profitable, and ahead of the curve. Optimize smart, trade smart!
Optimization
Dangerous Lies Your Backtest TellsDangerous Lies Your Backtest Tells
We are easily hooked on the dopamine rush of seeing profitable equity curves during backtesting. The allure of parabolic returns is often so strong it is blinding to the inherent flaws that exist, to varying degrees, in every backtest.
Backtesting, while often seen as an essential step in designing and verifying trading strategies - is far from a foolproof method. Many traders place too much confidence in their backtested results, only to see their strategies fail when used in the live markets. The reality is that backtesting is riddled with limitations and biases that lead to a false sense of security in a strategy’s effectiveness. Let’s take a comprehensive look into the many flaws of backtesting, and explore the common pitfalls of using a simple back test as your only method of verifying a strategy's efficacy.
1. Choosing the Winning Team After the Game is Already Over
(Selection Bias)
When selecting which instruments for backtesting, it is common to choose assets you are already interested in or those that performed well in the past. This introduces selection bias, as the strategy is tested on assets that may have been outliers. While this may produce impressive backtest results, it creates an illusion of reliability that may not hold up when applied to other assets or future market conditions - a theme that will be common for most of the explored backtesting drawbacks.
Example:
Imagine backtesting a Long only strategy using only tech stocks that surged during a market boom. The strategy might look incredibly successful in the backtest, but when applied to other sectors or different market phases it will most likely fail to perform - because the selection was based on past winners rather than a broader, more balanced approach.
2. You Only See the Ships that Make it to Shore
(Survivorship Bias)
Similar to the above, survivorship bias occurs when backtests only include assets that have survived of the test period - excluding those that were delisted, went bankrupt, or failed entirely. This creates a skewed dataset, inflating performance metrics beyond reasonable levels once again. By only focusing on assets that are still around, you overlook the fact that many others didn’t make it - and these failures could have significantly impacted the strategy’s results. By ignoring delisted companies, or rug-pulled crypto projects, you inherently induce a selection bias - as purely because your chosen instruments didn’t go to zero they must have performed better.
Example:
Suppose you backtest a low-cap cryptocurrency strategy. If your backtest spans for, say, five years the test can give the illusion of success - but what’s missing is the hundreds of tokens that were launched and failed during the same period. How can we possibly assume that we will be lucky enough to only pick tokens that survive the next five years?
3. Reading Tomorrow’s News Today
(Look-Ahead Bias)
Look ahead bias occurs when future information is unintentionally used in past decision making during a backtest. This can often occur due to coding errors in an automated system which leads to unreasonable and unrepeatable results. Look-ahead bias isn’t limited to algorithmic backtesting - it can also affect manual backtests. Traders will often miss false signals because they can already see the outcome of the trade. This knowledge of the future can affect the accuracy of a manual backtest - both as a conscious decision by the trader but also subconsciously.
if Current_Price < Tomorrows_Close
strategy.entry("Enter a Long Position", strategy.long)
// An extreme example
4. Perfecting the Final Chord, but Forgetting the Song
(Recency Bias)
Recency bias occurs when traders place too much emphasis on the most recent data or market conditions in a backtest. This usually occurs when a trader feels they missed an opportunity in the past few months - and tries to develop a strategy that would have captured that specific move. By focusing too heavily on recent history, it is easy to neglect the fact that markets usually move in long cyclical phases. This over optimisation for recent conditions will, at best, result in a strategy that performs well in the short term but fails as soon as market dynamics shift.
Example
Frustrated by missing the most recent leg of the bull market, a trader develops a strategy that would have perfectly performed during this period. However, when the trader begins live trading at the top of the market, the strategy quickly fails. It was only optimized for that short and specific market phase and was unable to adapt to the changing market conditions.
5. Forcing the Square into the Round Hole
(Overfitting)
Overfitting occurs when a strategy is excessively optimized for historical data, capturing noise and random fluctuations rather than meaningful patterns. Overfitting is common when traders test too many parameter combinations, tweaking their strategy until it fits the past data perfectly. In contrast to the previous point, this over optimisation can occur on data of any length, whether years or even longer periods.
Example
Adjusting a large range of parameters in a high frequency strategy by incredibly small increments and deciding to use the calibrations that yield the highest performance.
6. Mixing Oil and Water
(Conflating Trend and Mean Reversion Systems)
Traders often attempt to design strategies that perform well in both trending and mean reverting environments, which leads to muddled logic and poor performance in ALL environments. A trend following strategy is meant to capitalize on sustained price movements, and should naturally underperform during mean-reverting or ‘ranging’ periods. In a range-bound market, a trend-following strategy will often buy near the top of the range after detecting strength, only for the price to reverse. Conversely, a mean reversion strategy is built to profit from oscillations around a stable point and forcing both approaches into a single system results in unrealistic backtest performance and poor real-world results.
One of the common mistakes is when a trend following strategy ‘accidently’ performs well during mean-reverting periods. This skews the backtest metrics because any gains during non-trending markets are multiplied significantly during actual trends. As a result, the backtest shows artificially positive performance - but the strategy quickly falls apart in live trading. Normally, a trend following strategy would incur losses during a range-bound market and only begin to recover once a new trend emerges. However, if a strategy is overfit to handle both the trend and mean reversion periods of the past, it doesn’t need to recover losses and instead compounds gains during the entire trend. This creates inflated backtest results that won’t hold up in real trading.
Example:
A trader develops a trend following system that, through over-optimization, performs surprisingly well during mean-reversion phases. In the backtest, the strategy shows strong returns, even in ranging markets. However, in live trading, the system fails, leaving the trader with poor performance. Instead, the trader should have accepted ‘lower’ returns from a strategy that wasn’t overfit - because in live markets robust strategies with mediocre backtests perform better than overfit strategies that only excel in backtesting.
7. Seeing the World Through a Keyhole
(Limited Data Skewed by Outliers)
Strategies built on assets with limited data are highly susceptible to skew results, especially when outliers dominate the dataset. Without sufficient data, it becomes nearly impossible to assess whether a strategy can consistently perform into the future. Some strategies, like trend following, are designed to capture outliers, that is, the periods of performance above the norm. The issue arises when testing on a small sample as it’s difficult to determine if the strategy can consistently capture trends or just got lucky.
Example:
A trader develops a trend following strategy for a cryptocurrency that has recently launched. The backtest shows massive gains, as it is common for projects to make large returns as soon as they are listed. However without enough data history, it is impossible to assess the actual effectiveness of this strategy, as its performance metrics are positively skewed by the ‘listing pump.’
The image shows a cryptocurrency project launched in October 2020. At first glance, the EMA Crossover strategy appears profitable, but a closer look reveals that most of the profit comes from the first trade, which is considered an outlier. If that trade was removed, the strategy as a whole would become unprofitable. Following this strategy is essentially betting on the project to experience another sharp rise similar to what occurred in 2020. While technically this isn’t impossible, it is much riskier - a more proven and verified strategy would increase your probability of success.
8. Designing a Car that Doesn’t Fit on the Road
(Execution Constraints and Positions Sizing)
In backtesting, real world constraints such as minimum or maximum order sizes are often ignored, leading to unrealistic trade execution. Traders may find that they either don’t have enough capital to satisfy the minimum order size - either immediately or after a small drawdown. Additionally, compounded returns on a backtest can lead to absurd positions sizes that could never be bought or sold in the real market. This particularly is more problematic for deep backtestests.
Example:
A backtest shows spectacular growth, with the account size ballooning overtime and resulting in an extremely high profit percentage. However, in real-word conditions, the required position size to continue executing the strategy becomes so large that it exceeds the liquidity of the market - making it impossible to receive comparable profit percentages on real world trading.
9. Death by a Thousand Paper Cuts
(Not Accounting for Fees, Commissions and Slippage)
When performing a backtest, traders often overlook critical transaction costs such as fees, slippages and spreads. These seemingly small costs can accumulate and significantly erode profits, especially strategies that rely on frequent trades with a low average return per trade. Slippage also should include execution slippage - the time delay between receiving a signal from a system, placing an order and its execution. This is particularly problematic for lower timeframe trading where even minor delays can drastically swing a strategy from profitable to unprofitable
Example:
A day trader runs a backtest on a scalping strategy and sees parabolic returns. However in live trading, the small profits from each trade are wiped out by broker commissions, spreads and the slippage that occurs from both position sizing, and when trades are executed slightly later than expected. This strategy, while successful in the backtest, failed to account for the ‘death by a thousand paper cuts.’
10. Filling Half of the Grocery Cart
(Partial Order Fills)
In low liquidity environments, or when trading large position sizes, partial order fills are common - meaning traders only get a portion of their order executed at their desired price. This can significantly impact returns. Backtests will usually assume complete fills at the exact target price. However, in reality a trader experiencing a partial order fill must decide whether to complete the position at a worse price or leave a portion of the target position size out of the market. Both choices will lead to results that are not comparable to the backtested results.
Example:
A trader places a limit order to buy 100 shares of a low-liquidity stock at a price of $10. The order is only partially filled, with 60 shares bought at $10, while the remaining 40 shares require the new, higher price. The trader now faces the choice of paying more, or leaving part of the trade out. This is a major deviation from the backtest, which assumed the complete position was bought at $10.
11. Betting on Lightning Striking Twice
(Black Swan Events)
Black swan events are rare, inherently unpredictable, and have a significant impact on financial markets. Strategies designed to avoid drawdowns during these events are at risk of being overfit. Traders often fall into the trap of building systems that avoid drawdowns during past black swan events - overfitting their strategies to these rare occurrences. These strategies are unlikely to succeed in regular market conditions and contain no extra edge in protecting a trader from future black swans events.
Example:
After the FTX collapse caused a sharp drop in crypto prices, a trader chooses to develop a swing trading strategy designed to avoid all losses during this event. However, by optimizing the strategy to exit positions before the collapse, the trader unintentionally overfits it. As a result, the strategy begins to sell off positions too early in other situations, cutting profits short. Prior to the FTX collapse, the market was still in an uptrend, and there were no clear signs of an impending downturn - so attempting to optimize for such a rare event ends up compromising the strategy’s performance in more typical market conditions.
12. Expecting a Weeks Pay After Only Working One Shift
(Time of Day and Day of Week Restrictions)
Many traders are only able to trade during specific hours or days of the week, yet their backtests often include data from periods where they are unavailable - such as overnight sessions. This creates an unrealistic expectation of returns. For example, in markets like crypto that trade 24/7, backtesting a day trading strategy on the full market period gives a false impression of potential profits if you can only trade during certain hours. Additionally, market participants also differ depending on the time of day, as entire countries wake up and go to sleep at different times of day. One could make the assumption that human behavior as a whole might be the same, but the number of participants and liquidity will definitely change.
Example:
A day trader backtests a strategy using 24/7 crypto market data - but is only able to trade on weekday afternoons due to other commitments.
13. Siphoning Gas from a Moving Car
(Capital Drain and Addition)
Backtests frequently assume infinite compounding, where no capital is ever added or withdrawn from the trading account. In practice, however, traders will regularly add or remove funds - which significantly impacts the performance of a strategy. For instance, withdrawing money during a drawdown forces the strategy to work harder to recover losses, as it now requires higher returns to break even. Similarly, adding capital can skew results by altering position sizing. While it is necessary to manage capital in this way, backtests usually don’t account for these changes and once again, leads to results that are not repeated in practice.
Example:
A trader consistently pulls a portion of profits from their account each month. In the backtest, no withdrawals are considered, and the strategy appears highly profitable. However, in live trading these regular withdrawals put pressure on the account, and especially over longer periods of time, this reduced level of compound will lead to significant underperformance relative to the backtest due to the reduced compounding effect on returns.
14. Your Subscription Service Increase Price Without You Realizing
(Interest Rates and Funding Costs)
The ‘cost of capital’ - such as leverage costs, interest rate and funding fees - can fluctuate over time, but backtests often overlook these dynamic costs or even fail to account for them altogether. In live markets, these changes can significantly erode profit margins. Not considering these costs, especially the factors affecting their variability, can easily turn a profitable backtest into an unprofitable strategy in live trading.
Example:
A trader backtests a strategy for use in cryptocurrency perpetual futures. The strategy is designed for bull markets but fails to account for the rising funding rates frequently seen during periods of high demand. As the cost to maintain an open position skyrockets, the trader’s profit margins quickly shrink, making the strategy far less viable than the backtest indicated. This is particularly dangerous because as the funding fees erode the position’s margin, the liquidation price rises faster than expected, potentially resulting in the entire position being liquidated - even though the trade appeared profitable on paper.
15. You Can’t Ride the Wave Past the Shore
(Alpha Decay)
In highly competitive markets, especially in high-frequency trading, the edge of a strategy (alpha) can erode over time as more participants exploit similar inefficiencies. This gradual loss of profitability - known as alpha decay - often isn’t captured in backtesting, which assumes static market conditions. Alpha decay is particularly relevant in high-frequency trading, where competition and frontrunning are more intense, while it tends to be less of an issue in higher time-frame swing trading.
16. Playing Chess Against Yourself and Expecting to Win Every Time
(Psychological Factors)
Psychological biases still affect fully systematic traders. The assumption that traders will follow their strategy without hesitation or emotional interference rarely holds true in live trading, especially during periods of drawdown or high volatility. Manual and automated traders alike feel the same compulsion after experiencing drawdown. The temptation to tweak or abandon a strategy during this period is strong and often leads to the worst decision. It is well documented anecdotally that many traders find that after modifying a ‘losing’ strategy, the new version performs worse than the original, as it has been adjusted to avoid the losses of the past and misses future gains by virtue of overfitting.
Example:
An algorithmic trader watches as their automated strategy experiences a significant drawdown. Panicking, the trader tweaks the parameters in order to avoid further losses. Shortly after, the original strategy would have recovered, but the modified version continues to struggle as the adjustments were made in reaction to short term losses instead of accounting for long term performance.
Final Note:
Congratulations if you made it this far! This might not be the most exciting topic, but it’s essential knowledge for every trader and investor. This article was written to warn you of the dangers of relying on backtests - and provides a checklist of common pitfalls to watch out for. Whether you’re running your own backtest or reviewing someone else’s, it’s critical to look beyond the shiny numbers and assess the real-world viability. What looks great on paper may not hold up in the real world.
Best of luck in the markets - but remember: stay prudent, and you’ll make your own luck!
Wait for get profit in buy OP)❤️❤️Thanks for boosting 🚀 and supporting us!
📈support in first candle pollback.
📊 (Buy-sell) : 2.898
🔴 Stop Loss : 2.727
🎯 Take Profit : 3.092-3.315-3.357
🔗 For more communication with us, In the footnote and send a message in TradingView.
👨🎓 Experience and Education: Our trading team has five years of experience in financial markets, especially cryptocurrencies.
3 Must-haves for Top TradersStats published by exchanges clearly show that 80% of traders lose money. So the inquiry is not “how to get into the top 20%?” Who wants to settle for mere profitability? The inquiry becomes “how do I get into the upper 3% of traders?” - The “fly around in private jets” and “build whole schools in Africa” traders?
The answer is simple. Stop being human. It’s our human emotions that trip us up every time. We close winning trades too early (take the money and run) and hang onto losing trades too long (pray for a miracle).
Top traders (the ones with private jets) have one thing in common. They all know how to code. And they automate their trading to take the human element out of it. So here’s your quick list of must-haves skills and tools to invest in. Your best investments to get you into the upper 3%. The journey from staring at your computer doing live Technical Analysis to sitting on the beach while your bot trades for you.
Get Tradingview Premium. If you’re serious about making money, you need the ability to back-test. The deep-backtester that comes with a premium level account is going to give you the accurate results you need. Trading code that hasn’t been properly back-tested and optimized is a game of russian roulette. The money you make from trading well tested strategies will pay for this investment ten-fold. Which brings me to my third and final point, optimization.
Master Pine Script. Shout-out here to Matt Slabosz who developed Pine Script Mastery - an excellent overview course for beginners. Matt is not only a solid trader and exceptional coder, his true brilliance is being able to explain complex coding concepts in simple terms. Given that the last time I’d coded was punching fortran cards in University, taking his course was a pleasant surprise in how easy and addictive learning to code Pine Script can be.
Optimize your strategy. There are solid strategies published on tradingview that look unprofitable at first glance. That’s simply a matter of knowing the correct settings for the derivative and market conditions you’re trading. So you have two choices.
Sit at your computer all night and run scenarios on the deep back-tester, or
Buy an optimization tool that will run scenarios while you sleep.
Being way past the age where I’m willing to give up my sleep, the answer, for me, is a no-brainer. TradingTools.software is my top pick for optimizing your TradingView strategy. It allows you to pinpoint the weak areas of your strategy down to the window of where the biggest losses are occuring. This information is pure gold to creating strategies to filter against market conditions where your automated strategy would otherwise fail.
Learning the skills and buying the tools you need is critical to mastering any profession. Trading is no different.
What Are The Best Indicator Settings & Timeframes?Timeframes and technical indicator settings are ubiquitous concepts to technical analysts, two things that they will have to interact with at some point in point. For certain traders, they make part of the million-dollar questions:
"What is the best timeframe to use?"
"What are the best indicator settings to use?"
Where "best" refers to the timeframe/settings that lead to the most profits. Both questions are very interesting and very difficult to answer, yet traders have tried to answer both questions.
1. What Is The Best Timeframe To Use?
Timeframes determine the frequency at which prices are plotted on a chart and can range from 1 second to 1 month. We can notice that price charts tend to be similar to each other from one timeframe to another, having the same irregular aspect and the same patterns, this explains the fractal nature of market prices, where shorter-term variations make up of longer-term variations found in a higher time-frame.
Based on this particularity, methods used to determine the start/end of a trend can be the same regardless of the selected time-frame, as such traders could choose a time-frame based on the trend they want to trade, for example, daily/weekly timeframes could be used to trade primary trends while other could use intraday timeframes to trade intraday trends, note that it is still possible to trade a specific trend by using any time-frame you want, however using a timeframe that is too low for trading long term trends might result in an excess of parasitic information while using a high timeframe for trading short term trends will result in a lack of information.
It is important to note that lower time-frames will return price change of lower amplitude, as such trading the variations of a lower timeframe will make a trader more affected by frictional processes, particularly frictional costs, as such trading lower time-frames aggressively might require more precision, which is why beginner traders should stick with higher time-frames.
So "the best timeframe to use" should be chosen based on the trend the trader wants to trade, with a timeframe giving the right amount of information to trade the target trend optimally. Your target trend will depend on your trader profile (risk aversion, trading horizon...etc).
1.1 Multi Timeframe Analysis
Some traders might use multiple timeframes, such practice is called multi-timeframe analysis and consists of getting entries in a certain time-frame while using the trend of a higher timeframe for confirmation. There are various methods in order to choose both timeframes, one consisting of choosing a timeframe such that the trend of the lower one is an impulse of the trend of the higher timeframe.
2. Best Technical Indicators Settings
When using technical indicators, reducing whipsaw trades often introduce worse decision timing, finding settings that minimize whipsaw trades while keeping an acceptable amount of lag is not a simple task.
Most technical indicators have user settings, these can be numerical, literal, or Boolean and allows traders to change the output of the indicator. In general, the main setting of a technical indicator allows making decisions over longer-term price variations, as such traders should use indicator settings in order to catch variations of interest like one would do when selecting a timeframe, however, technical indicator settings often allow for a greater degree of manipulation, and can have a wider range of values, as such setting selection is often conducted differently.
2.2 Indicator Settings From Optimization
When using technical indicators to generate entry rules it is common to select the settings that yield the most profits, various methods exist in order to achieve optimization, certain software will use brute force by backtesting a strategy for every indicator setting. It is also possible to use more advanced procedures such as genetic algorithms (GA).
GA's are outside the scope of this post but simply put GA's are a search algorithm mimicking natural selection and are particularly suitable for multi-parameter optimization problems. When using a GA the setting is as genes in a
chromosome.
Such a selection method has some limitations, the most obvious being that optimal settings might change over time, rending useless the process of optimization. Optimization can also take a large amount of time when done over large datasets or when using a large combination of indicator settings, it might be more interesting to analyze the optimized settings of a technical indicator over time and try to find a relationship with market prices.
2.3 Dominant Cycle Period For Setting Selection
Certain technical analysts have made the hypothesis that the dominant cycle period should be used as a setting for technical indicators instead of a fixed value, this method can be seen used a lot in J. Elhers technical indicators. Most technical indicators using the dominant period as a setting are bandpass filters, which preserve frequencies close to the dominant one.
There are several limitations to such a selection method, first, it depends a lot on the accuracy and speed of the dominant cycle period detection algorithm used, the noisy nature of the price makes it extremely difficult to measure the dominant period accurately and in a timely manner, in general, more accurate methods will have more lag as a result. Another downside is that it is not a universal solution, technical indicators can process market price differently.
3. Conclusion
From the two questions highlighted at the start of this post the one involving technical indicators remains the most challenging one to answer, which is often the case with "what is the best..." kind of questions. What is certain is that there isn't a universal setting for each indicator, certain settings might be more adapted to specific market conditions (such as ranging or trending conditions), and the presence of a setting in itself will always mean that interaction will occur at some point, as such recommending an indicator setting or timeframe must be done with a significant rationale.
The problem of the best technical indicator settings offer a great challenge for any technical indicator developer, but it is important for the common traders to lose some focus about them, while important, these should not be adjusted in opposition to your trader profile , having a well-defined trader profile will help you adjust these settings more effectively, as such a reasonable answer to "what are the best timeframe/indicator settings?" could be "the ones that are adapted to your trader profile".
Profitable RSI optimizes 3 parameters!Well, it's just a small public announcement.
I went to this for a long time and now it has become possible. Profitable RSI now handles 3 parameters of the standard RSI indicator to find the best tuple of settings. So, additionally to period setting, the optimizer takes under consideration different Overbought (from 60 to 70 ) and Oversold levels (from 30 to 40 ) for each RSI period.
Four main conclusions from my research (if you gonna trade with RSI):
The OB/OS levels are not necessary to be the standard 70/30 ones. With all my respect to J. Welles Wilder, but those bounds cannot be considered optimal.
The OB/OS levels can be asymmetric. So OB can be 65 while OS is 39. Not 70/30, not 60/40 and not 75/25. Asymmetric ones.
There is no efficient trading with period setting higher than 50.
We can make a feast even from the old indicator
And the last thing I wanted to add - let's not live in the old paradigms. The world is changing, trading is changing and we must change too. Don't be afraid to experiment with something new for you.
The tool I talked about, the Profitable RSI, is here
Good luck, Good health, God bless you
