アンドリュー・セルビー, Author at Algorithmic and Mechanical Forex Strategies

定量的な外国為替トレーダーを失望できる4つの危険

2 月 28, 2014 によってアンドリュー・セルビー Leave a Comment

Many traders shift to the quantitative side of the aisle in an attempt to get away from the emotional frustrations that discretionary traders are forced to deal with. しかし, quantitative Forex traders quickly learn that there are a plenty of frustrations with quantitative approaches as well.

While quantitative traders might not stress over whether or not they entered a position correctly, they are more likely to wonder whether their entire strategy is still viable. Instead of doubting their current positions, they spend their time doubting their backtesting results.

There are a number of hazards that can frustrate quantitative forex traders, at the root of most of them is a failure to follow the rules of a trading strategy.

There was a recent post on Forex Crunch that looked at four hazards that Forex traders are faced with. While the article was interesting, I thought it would be more interesting to take a look at those same four hazards from a quantitative perspective.

News Events

News events are made out to be a very big deal on many different financial news channels. Many discretionary traders focus their entire strategies around things like crop yields or economic reports. Much of this is done with good reason, as those reports can affect prices.

定量的なトレーダーとして, our job is to follow the rules of our strategy, regardless of what the rest of the world is saying or doing. There is a tremendous danger to a quantitative trader’s mindset if he allows himself to be influenced by news events, even if those news events pertain to the markets he is trading.

Currency Interventions

Currency interventions are actually very similar to news events for quantitative traders. Both are unpredictable, and neither should impact your decision making process.

In the event of a government currency intervention, the most successful traders are the ones who are able to keep a level head and stick to their trading strategy. The traders who alter their strategies based on external events are usually the ones that blow up their accounts.

Trading Psychology

One of the trickiest hazards for any trader to deal with is their own psychology. One of the most complicated things about trader psychology is that it can be very hard to identify before it becomes a problem. After it is clear that psychology is an issue, it is probably already too late.

For quantitative traders, psychology issues generally stem from failure to stick to the rules of their strategy. If your system calls for you to wait for a bar to close before cutting a loss, it might be difficult to wait for that bar to close if it is already showing a massive loss. 反対側に, you might also talk yourself into ignoring a stop if your psychology gets in the way of a trade.

System Faults

Another hazard that can frustrate quantitative Forex traders are faults that exist within the systems that we trade. This could stem from any number of biases that invalidate our backtesting results. It could also stem from a bug in our programming.

In order to cautiously avoid any system faults, we must be constantly on the lookout for them. Even the slightest error could seriously jeopardize your profits.

サンプルサイズの懸念を説明するためにNFLの使い方

2 月 27, 2014 によってアンドリュー・セルビー Leave a Comment

私の自由な時間に, 私は、メジャーリーグの試合に賭けるための定量的な戦略の開発に取り組んできました. これは非常に興味深いサイドプロジェクトとなっています, 現時点では、それは非常に成功していません.

私が代わりにサッカーやバスケットボールのゲームの野球の試合に集中することを選んだ主な理由は、野球チームは、他のスポーツよりも多くのゲームをプレイすることです. 時間をかけて, これは、より大きなサンプルサイズを提供するべきです, 私はより多くの重要な結果を与え、小さなサンプルサイズに起因する差異をなくします.

NFLは、小さなサンプルサイズの統計的有意性の観点から避けるために、何の優れた例を提供してくれます.

同様にサンプルサイズのトピックを見て月曜日にGestaltuの投稿がありました. 著者は、自然を使用しました NFLで発生復帰を意味します例として、すべてのシーズンが影響を説明することサンプルサイズが小さいですパフォーマンスに持つことができます. 記事はまた、小さなサンプルサイズは、当社のファンドマネジャーの評価やバックテスト結果に導入することができる危険性に対処.

ゲームの数は、事項を再生しました

NFLでレギュラーシーズンの構成は 16 ゲームで再生 17 週間. 比較して, NBAやNHLの各プレイ 82 シーズンゲーム, そして、MLBチームは遊びます 162 ゲーム. 各リーグのチーム数により、これらの合計を掛けて、あなたは他のどのスポーツよりも大幅に少ないNFLゲームが毎年あることがわかります.

ここでは、取引の比較は明らかです. 私たちは真剣に基づいた戦略を検討することは困難であろう 16 取引. 作る戦略 162 取引毎年不運な取引を回避するためのより良いチャンスがあります, あるいは、少なくともそれらからの回復.

平均チームはプレーオフを作ります

記事では、毎年NFLでプレーオフに潜入平均才能レベルのチームがあることのポイントを強調. そのため、小さなサンプルサイズの, これらの平均のチームは、シーズン中にいくつかの幸運休憩から莫大な報酬を得ることができます.

同じやり方で, 彼らのパフォーマンスの歴史の中で同様のラッキーブレイクからの多くのファンドマネジャーの利益. 記事はさらにさかのぼるレコードを追跡することを示しています 10 統計的に有意であるのに十分に大きいサイズのサンプルを欠くことができる年.

平均サッカーチームがプレーオフに潜入することができ、同様に限界ファンドマネジャーは、印象的なリターンを投稿することができます, 彼らの理想的な環境でのバックテスト時の平均取引戦略は、優れたリターンを生成することができます.

むしろバックテスト結果のみに焦点を当てより, 我々はまた、戦略の基盤となるプロセスを見てする必要があります. 我々は、我々の戦略は制限したいです入力パラメータの数かつ徹底的な統計分析に耐えすることができます. あなたは、資本のすべてがに乗って巻き込まする必要はありません 2012 中にボルティモア・レイブンズ 2013 シーズン.

お使いのシステムに障害が発生したとき知ってどのように

2 月 26, 2014 によってアンドリュー・セルビー Leave a Comment

One of the most frustrating aspects of quantitative trading is that most of the strategies we develop will end up failing. Experiencing system failure can be very difficult for a trader to handle on many levels. There will be a tough emotional and psychological impact to deal with, and there will also be financial losses to address.

Because system failure can be such a devastating event, we need to be prepared to recognize it as early as possible and have a plan to deal with it. System failure could be defined by drawdowns that are too large, drawdowns that are too long, or a general failure to create profits. Whatever definition you prefer, it is important to consider failure in quantitative terms, leaving subjective opinions out of the decision.

Dealing with system failure can be extremely difficult. The key is to avoid making it a subjective evaluation by pre-defining failure criteria.

Daniel Fernandez from Mechanical Forex wrote a post this week on how to define and quantify system failure. その記事で, Daniel discusses having a specific definition for failure that accounts for sample size, relative performance, and performance relative to historic testing results. His point is that traders need to have a quantitative limit at which they will give up on a system.

Avoiding Subjective Assessments

Daniel makes a great point about traders who have an emotional attachment to their strategies ignoring statistical evidence that the system is failing:

When the attachment – due to economical, psychological reasons, etc – is too great, a trader will always have problems with saying that a system failed, because the burden of failure might be greater than the burden of financial loss if the system continues to trade.

When we spend a large amount of time developing our system, we can naturally become attached to them. Just like parents dealing with disciplining their young children, we will have to separate our desire for these systems to succeed from our ability to realistically interpret what is actually happening.

Failure is Relative

Whether you choose to compare your system to a benchmark, historical backtesting, or a monte carlo simulation, you should have a pre-defined limit for how far the system will be allowed to deviate from its expected results. This will help to eliminate any subjective opinions about how well a system is performing.

Sample Size Matters

It is also important to have a pre-defined limit for the サンプルサイズ that you will consider statistically significant. Comparing a 5 trade sample to a 5000 trade backtest is obviously quite flawed, but you have to set a number of trades that you will consider to be a good representation of your strategy.

As the number of trades increases or decreases, so does the significance of the depth or length of a drawdown. It is your responsibility to define the point at which the number of trades crosses the threshold of significance.

3 移動平均の基本的なアプリケーション

2 月 25, 2014 によってアンドリュー・セルビー Leave a Comment

定量的なトレーダーとして, we design our strategies to make trading decisions based on certain signals. These signals can be as simple, or as complex as we desire.

One of the most basic types of signals that a quantitative strategy will implement is a moving average. While these signals are simple to understand and widely utilized, it is surprising how effective they can be.

Whether you use them as trade signals, trend filters, or as parts of other indicators, moving averages are an essential part of quantitative trading.

A recent post on Forex Crunch discussed three ways to use moving averages to generate trade signals. While none of these methods is new to us, the post provided a good reminder that there are multiple ways to implement a moving average in our trading strategies. Each method has a different goal, but they can all contribute to a profitable trading system.

Crossover Entry/Exit Signals

This is the most common way that moving averages are utilized. We have covered plenty of strategies that use moving average to determine when to enter or exit a trade. This is the basis for many trend following strategies.

The basic concept is that when a faster moving average crosses above a slower moving average, an uptrend has begun and the strategy should take a long positions. その後、, when the faster moving average crosses back below the slower moving average, the uptrend has ended and the strategy should exit its position and possibly establish a short position.

One evolution of this strategy is to include a third moving average somewhere between the fast and slow moving averages. This middle moving average will allow your strategy to exit quicker, hopefully preventing giving back profits.

Trend Filters

Another popular application of moving averages is to use a long term moving average as a trend filter for a strategy that uses some other criteria for entries and exits. This can be seen quite often in the mean reversion strategies developed by Larry Connors and Cesar Alvarez.

One simple example of this would be a mean reversion system that only wants to trade short-term dips in the midst of a long-term uptrend. The strategy could use a 200-day moving average to determine the overall trend. その後、, if the overall trend is up, it might use a different indicator, like RSI, to identify short-term oversold conditions.

Smoothing Other Indicators

Moving averages are also used in many different indicators in order to smooth out the data signals. Averaging the signals that an indicator produces enables a trader to eliminate some of the noise to get a clearer picture of what is actually happening in a market.

Two great examples of other indicators that utilize moving averages are the ストキャスティクスと、 MACD Indicator. The Stochastic Oscillator uses the %K line, which is simply a moving average of the %D line, as an entry/exit signal. The MACD Indicator is actually based completely on moving averages.

我々はそれほど頻繁に取引することによってリターンを増やすことができます。?

2 月 24, 2014 によってアンドリュー・セルビー Leave a Comment

“それは、ほとんどすべての理論の最高の目標は、経験の単一のデータの適切な表現を放棄することなく、可能な限りシンプルで、わずか既約の基本的な要素を作ることであることを否定することはできません。” – アルバート・アインシュタイン

アインシュタインからの引用は見Gestaltuの最近のポストの後ろのインスピレーションでしたさらに簡素化する方法すでに簡単な回転戦略. 投稿はメバネフェーバーの非常に人気を取りましたアイビーポートフォリオそして、同様の戦略は、毎月のではなく、年ごとに一度だけ、ポートフォリオをリバランスすることによって同等の結果が得られたことができるかどうかを試験.

それだけで年に一回、それを取引することにより、毎月のリバランス戦略を向上させることが可能です?

それは私がフェーバーのアイビーポートフォリオの大ファンだということは秘密ではありません. 私はそれが取引にマルチ戦略的なアプローチを構築するための優れた基盤を表していると信じています. 結果は、そこに最高のすべての周りの長期戦略の一つであることを示しています, 同時に従うことが非常に簡単です.

この理論の試験のため, 著者は、取引アイビーポートフォリオのバージョンで開始 5 その10ヶ月移動平均に基づいて、資産クラス. ポートフォリオは、彼らがリバランス時に、その移動平均の上または下にあるかどうかに基づいて、資産クラスのそれぞれについて、長いまたは現金でのどちらかであります.

バックテスト結果

可能なリバランス日付の広い範囲に基づいて提供数の圧倒的な量がありました. 結局のところ, ほとんどすべての場合には, 毎月のリバランスは、毎年恒例のリバランスを上回りました.

著者らは、取引暦の各個々の日に毎年恒例のリバランスをテストする限り行ってきました. いくつかの日は、毎月リバランスをアウトパフォームすることができましたが, そのパフォーマンスがありました運に起因します, むしろ、再現エッジより.

ポストはまた、10ヶ月移動平均の代わりに、12ヶ月移動平均を使用して実験を行いました. その場合, リターンはほぼ同一でした.

我々は、他の方向に行くことができます?

記事では、リバランスの頻度と取引コストとの関係に関する議論で締めくくり. 明らかに, 毎年リバランスの代わりに、毎月、トランザクションコストの多くを救います, パフォーマンスが価値ではありません. 反対に, リバランスの毎日は、戦略が、潜在的なエッジを失うので、多くの取引費用を負担.

記事が上に触れていなかった1つの領域は、毎週のリバランスでした. これは、毎月のリバランスよりも仕事と高い取引費用を表すことになります, それでも毎日リバランスよりもはるかに少ないの取引になります. 私は、毎週または隔週をリバランスすると、毎月のリバランスを超える改善であるかもしれないかどうかの好奇心.

3 つの異なる方法がサバイバーシップ・バイアスは、バックテストを傷つけることができます。

2 月 23, 2014 によってアンドリュー・セルビー Leave a Comment

ようだ学ぶ開発とテストに関して定量的な取引戦略, 多く我々のバックテスト結果を台無しことができますどのように簡単に実現します。. 我々は完全に無駄なテスト結果を生成できる最も簡単な方法の 1 つは適切に洗浄されていないデータを使用して、します。.

サバイバーシップ・バイアスが多くの微妙な方法で私たちのバックテストは、価格データにクリープすることができます。. として取引価格の違いによって、3 つの最も明白な方法の, 上場廃止株のインクルードに失敗, か、今日ではなく、歴史的なコンポーネントを使用して構成されているとインデックスをテスト.

サバイバーシップ・バイアスの多くの異なるバージョンまでのバックテスト結果を開くことができます生き残るために十分な強されている株式のみを含む.

これらのデータセットの欠陥のそれぞれは、バックテスト結果を様々な影響を持つことができます。, 我々はテスト戦略と他の変数の数の種類に応じて. で、最近のポスト, セザールアルバレスサバイバーシップ・バイアスの異なるバージョンに含まれるデータを使用して、いくつかの戦略をテストするのには時間がかかった. 彼の結果を示したときバイアスは、ごくわずかな影響を持つことができます時間があること, しかし、影響が重要な場合もあります。.

インデックスコンポーネント

年間を通じて異なる時期に, 各主要なインデックスは、インデックスを構成する株式を調整をしていく. これは特定の方法で一般的な市場を追跡するインデックスの機能を維持するために行われます.

バックテスト宇宙としてインデックスの今日のバージョンを構成する株式のみを使用して, 我々はすべての私たちのバックテストの長さのインデックスから削除する株式不十分な十分な実行を排除する自動的に. これは我々が実際にあろうその期間ライブシステムを取引した場合のバージョンよりもはるかに強い株式の宇宙と私たちを葉します。.

セザールの研究は私たちの毎年の収益を投稿することができた方法を示します 36.25% 最大ドローダウンと 24.54%. しかし, 正確な同じ戦略がインデックスの歴史的に正しいバージョンでテストされたとき, 年利に落ちた 14.07% 最大ドローダウンに上昇しました。 30.42%.

上場廃止銘柄

上場廃止株が最も一般的理解の状サバイバーシップ・バイアス. これらは、彼らはもはや買収や倒産により取引されているので、バックテストを欠場する株式. 不要になったインデックスに記載されている株式と同様, 私たちに我々はライブ取引中にあろうよりも株式の強力な宇宙を与えること我々の宇宙で上場廃止株の失敗.

一貫性がないので、セザールをこの場合提供する証拠は面白い. テストするとき、平均回帰戦略, 実際にパフォーマンスを向上させるが表示されます上場廃止株式を含む. しかし, テストするとき、戦略を次の傾向, 年間収益率と最大ドローダウンにマイナスの影響を持っていた上場廃止株式を含む.

として取引価格

セザール試してみたかったもう一つのサバイバーシップバイアスアイデアはバックテストのパフォーマンス分割調整価格の影響. 株式分割のため, 個別銘柄の歴史的な分割調整価格は当時実際にあったよりもはるかに低い多くのインスタンスがあります。.

セザールの理論はこれが持っていることバックテストに負の影響を検討すると株の最低価格を必要とする戦略. これは非常に合理的な音ながら, 結果表示の違いはほとんどの場合ほとんど無視できます。.

インターネット遺跡のトレーダー

2 月 21, 2014 によってアンドリュー・セルビー Leave a Comment

The Internet has become the greatest resource that our planet has ever seen. There are endless advantages that it affords traders today that were simply not possible years ago. しかし, there are also some serious drawbacks that have been created by the world wide web.

Traders today have the ability to learn just about anything they want to know about anything through the internet. The problem is that this vast amount of knowledge can cause traders to experience information overload. Too much information can actually be worse than not having enough.

The Internet can be a valuable component in the development of a trader, but it can also lead to a condition of information overload.

Nial Fuller from Learn To Trade The Markets wrote an interesting post about information overload where he covered how expanding coverage of popular economic reports and wider availability of different trading systems can actually be a detriment to developing traders.

Too Many Numbers

“Knowing what the latest Non-Farm Payrolls numbers are is not going to help you become a successful trader.” – Fuller

One of the great trading fallacies of our modern era is that more economic information is going to help us gain some sort of insight that no one else has seen. Many traders get caught up in the excitement surrounding different economic reports, but at the end of the day their strategies aren’t affected by the report either way.

Most quantitative strategies are based on signals that are generated by very specific technical data. While the data itself may be impacted by economic conditions, the actual strategy isn’t basing any decisions on those conditions until they show up in the data. したがって, the actual economic reports have no direct influence on the strategy.

Too Many Systems

“Knowledge and theory are great, but without practice and experience they are nothing.” – Fuller

Another issue that many modern traders have to confront is getting lost in learning about trading and never crossing over to actually trading. It is easy to convince yourself that you are not ready to trade. There will always be something else to learn. There will always be some concept you haven’t researched yet.

In order to be a successful trader, at some point you have to stop looking for the best strategy and actually start trading one. This shift in mindset is one of the hardest aspects of becoming successful for new traders. In order to climb to the top of the trading mountain, you have to stop reading about climbing the mountain and actually start climbing.

Shifting the Trading Mindset

The Internet can do a great job of convincing traders that they need to process and understand all of the information available in order to make better decisions.

What many successful traders have actually found is that there is much greater value in ignoring most of the data available in order to focus on the specific signals of their strategy. Ignoring all of the noise that comes with trading to focus on specific data points is the key to shifting from the mindset of learning to the mindset of trading.

Are You Sabotaging Your Projected Trend Following Profits?

2 月 20, 2014 によってアンドリュー・セルビー Leave a Comment

It is widely understood that most of the profits from trend following and momentum strategies come from a select few big winners. Despite that understanding, traders generally don’t appreciate exactly how few trades make up that select few.

定量的なトレーダーとして, we willingly forfeit the desire to pick and choose between signals that our strategy triggers. Even though we aren’t making discretionary decisions about entries and exits, there is still a level of respect that needs to be paid to the importance of the select few trades that will drive our performance.

Missing just one trade can have a severe impact on your overall return, so if you aren’t committed to taking every trade you might be better off with a buy-and-hold strategy.

The Dorsey Wright Money Management blog published a post earlier this month that did a tremendous job of breaking down exactly how the top 20% of returns of a momentum strategy were relative to the other 80%. The post also showed how each quintile performed relative to a buy-and-hold benchmark.

Breaking Down Performance of Trades

The article took a basic sector rotation strategy that trades S&P 500 sub-sectors and broke its returns into five groups that each made up 20% of the total trades after sorting all trades by performance. They charted the performance of each of these groups and compared it to a chart of a equal-weight strategy that held all of the sub-sectors and rebalanced monthly.

The charts show that, regardless of lookback period, the bottom 60% of the strategy’s trades underperform the benchmark. The next 20% of trades just barely outperform the benchmark, and more or less match it after transaction costs. The only group that significantly outperforms the benchmark is the very top 20% of trades.

The Best Trades Are Critically Important

The point that the post is trying to make is that you absolutely must be willing and able to take every single trade that your trend following or momentum strategy produces. Omitting even one trade from the top 20% could cripple your overall performance because. Because we have no way of knowing what trades will end up producing the best profits, we cannot afford to miss out on any of them.

The article sums up this theme quite nicely:

If you are unwilling to constantly cut the losers and buy the winners because of some emotional hangup, it is extremely difficult to outperform.

Even the Best Traders Learn the Hard Way

Market Wizard Tom Basso tells a great story that fits in well here. Early in his trading career, Tom took a day off to spend the day with his parents who had come to visit him. On that day, he missed a signal for a silver trade that would have ended up being hugely profitable. That single trade would have made the difference between a profitable year and a losing year.

The moral of the story is that if you are going to trade a trend following or momentum strategy, you absolutely must be willing and able to take every single trade signal. There are no exceptions, because just one exception could ruin your performance.

Using an ATR Filter to Gauge Market Conditons

2 月 19, 2014 によってアンドリュー・セルビー Leave a Comment

平均真の範囲 (ATR) is primarily used as a mechanism to determine stop-loss levels. Another way to use ATR that is not quite as popular is as a filter to isolate market environments that have the potential to make significant moves.

By gauging the volatility of a given market, ATR can provide us with insight to the possible magnitude of a move. If a market has been experiencing greater volatility, it is probably more capable of producing a significant move than a market that has been experiencing lower volatility.

This interesting example demonstrates how we can use an ATR Filter to evaluate market conditions.

Nat Stewart from NAS Trading wrote an interesting post about this topic where he compared the state of a market to weather conditions. He explains how market conditions can be evaluated just as weather conditions and then breaks down an example using ATR to evaluate market conditions.

Market Conditions and the Weather

Nat starts his post by comparing the similarities between wanting to know about weather conditions and market conditions. His concept that underlying conditions can impact the potential of a buy or sell decision is not revolutionary, but it provides us with an interesting visual when coupled with the weather analogy.

Being aware of your environment is essential to success in life and trading. You would probably be far less likely to leave the house during a hurricane. 同時に, you would have a hard time buying breakouts in a sideways trending market. 定量的なトレーダーとして, we have the ability to build filters for our strategies that check for weather conditions.

The ATR Filter

Nat explained how this concept could be applied by providing us with backtesting results for a simple S&P 500 futures breakout strategy. For these backtests, he used ATR as a filter, requiring a certain level of volatility before his strategy would participate.

As the volatility required by the strategy increased, so did the win rate and average profit per trade. When an ATR of 10 was required, the strategy posted a win rate of 53.3% and an average trade of $82. When the required ATR was boosted to 40, the win rate increased to 76.5% and the average profit per trade jumped to $761.

Key Takeaways

Nat points out that these results are opposite of what we would expect based on the common practice of setting position sizes based on ATR. Many trend followers will reduce position sizes when ATR expands when those trades appear to actually be more profitable.

One thing that he doesn’t provide us is how many trades were eliminated when the ATR filter was raised from 10 宛先 40. It is possible that the bigger filter eliminated most of the trades, which would result in a lower annual return and total profit. It could also expose the backtesting results to サンプルサイズが小さいです bias.

Regardless of whether Nat’s backtesting results are statistically significant, his greater point remains effective. Every trader should be concerned with determining what type of market weather his strategy performs best in and look for ways to isolate those situations.

あなた自身の外国為替の戦略を作成するための青写真, Part 2

2 月 18, 2014 によってアンドリュー・セルビー Leave a Comment

Earlier this month, we looked at an article from Forex Crunch that covered the first three steps for building a new quantitative Forex strategy. Those first three steps covered brainstorming strategy ideas, defining the rules, and optimizing the parameters.

At that point we had a strategy that we had reason to believe would perform well in a trading situation. The next steps would involve properly testing our strategy in order to prove its value.

After brainstorming, defining rules, and optimizing a new Forex strategy, the next steps involve rigorous testing.

Forex Crunch has since published the second three steps for creating a robust Forex system. This post focuses on testing the system that was created with the first three steps. It suggests starting with in-sample testing, then moving to out-of-sample testing, and then suggests some even deeper methods of testing.

The Most Important Point Regarding Testing

While there is plenty of great information in the article about the different types of testing that should be performed on a new Forex strategy, the most significant point that the article makes is actually stated in the introduction:

Relying on the CAR (compound annual return) figure is not always a good idea because this metric does not take into account the risk that was involved in producing those gains.

This point is extremely basic, which makes it easy to overlook. While a strong compound annual return is the end goal of every trader, we all know that there are many ways to arrive at a strong compound annual return, and some of them aren’t worth the effort.

In addition to compound annual return, we also need to be concerned with how the strategy performs from a risk perspective. Looking at statistics like maximum drawdown, プロフィットファクター, Sharpe ratio, and winning percentage gives us a better idea of how the strategy arrives at its compound annual return.

This bigger picture view will give us a more qualified overview of what trading the strategy will feel like. We can use that to determine if the amount of risk the strategy exposes our capital to is in our tolerable range.

Testing Forex Strategies

Testing on in-sample data is where we can fine tune our strategies in order to get the return and risk statistics into the desired range. From there, we move to out-of-sample testing where we attempt to replicate those statistics on a fresh data set.

There are also testing methods like Walk-Forward Optimization and Monte Carlo Simulations that can shed even more light onto how our new system can be expected to perform in live trading. The important thing to watch for during this testing phase is consistency. The strategy should perform similarly across all of these different types of testing.

If the strategy produces solid returns through a wide range of testing, it can be expected to produce similar results in live trading.