time-tested forex strategies
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Time-tested forex strategies forex strategy 10 points

Time-tested forex strategies

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When you place an order through such a platform, you buy or sell a certain volume of a certain currency. You also set stop-loss and take-profit limits. The stop-loss limit is the maximum amount of pips price variations that you can afford to lose before giving up on a trade. Many come built-in to Meta Trader 4. However, the indicators that my client was interested in came from a custom trading system. They wanted to trade every time two of these custom indicators intersected, and only at a certain angle.

The start function is the heart of every MQL4 program since it is executed every time the market moves ergo, this function will execute once per tick. For example, you could be operating on the H1 one hour timeframe, yet the start function would execute many thousands of times per timeframe. Once I built my algorithmic trading system, I wanted to know: 1 if it was behaving appropriately, and 2 if the Forex trading strategy it used was any good.

In other words, you test your system using the past as a proxy for the present. MT4 comes with an acceptable tool for backtesting a Forex trading strategy nowadays, there are more professional tools that offer greater functionality. To start, you setup your timeframes and run your program under a simulation; the tool will simulate each tick knowing that for each unit it should open at certain price, close at a certain price and, reach specified highs and lows.

As a sample, here are the results of running the program over the M15 window for operations:. This particular science is known as Parameter Optimization. I did some rough testing to try and infer the significance of the external parameters on the Return Ratio and came up with something like this:. You may think as I did that you should use the Parameter A.

Specifically, note the unpredictability of Parameter A: for small error values, its return changes dramatically. In other words, Parameter A is very likely to over-predict future results since any uncertainty, any shift at all will result in worse performance. But indeed, the future is uncertain!

And so the return of Parameter A is also uncertain. The best choice, in fact, is to rely on unpredictability. Often, a parameter with a lower maximum return but superior predictability less fluctuation will be preferable to a parameter with high return but poor predictability. In turn, you must acknowledge this unpredictability in your Forex predictions.

This does not necessarily mean we should use Parameter B, because even the lower returns of Parameter A performs better than Parameter B; this is just to show you that Optimizing Parameters can result in tests that overstate likely future results, and such thinking is not obvious. This is a subject that fascinates me. Building your own FX simulation system is an excellent option to learn more about Forex market trading, and the possibilities are endless.

The Forex world can be overwhelming at times, but I hope that this write-up has given you some points on how to start on your own Forex trading strategy. Nowadays, there is a vast pool of tools to build, test, and improve Trading System Automations: Trading Blox for testing, NinjaTrader for trading, OCaml for programming, to name a few. Here are a few write-ups that I recommend for programmers and enthusiastic readers:.

Forex or FX trading is buying and selling via currency pairs e. Forex brokers make money through commissions and fees. Forex traders make or lose money based on their timing: If they're able to sell high enough compared to when they bought, they can turn a profit. Backtesting is the process of testing a particular strategy or system using the events of the past. Subscription implies consent to our privacy policy. All of these factors will tell them how strong the current trend is and when the market may be primed for reversal.

Even when a market is trending, there are bound to be small price fluctuations that go against the prevailing trend direction. For this reason, trend trading favors a long-term approach known as position trading. When investing in the direction of a strong trend, a trader should be prepared to withstand small losses with the knowledge that their profits will ultimately surpass losses as long as the overarching trend is sustained. For obvious reasons, trend traders favor trending markets or those that swing between overbought and oversold thresholds with relative predictability.

All moving averages are lagging indicators that use past price movement to lend context to current market conditions. In addition to providing insight into the current trend direction and strength, moving averages can also be used to gauge support and resistance levels. Rather than anticipating the direction of the reversal and entering into a new position, trend traders will use these signals to exit their current position.

Once the new trend has manifested, the trader will once again trade in the direction of the current trend. Price momentum will often change before a price change occurs, so momentum indicators, such as the stochastic oscillator and relative strength index RSI , can also be used to help identify exit points. These indicators help traders identify when price is approaching overbought or oversold levels and provide insight into when a change will occur.

As such, it tends to be a more reliable and consistent strategy. Although you may not be the first one to enter the trade, being patient will ultimately shield you from unnecessary risk. Forex trading strategies come in all different shapes and sizes, so before you jump into any of them, we highly recommend you test-drive them first.

Position trading is a strategy in which traders hold their position over an extended time period—anywhere from a couple of weeks to a couple of years. As a long-term trading strategy, this approach requires traders to take a macro view of the market and sustain smaller market fluctuations that counter their position.

Position traders typically use a trend-following strategy. They rely on analytical data typically slow moving averages to identify trending markets and determine ideal entry and exit points therein. They also conduct a fundamental analysis to identify micro- and macroeconomic conditions that may influence the market and value of the asset in question.

To lock in profits at regular intervals and thereby mitigate potential losses , some position traders choose to use a target trading strategy. Range trading is based on the concept of support and resistance. On a price action graph, support and resistance levels can be identified as the highest and lowest point that price reaches before reversing in the opposite direction. Together, these support and resistance levels create a bracketed trading range.

In a trending market, price will continue to break previous resistance levels forming higher highs in an uptrend, or lower lows in a downtrend , creating a stair-like support and resistance pattern. In a ranging market, however, price moves in a sideways pattern and remains bracketed between established support and resistance thresholds.

When price reaches the overbought resistance level, traders anticipate a reversal in the opposite direction and sell. Finally, if price breaks through this established range, it may be a sign that a new trend is about to take shape.

Range traders are less interested in anticipating breakouts which typically occur in trending markets and more interested in markets that oscillate between support and resistance levels without trending in one direction for an extended period. Range traders use support and resistance levels to determine when to enter and exit trades and what positions to take.

Trading the dips and surges of ranging markets can be a consistent and rewarding strategy. Because traders are looking to capitalize on the current trend rather than predicting it, there is also less inherent risk. That said, timing is exceptionally important. Oftentimes, an asset will remain overbought or oversold for an extended period before reversing to the opposite side. To shoulder less risk, traders should wait to enter into a new position until the price reversal can be confirmed.

As a multinational marketplace, forex is influenced by global economic events. Understanding economic news events and their potential impact on currency pairs helps traders anticipate short-term intraday or multiday market movements, or breakouts. No one event is inherently more important than another. Instead of focusing on one variable, traders examine the relationship between them in tandem with current market conditions. News traders rely on economic calendars and indexes such as the consumer confidence index CCI to anticipate when a change will occur and in what direction price will move.

Trading small breakouts that occur over a short time period has high profit potential. Of course, it also carries greater risk. When price consolidates, volatility increases. Getting in early is part of the game, but getting in too early can be reckless. More experienced traders will often wait for confirmation of the breakout before acting on a hunch.

Swing trading is a trend-following strategy that aims to capitalize on short-term surges in price momentum. These smaller surges and dips may go against the prevailing trend direction, and thus require a more limited market outlook examining minute, hourly, daily, and weekly price charts as opposed to analyzing overall market trends.

Despite being classified as a short-term trading strategy, this approach demands that traders hold their position overnight unlike day trading and may keep them in a trade for a few weeks at a time. This strategy relies on both technical and fundamental forms of analysis.

On the technical side, traders use momentum indicators and moving averages to analyze price movement over multiple days. From a fundamental standpoint, swing traders often use micro- and macroeconomic indicators to help determine the value of an asset. Swing trading anticipates rapid price movement over a wide price range—two factors that suggest high profit potential. But greater potential profits naturally come with greater risk.

Price momentum can change rapidly and without warning, so swing traders must be prepared to react immediately when momentum changes. To mitigate the risks of holding their position overnight, swing traders will often limit the size of their position. Although a smaller position size curbs their profit margin, it ultimately protects them from suffering substantial losses.

Scalping is an intraday trading strategy in which traders buy and sell currency with the goal of shaving small profits from each trade. In forex, scalping strategies are typically based on an ongoing analysis of price movement and a knowledge of the spread. When a scalper buys a currency at the current ask price, they do so under the assumption that the price will rise enough to cover the spread and allow them to turn a small profit. In order for this strategy to be effective, however, they must wait for the bid price to rise above the initial ask price—and flip the currency before price fluctuates again.

Oftentimes, scalpers will hold professional trading accounts with brokers to access lower spreads. Their success also hinges on their use of a low-latency platform that is capable of executing multiple trades at a time with speed and precision. To determine what position to take, scalpers use technical analysis and pattern recognition software to confirm trend direction and momentum, locate breakouts and divergences, and identify buy and sell signals in their target period.

Like other day traders, they may also track economic events that are likely to impact short-term price movement. But handling such a large volume of trades also comes with its own challenges. For any trader, managing more than one trade adds complexity to the process. In such a volatile, fast-moving market, the stakes are amplified.

Succeeding as a day scalper demands unwavering concentration, steady nerves, and impeccable timing. If a trader hesitates to buy or sell, they can miss their already limited profit window and dwindle their resources. Day traders earn their title by focusing solely on intraday price movements and capitalizing on the volatility that occurs therein.

These small market fluctuations are related to current supply and demand levels rather than fundamental market conditions. Day traders use a variety of short-term trading strategies. Some trade the news using economic calendars and indexes and change their focus based on global economic events.

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Indicator Forex strategies are such trading strategies that are based on the standard Forex chart indicators and can be used by anyone who has an access to some charting software e. These FX strategies are recommended to traders that prefer technical analysis indicators over everything else:.

Price action Forex strategies are the currency trading strategies that do not use any chart or fundamental indicators but instead are based purely on the price action. These strategies will fit both short-term and long-term traders, who do not like the delay of the standard indicators and prefer to listen as the market is speaking. Various candlestick patterns , waves, tick-based strategies, grid and pending position systems — they all fall into this category:. Fundamental Forex strategies are strategies based on purely fundamental factors that stand behind the bought and sold currencies.

Various fundamental indicators, such as interest rates and macroeconomic statistics, affect the behavior of the foreign exchange market. These strategies are quite popular and will benefit long-term traders that prefer fundamental data analysis over technical factors:.

It is very important to test your trading strategy before going live with it. There are two ways to test your potential trading strategy: backtesting and forward testing. Backtesting is a kind of a strategy test performed on the past data. It can be either automated or manual. For automated backtesting, a special software should be coded.

Automated testing is more precise but requires a fully mechanical trading system to test. Manual testing is slow and can be rather inaccurate, but requires no extra programming and can be done without any special preparation process. Any backtesting results should be taken with a grain of salt as the tested strategy might have been created to fit particular backetsting historical data. Forward testing is performed either on a demo account or on a very small micro live account.

During such tests, you trade normally with your strategy as if you were trading your live account. As with backtesting, forward testing can also be automated. In this case, you would need to create a trading robot or expert advisor to execute your system. Of course, with discretionary strategy, you are limited solely to manual testing.

Forward testing results are considered to be more useful and representative than those of the backtests. Regardless of how you decide to test your strategy, you need to understand the results you get. Intuitively, you would want to judge the results according to strategy's profitability, but you should not forget about other important parameters of successful trading strategies.

They are: low drawdown sizes, short drawdown periods, high probability of winning, high average reward-to-risk ratios and big number of trades. Ideally, your system should earn equally well on bullish and bearish trades, the resulting balance curve should be consistent and uniform, without significant drops or long flat periods.

If you are using MetaTrader for backtesting or forward testing, you can use our report analysis tool to better understand the strong and weak sides of your strategy. If you want need information on currency trading strategies or need some additional examples of working strategies, you are welcome to browse our e-books section on strategies to learn from completely free downloadable e-books.

You may also choose to read some articles from our strategy building section to improve your knowledge of the subject. See, before Warren Buffett started to make his billions, one of the businesses he ran involved setting up pinball machines in barber shops.

Many times people have to wait in line before they can get their hair done, which can be quite boring. Young Buffett saw a business opportunity in this. In , Buffett and his friend Danley bought a pinball machine for 25 dollars and installed it in a barber shop. Now, instead of getting bored, customers can play pinball while they wait. This business was a hit from day one. The first night, Buffett and Danley made 4 dollars, and after one week, they had already made 25 dollars in total.

They reinvested this amount into the business, bought another pinball machine, and installed it in another barber shop. Then, the profit made from two machines was reinvested to buy 2 more machines. They kept reinvesting the profit, and soon, Buffett had pinball machines in barber shops all over Washington, D. Young Buffett ended up selling the business, which he started with just 25 dollars, for over dollars after a year.

But then I remembered the Turtle Traders and their trading rules, and started to question if my method of compounding was even right? You see, in the previous episode, we saw how multi-millionaire Richard Dennis taught random traders how to make millions.

These random traders were called turtle traders and they together ended up making more than million in around 5 years. Which method of compounding is right? Which one makes more money? And does reducing the position size when there is a loss even make sense? To find out, I ran 5 experiments, took trades, and simulated trades. The data you are about to see is not only surprising, but you will most likely end up using the best compounding method we have found.

In the first experiment, we will test if reducing the position size when there is a loss is a good move or not. On one side, we will simulate trades with no adjustment to position size when there is a loss. On the other side, we will simulate trades, but this time we will reduce the position size when there is a loss. As soon as I started the simulation, the account balances of these two accounts started to fall like it was a market crash.

The 1st experiment shows us that reducing the position size when there is a loss and when the account gets smaller is actually a good thing. But what if your win rate is good? Then what? Will reducing the position size when there is a loss even make sense since we are not going to lose money in the long run?

To find out, I simulated trades with a 55 percent approximate win rate on two separate accounts. The first account will compound when there is a profit, but will not reduce the position size when there is a loss. On the other hand, the second account will compound when there is a profit, but will also reduce the position size when there is a loss. As soon as I started the simulation, the account balances of these two accounts started to move in the upward direction. The second account however was slightly better than the first one.

In the second experiment, to find the best compounding method, I opened the Trading Rush Website, selected the trading strategy that got the highest TR Score as well as the highest win rate after trades. Then, I took trades again on the exact same market structure 3 times. The third time, I compounded every time there was a profit and reduced the position size every time there was a loss. In other words, with the highest win rate trading strategy, compounding and adjusting the position size after every trade was the best compounding method.

But what if, the results were too good because of the high win rate strategy? To make sure the data is correct, in the 3rd experiment I used the worst trading strategy we have ever tested times. This strategy after trades got a 33 percent approximate win rate with a 1. Since the win rate is so low, all three adjustment methods in the third experiment should make a loss. But, if our previous data is correct, and compounding and adjusting position size after every trade is actually the best method, then the same method should also make the lowest amount of loss with a bad strategy.

And in the third experiment, it did. The account with no adjustment method made the highest amount of loss. And the account that compounded and adjusted the position size after every trade had the lowest amount of loss. After looking at all of this data, we can safely say that we have found the best compounding and adjustment method, but unfortunately, not everyone has the same win rate in trading.

The win rate in trading also depends on your Market View, on how good you are at identifying the good vs bad markets. Even the Trade Alerts I gave on Patreon had a higher win rate than this. In the fourth experiment, I simulated trades with no compounding on one side, and trades with compounding and adjustment after every trade on the other side.

But what if your win rate is even worse? With a 1. There is clearly a big difference. But after 5 experiments, we have enough data that says compounding and adjusting position size after every trade is not only better but adjusting position size when there is a loss significantly reduces the probability of blowing up the account.

So I will change my compounding method as well. I hope you learned something. Get trade alerts, see how I take high probability trades by supporting Trading Rush on Patreon. Thanks for watching. Read more. Tags best , buffett , compound , compounding , experiment , illusion , re-investing , reinvest , reinvesting , trading , trading strategies , trading strategy , warren , warren buffet , warren buffett.

I would say the one magic wish most traders would like to be granted, would be to be able to see into the future. Once Dennis and Eckhardt had shared their proprietary trading concepts, Turtle Traders were only allowed to trade for Richard Dennis and were not allowed to trade futures for themselves or others. Each student received 1 million to trade. Learning from a multi-millionaire and trading with his money?

It was like winning a lottery. However, when the two weeks class started, one of the first topics the Turtle Traders were taught was not about the money-making strategy, it was about Managing Risk. You see, Dennis understood probabilities and used calculated risks to his advantage. The strategies Turtle Traders were going to learn were high-risk strategies.

There was no buying low and selling high, it was a high-risk high reward. So it was important for the traders to understand risk management more than anything else. Turtle Traders were also taught to disassociate the dollars from trading. But if the amount lost in dollars is high, then emotions kick in, and bad decisions are made. Turtle Traders were taught to consider losses in percentages and not in amounts of money.

Dennis and Eckhardt wanted their students to see trading as a probability game. Dennis since his early twenties believed that looking at the news for stock tips was not a good idea. If acting on the news was the real key to success in trading, everyone would be rich. He wanted Turtle Traders to make their decisions by looking at the price directly.

In simple words, Dennis would make money by holding long and short futures positions simultaneously. Turtle Traders on the other hand were trained to be trend-following traders. This shift in strategy was mainly influenced by Richard Donchian, who was a well-known trend trader with a positive record from the s to the s. After losing money in the market crash of , Richard Donchian started studying Technical Analysis.

The rule was pretty simple: When the price breaks above the high of the previous two weeks, you close your short position and buy. When the price breaks below the low of the previous two weeks, you close your long position and sell short. He also developed the popular Donchian Channel Indicator, and its trading strategy was one of the best strategies we have ever tested times on the Trading Rush Channel. It ranks 2nd from the top on the TR Score chart.

However, they still make money in the long run by taking trades in the direction where the price is already heading. Turtles were also trained to keep things simple, but Dennis and Eckhardt knew there will be mistakes and missed entries. If turtle traders made mistakes on regular basis, their probability of losing money, in the long run, will be high.

So one of the main concepts Turtles were taught was to know their edge in the market. See, trading is a zero-sum game. You either have to win more times than the other person to be profitable, or need winning trades many multiple times larger than the losing trades.

If you win more trades, you can risk more on a single trade and still have a good probability of making money in the long run. If you risk more on a single trade and have a big losing streak, you will blow up the account. The idea was to catch the start of a strong trend and stay in that trend for as long as possible.

For example, if the price gives a day breakout in the upward direction, or in other words, if the price makes a move higher than the highest price of the previous 30 days, the Turtle Traders would buy. To exit a long position, Turtles had to wait for a breakout in the opposite direction. For example, price breaking below the 15 days low. If the price gives a day breakout in the downward direction, or in other words, if the price makes a move lower than the lowest price of the previous 30 days, the Turtle Traders would go short.

To exit a short position, Turtles had to wait for a long breakout. For example, price breaking above the previous 15 days high. The exact Turtle Trading Strategy is now protected by copyright laws, but basically, it was a breakout strategy that tried to take a position at the start of a new trend, no more complex than the strategies most traders use today. Turtles were made very clear that if two traders with the same account size were facing the same situation in the market, they both should take the same optimal course of action.

They both should place the same trade. Basically, Eckhardt and Dennis wanted their students to understand that they are not special and definitely not smarter than the market. They did not want Turtles to make decisions because they felt smart or lucky. Since Turtles were told to exit trades when there was a breakout in the opposite direction, they had to watch 10 to 50 percent of their unrealized profit disappear before the exit signal appeared.

This made sticking to the rules challenging for some. But those who followed the rules remained in the experiment and made huge profits in the long run. In the next 5 years, the Turtle Traders with the Breakout Strategy ended up making more than million dollars in total. Several Turtle Traders after the experiment have gone on to have professional careers in trading. Richard Dennis and Turtle Traders showed that with the right mindset, with proper rules, proper money management, anyone can be profitable in trading.

I used the MACD strategy that got the highest win rate, the Volume Weighted Average Price strategy, and took trades near strong support and resistance areas with pretty much the same trading concepts taught by Richard Dennis to Turtle Traders:.

Trade with the trend! We want to book more profits than the loss. We want to capture bigger profits when we are right about the direction and book smaller losses when we are wrong. We want to know our edge in the market. Trading is a probability game. We want to know how many times we can lose in a row, so we can manage risk and not blow up the account. Remember, one of the first things Turtle Traders were taught was Risk Management.

And most importantly, we want to use the KISS principle as much as possible. Navy in The KISS principle states that most systems work best if they are kept simple rather than made complicated. Get Trade Alerts, see how I take high probability trades with the current best strategies by supporting Trading Rush on Patreon. Thanks for Watching!

Tags million , million , strategy , traders , turtle , turtle strategy , turtle trader , turtle traders , turtle trading , turtle trading strategy. Would you do me a favor? This is exactly what happened with many traders in February of In February , the price was at an all-time high, and many traders were looking for a discount.

Many of these traders, are in-experienced and are most likely to lose money. I would like you to meet some of these traders and watch very carefully, how an event or a chain of events that follows have a big impact on their lives. The price is near the period moving average, and Bill is about to take a long trade using margin. At the same time, Emma is doing research on the market. She discusses the stock market with her friends, and fears, when a random person on social media, says the stock market is about to crash.

Both of these people, are in-experienced in this field and have little idea about what information is right, and where to get it from. Both want to buy the stock market index, in the hope to make a decent profit. Bill buys because he thinks the price is at support, but Emma, decides to take advice from the television, and fears it might be a crash. By the end of March , the stock market index, was down more than 30 percent from the high. Many traders lost a lot of money, and people like Bill, who went long with margin, blew up their accounts.

Since margin is like a loan from the broker, in the wrong hands, margin trading can be quite devastating. By the time Bill had lost everything, Emma had done a little bit of research and was ready to take a position in the market. Since she is not an experienced trader, she too ends up taking a long position, with a high margin.

After April , the Stock Market Index starts moving in the upward direction with good momentum. By December , the price was already up by almost percent. If she had taken the long trade, just 30 days earlier, she would have blown up the account like Bill. Just one month, made a big difference, on how they are going to look at trading in the future. Even if there was no margin used while taking the trades, both Bill, and Emma, would have been sitting with a profit.

Since Emma bought late, at a better discount, she would have ended up with almost a percent gain. This raises an interesting question, and it was also asked on the Trading Rush Discord Server. If buying late can make more money, why not take every trade late?

According to the strategy, if we want to take a long position, we have to wait for the long MACD crossover. We have to set the stop-loss below the pullback of the trend, and use a 1. But instead of waiting and taking trades when the long MACD crossover happens, why not wait, for the price to move in the opposite direction a little after the entry signal? Taking the entry late like this will obviously result in a smaller stop-loss, but that will turn the 1.

See in trading, the reward risk ratio, and win rate, are inversely related. This means, if you increase the reward risk ratio, the win rate will go down, and if you decrease the reward risk ratio, the win rate will go up. Since the MACD strategy, with the 1. However, the counterpoint was, the price after the entry, goes in sideways, and in the opposite direction many times, and on top of that, there is also market noise.

So by waiting for the price, to move in the opposite direction after the entry, we will run the risk of missing out on some excellent trades, but if the price, moves in the opposite direction, we get a chance, to make 4 times more profit, than the risk. And then I moved the profit target closer, to make it a 1. But all of them will have different profit potentials. Which one of these entry methods, will make more money in the long run?

To find the answer, I am going to take trades, with each entry method. By looking at the reward risk ratio, you probably have guessed which one of these methods will give a higher and lower win rate. With a higher reward risk ratio, comes a lower win rate. And because of that, we can even guess, the number of winners and losers in a row, the strategy can have.

And because of that, we can guess, how much percentage of our account, we can risk per trade, to be profitable in the long run. If the win rate is low because the reward risk ratio is high, the strategy obviously, is going to have a bigger losing streak. Unfortunately, not everyone can handle losing multiple trades in a row.

When a strategy is having 10 losers in a row, believing in that strategy, and the long-term picture can become difficult for many beginners. For them, a higher win rate strategy is more suitable, and you get that higher win rate, by using a smaller reward-risk ratio. And we did. Other 34 trades reached the profit target before the price moved in the opposite direction. And that resulted in the highest number of losing trades in a row. Ask yourself, can you handle losing 20 trades in a row?

For most of us, the answer is no. A smaller losing streak is much more preferable. If they both made pretty much the same amount of profit, is the high reward risk ratio, really worth it, when it makes you sit through a big losing streak? We can see that the main strategy, not only performs better when it comes to the win rate but also makes way more profit in the long run. In this experiment, it made almost 2 times more money!

Now ask yourself, which one of these strategies, and reward risk ratios would you like to use? As for me, the answer is the smaller reward risk ratio, so I can win trades more frequently, with a strategy, that has a high probability of making money in the long run. Thanks for watching! Tags trades , best , best reward risk , best stop , profit , profit illusion , profit target , ratio , reward , reward risk , reward risk ratio , risk , stop loss , stoploss.

This time I took more than trades in 48 hours so you can get a high win rate in trading. What I am going to do is take 2 trades every minute for 48 hours straight. Since 2 trades every minute is a lot of trades, I am going to use the trading bot we made in one of the previous videos. In the first experiment, I am going to tell the trading bot to use the period moving average to find the trend, and then at every 60 seconds, take 1 trade in the direction of the trend.

Set a tight profit target so the trade is won easily, and set the stop loss 10 times farther. Basically, use a 10 to 1 risk-reward ratio. I told it to do this on 1 account first, and repeat the strategy on the second account. The theory is if what I am about to show you can actually increase the win rate of a trading strategy, the first account should perform better than the second account and should get a higher win rate. How do I know the first account will perform better?

I will show you by the end of this video. I will start the first experiment, and you keep an eye on the profit loss number as the trading bot takes new trades every minute. I gave a trade alert on Trading Rush Discord Server earlier to go short on this pair, so I am expecting the price to move in the downward direction on the good chart in the long run. Now 1 hour has passed, and this time the bad market is the one with more loss.

Interesting how 30 mins ago the bad market was sitting with a profit, but then it lost dollars quickly. If we look at the two accounts, we can see that the good market has made less loss in the long run. It lost so much money that it ran out of margin to take new trades with the same position size. On the other hand, the first good chart account had more money and was still taking trades. Our theory matches the practical results, but there is one thing I overlooked before starting this experiment.

You see, I told the trading bot to take trades in the direction of the trend and set 10 times bigger stop loss. But in reality, the 10 times bigger stop loss never got triggered even once. If you have a long position open and you send a new short order, you close the running long position. But even though we got the results just like we anticipated, the good account performed better than the bad account, it could have been just a fluke.

If you want to make sure something works, try it again and see if you get the same results. In the second experiment, we are going to take 2 trades every 1 minute, but this time only in a downtrend. Another 24 hours have passed, and what I am going to do is plot the balance on these 4 accounts using a chart and you will see something interesting. The total balance of the two accounts in the first experiment kept going in the downward direction. Obviously, this is because we used a bad trend trading strategy.

But just like we anticipated, the second account performed worse than the first. And we know this was not just a fluke because, in the second experiment, the same thing happened. The first account performed better and the second account performed worse. But how did I know beforehand that the first account will get a higher win rate both times?

These are the two charts I am going to run the experiments on and what we are going to do is draw over the price movement. You see, the price in the forex and stock market moves like this. A zig-zag pattern that is either moving up, down or in the sideways direction.

If you are using a range market strategy, then you want the price to make this kind of zig-zag pattern. Now one might think, both of these charts are trending. The first one is trending in the upward direction and the second is trending in the downward. So in theory, if we use a trend trading strategy on these two charts, we should get a good and similar win rate. Most of the time the market is ranging and choppy. So instead of trading charts that are trending now, it is better to trade charts that will also trend for the most part in the future.

How exactly do you find that? Before starting the experiment, to see if the chart has a higher probability of ranging or trending for the most part, I first opened the entry timeframe. In this case, it was the 1 min timeframe since we were taking trades every 1 minute. Then I checked what kind of zig-zag pattern the price was making on the entry timeframe and made sure the direction of the price movement is not sideways.

And then I checked what kind of zig-zag pattern is price making on the higher timeframe. Till now, there was not much difference between the two charts on the entry timeframe, but if we draw on the price movement on the higher timeframe, you will see something interesting. The price movement on the two charts no longer looks similar. The zig-zag pattern on the first chart still looks like it is moving in the up or down direction, but the zig-zag pattern on the second chart is clearly moving in the sideways direction.

The price has a higher probability of moving in the direction of the higher timeframe trend. If the trend direction of the higher timeframe, or in other words, if the long-term price movement direction is sideways, the price movement on the smaller timeframe will also be sideways for the most part. If we go back to our entry timeframe and look at the price movement between the start and end of our experiments. You will notice that the chart that had a one-sided zig-zag pattern on the higher timeframe, also had a one-sided price movement on the smaller timeframe for the most part.

Whereas, the chart that had a sideways movement on the higher timeframe, also had a sideways and choppy price movement for the most part. In the stock market, when the price moves in the upward direction, people say the bulls have control over the market. When the price moves in the downward direction, they say bears have control over the market.

But when everyone has a mixed and no one-sided market view, the price stays flat as we see in the second chart. The chart where the higher timeframe market view was flat, got a lower win rate. Although these random trades showed us the data we were looking for, what I am going to do in the third experiment, is take the highest probability trades for 24 hours straight. If everything I talked about till now is correct, the win rate I should get after 24 hours should be high and the profit graph should look like this.

To find trades more frequently, I am going to use a smaller timeframe like 5 min as the entry timeframe, and then use a higher timeframe like 30min to see if the price movement is worth trading or not. Basically, I am using the period moving average on the entry timeframe to find the trend direction, and I am making sure the zig-zag pattern on 30 minutes higher timeframe is not moving in the sideways direction.

On this pair, the higher timeframe is not moving in the sideways direction, but the entry timeframe is. Moving Averages are lagging indicators, they show everything late. In this case, I know the price has started ranging because the price has stopped making new lower lows. The price on this pair is good. The entry timeframe is trending in the downward direction and the higher timeframe is not moving in the sideways direction. Also, the price is near weak resistance.

If you have seen the Live Trade Analysis Posts on Patreon or other videos on the channel, you know I also use the moving average on the higher timeframes as well. But to keep things simple in this experiment, I am only using it on the entry timeframe. Since this chart is good, I am going to take a short trade with a reward risk ratio of 1. The trade will look something like this. We check back on this trade at the end of this video.

On this pair the price on the entry timeframe is extremely good, but if you look at the higher timeframe, you will see that it is not exactly moving in one direction. There can be also situations like this, where the price on both entry and higher timeframe is bad.

The price is moving in the sideways direction on the entry timeframe and has sudden big choppy moves on the higher timeframe. This is not something we want. This chart is just like the previous one. The entry timeframe is ranging and the higher timeframe has sudden big moves.

This chart is an interesting one. The price on the entry and higher timeframe is moving in one direction, but the quality of the trades on this chart will be low. A good clean price movement looks like this, but on this chart, the price movement is like this. On these kinds of charts, the probability of losing the trade increases, because when the price moves like this, the indicators become less reliable.

So I am going to avoid this chart even though the entry timeframe pullback looks like a good discount. Nothing is happening on this pair. The higher timeframe is ranging and the entry timeframe is not in a good trend. This chart is interesting. The price on the higher timeframe is moving in the upward direction.

It is not exactly a good trend, but it is better than a ranging market. But on the entry timeframe, the price is moving in the sideways direction. Looks like the price broke below the period moving average. But, I can see that the price found support from this area before. And as we have seen in the previous video, if the price found support from an area before, it can do it again. So what I am going to do is take a support resistance trade. In this case, a long trade at a support area.

Something like this. We check back on this trade later. On this chart the price movement is excellent, but right now there is nothing to trade. We will wait for the price to give a discount. In other words, a good pullback. This chart is probably the best of all. The price is reversing from the period moving average resistance in a strong downtrend. The higher timeframe is also not flat.

What we are going to do is use the highest win rate trading strategy we have tested on the Trading Rush Channel. According to the MACD strategy, the entry is when the short crossover happens. But if I enter right now, the 1 to 1 reward risk ratio profit target goes very close to swing low support.

It is better to wait for the price to move in the upward direction. Actually, now waiting for the price to move in the opposite direction is not going to work because the price already made a good move in the entry direction. This was an excellent setup, and the trade would have looked like this if the price would have moved in the opposite direction a little. After a few hours, I found two pairs where the price movement was good.

We will use the Momentum Indicator to see the pullback reversal. The MACD gave a short signal above the moving average which is against the entry rules. We want the short entry below the moving average. So what I will do is switch to one timeframe higher, and see if the price starts to move back in the trend direction.

This was the 5 min timeframe, so one timeframe higher will be the 10 min timeframe. If the price goes back in the trend direction on the 5 min timeframe, MACD should give a short entry signal on the 10 min timeframe as well. We will take that entry instead. Unfortunately, the same thing happened again. The price made a strong move in the entry direction, just before the entry signal. Now the trade is not worth it because the reward risk ratio is low.

I found this chart with a strong downtrend. Since the price is moving strongly in the downward direction without giving any bigger pullbacks, we will use the Beep Boop indicator we made in the previous videos to take short trades. It has made money three times on this trend already, and if the downtrend continues, it will most likely make money again.

The Beep Boop indicator did give a good entry signal, and a few minutes later it did make money. The price touched the profit target easily. In total, I scanned over 10 charts multiple times throughout the day and was only able to find 3 setups that were actually good. We were only able to find 3 good setups because there was not much to trade in the first place. We filtered out the bad charts and traded on the few good charts that we found.

Remember, most of the time markets are ranging and not worth trading. So if you are finding trades every time you open your charting platform, you are most likely not filtering out the bad charts. The secret is the chart selection process. In the first experiment, we saw how different charts can give different results.

The bad chart that was looking like this on the higher timeframe, performed worse than the good chart that was looking like this. The practical results matched the theory, but to make sure it was not just a fluke, we ran the experiment again, and we got similar results.