アルゴリズムと機械の外国為替戦略 | OneStepRemoved

どうなっているのですか?

によって 9 コメント

それはどんな定義によるでこぼこ月をされています。. 先月の連銀発表の余波でお金のトンを作りました。, only to give it all back the next week. QB Pro recovered most of the earlier gains, then last week’s drawdown took it all back again. It’s been painful.

The good news is that the new changes to QB Pro are rolled out. Several of you sent in emails asking about new currencies like GBPNZD and AUDCAD appearing in your account. Kudos to you for paying close attention to the trading.

The total currencies traded in the basket is up to 16 ペア. While the max leverage is unchanged at 36:1 (still very, very high), the leverage per pair is only 2.25:1. Future losses like the one from last week will still occur.

The difference is that the size of the positions is reduced by over 2/3. The impact of getting caught in losing trades that are all reflective of USD weakness decreases significantly. We’re now trading a mix of AUD, CAD, スイス フラン, ユーロ (EUR), 英国ポンド, 円, NZD, USD and XAG. No one currency should dominate the performance.

The system also does extremely well on emerging market currencies. I’m holding off on adding RUB, MXN and others until I determine the impact of the spreads on overall profitability. They’d do amazing if we could trade for free!

Short term performance expectations for QB Pro

We’re coming into the summer, which is when the forex market traditionally falls into the doldrums. That’s generally a good thing for QB Pro. The markets whipsaw up and down without really going anywhere.

The alternative is that the Fed hikes rates in June and sends the market into a USD buying frenzy. That’s also good news. Most of the money that QB Pro made over the past 8 months was driven by USD strength. A rate hike would unleash chaos in emerging markets and equities. That’s the kind of condition to push volatility into our new crosses, creating opportunities for us to trade.

QB プロ 2.0 isn’t happening

I’m extremely disappointed. After several thousand dollars in programming expenses, and not to mention the 100+ hours that I spent coding myself, the QB Pro 2.0 change is a wash.

I had a trusted developer audit my code to make sure I wasn’t doing something stupid like trading on future prices or anything. Neither him nor myself caught anything from December until March.

Towards the end of last month, a single line of code ruined it all. One of my key features was deciding when to bail on trades and go the opposite direction. まあ, it turned out that I accidentally introduced data snooping into the backtesting platform. I pre-calculated when losing trades occurred to calculate probabilities.

In plain English, my goal was to calculate “If today was a big loser, then do the opposite tomorrow.”

What I accidentally coded was “If tomorrow is a big loser, then do the opposite.” If only that were possible!

I don’t want to muddle up the explanation with code examples. Suffice it to say that the idea didn’t work out when I took away the ability to look into the future.

There are some features of the 2.0 system that I wish to analyze in the coming months, but for now it’s going to have to take a back seat.

What’s next?

My plan is to sit tight for a few weeks to ensure that the new pairs are working as intended. Whenever I am personally satisfied with the system behavior, I intend to increase the amount of capital in my account.

Don’t hold my feet to the fire. This part is a subjective process, so I can’t put a precise time frame on it. If and when I am satisfied – and it’s going very well the first few days – then I will make a decision about increasing my capital at risk.

If and when I choose to increase my capital in the account, I will then re-open QB Pro to new traders.

PS: I hope that the drawdowns encourage some of you to withdraw profits the next time the opportunity presents itself. You don’t want to lose more than you are comfortable risking.

正しい戦略を選択します

によって 3 コメント

Traders use a variety of strategies in the markets, all based on two forms of analysis: Fundamental analysis and technical analysis. Although institutions and other large traders often use a combination of these two analytical styles, most independent traders rely on strategies based largely on technical analysis.

Let’s take a look at both analytical styles as they apply to trading forex.

Fundamental analysis

In the stock markets, equities traders are sometimes able to value a company (and therefore predict its share price) if they know all the information about that company. That’s because the share price of the company reflects the value of its known assets. By knowing a company, the equity trader knows what its share price should be.

しかし, in forex markets using fundamental analysis alone is far less effective, because it’s extremely difficult to value an entire country’s economy in order to predict its currency’s value. Most forex traders use exclusively technical analysis.

When full scale fundamental analysis is applied to forex markets, it’s most often used as a way to predict longer-term trends. と, some traders use data such as news releases in the short term to generate trades or confirm signals. だから, along with their mainstay technical analysis, some traders incorporate fundamental data.

Here are some of the fundamental indicators commonly used by forex traders:

★ Non-Farm Payroll

★ Consumer Price Index (CPI)

★ Purchasing Managers Index (PMI)

★ Durable Goods Sales

★ Retail Sales

For best results, savvy traders also pay attention to various meetings of government officials and industry conferences, and other venues where market-moving quotes and commentary can be found.

Meetings are scheduled to discuss inflation, interest rates and other issues that directly affect currency prices. These meetings and conferences are often reported in the industry press before they reach mainstream media. The important event for fundamentals-based forex traders is the Federal Open Market Committee (FOMC) press conference and meeting transcript.

Forex traders can follow meetings and conferences and become highly knowledgeable specialists, and profit by knowing a particular market better than most others.

Technical analysis

Technical analysis is by far the most common basis for forex strategies. Using technical analysis in forex is different than in equities, because the forex time frame is 24 hours worldwide whereas many stocks don’t trade overnight, so their price movements are different.

Traders use a huge variety of individualized systems, often built by knowledgeable EA providers, with many different indicators. Here are just a few of the most common indicators and theories used in technical analysis:

★ Elliott Waves

★ パラボリック SAR

★ ギャン Theory

★ Fibonacci Numbers

★ Pivot points

Traders craft many different strategies based on technical analysis, especially by combining multiple indicators. Other developers create trading systems based on finding historical buying and selling patterns that are expected to be repeated.

Developing a personal strategy

Successful forex traders develop and fine-tune their strategies over time. Some traders focus on a particular tool or calculation, while others user a broader approach and experiment with a combination of technical and fundamental analysis.

Many new traders wisely start out by “paper trading” or using a demo account with a 外国為替ブローカー. と, experienced traders almost invariably develop new systems with backtesting before trying them in real time. Lack of experience can cause you to lose your capital, so it’s important to take the time to practice before committing significant money to any new trading system.

Regardless of whether you use technical indicators alone, or incorporate fundamentals as well, if you have the discipline to learn your target markets and trade confidently while carefully managing risks, then your strategy has an excellent chance to succeed.

Do you rely on technical indicators? Fundamental indicators? または, a combination of both?

機械取引システムと勝つ方法

によって 13 コメント

多くのインクは、機械的な取引システム故障の原因を特定することに専念してきました, 実際には、特に後. それは矛盾に見えるかもしれませんが (または, 一部のトレーダーに, 単に低能), なぜこれらの取引のシステムが失敗する主な理由はハンズフリーにあまり依存しています。, メカニカルトレーディングの火災を忘れて自然. アルゴリズム自身ない客観的人間監督と市場環境の変化に伴い進化システムのために必要となる介入.

機械的な取引システム障害, またはトレーダーの失敗?

取引システムの障害を嘆くのではなく, 建設的なトレーダーが両方の世界のベストを持つことができる方法を検討するは: それです, トレーダーは、アルゴリズムで管理された機械的な取引システムの利点を楽しむことができます。, 矢継ぎ早の自動実行など取引の決定の感情解放, まだ失敗と成功について客観的な思考の人間に生来の能力を活用しながら.

すべてのトレーダーの最も重要な要素は、人間の能力 進化. トレーダーすることができます変更し、財政的にまたは感情的に壊滅的な損失になる前に勝ち続けるために彼らの貿易システムを適応.

右のタイプと量をテストするための市場データを選択します。

成功したトレーダーは、市場の短期的な非効率から利益を収穫するのに反復的な規則のシステムを使用します。. 小型用, 有価証券及びデリバティブ取引の大きな世界で独立したトレーダー, スプレッドが薄いと競争激しい, 利益のための最高の機会は、スポッティングに基づく市場の非効率性から来る, 簡単に定量化するデータ, 可能な限り迅速に行動し、.

トレーダーが開発し、動作履歴データに基づく機械的な取引システム, 彼または彼女は、現在の市場の非効率性が引き続きアイデアに基づいて将来の利益を望んでいます。. 間違ったデータを設定または不適切なパラメーターを使用して、データを限定トレーダーを選択した場合, 貴重な機会が失われることがあります。. 同時に, 履歴データが存在しなく、非効率で検出されます。, その後、取引システムが失敗します。. なぜそれが消えた理由は機械のトレーダーに重要ではないです。. 結果問題のみ.

mechanical trading rules

作成し、機械的な取引システムをテストするデータ セットを選択するときに最も適切なデータ セットを選ぶ. と, 取引ルールの市場条件の広い範囲の下で一貫した動作かどうかを確認するのに十分な大きさのサンプルをテストするために, トレーダーは、テスト データの最長期間を使用する必要があります。.

だから, 設計パラメーターの最も簡単なセットと同様、両方の可能な最も長く歴史的データ セットに基づいて機械的な取引システムを構築するようにします. 堅牢性は多くのタイプの市場の条件に耐える能力を考慮して一般的に. 堅牢性を過去のデータと単純なルールの長い時間範囲にわたってテスト システムに固有にする必要があります。. 長時間にわたるテストと基本的なルールは、将来的に潜在的な市場条件の広い配列を反映すべき.

履歴データは明らかにすべての将来のイベントを含まれないために、すべての機械的な取引システムは最終的に失敗します。. 歴史データで作成されたすべてのシステムは最終的に歴史に無関心な条件を発生します。. 人間の洞察力と介入からレールを離れて実行している自動化された戦略を防ぎます. 騎士の首都では、人々 が何かを知る ライブ取引混乱状態.

その適応性、シンプルさ勝

成功した機械的な取引システムは生きているようなします。, 生物の呼吸. 世界の地層に満ちている生物の化石を, 自分の歴史の期間の間に短期的な成功に最適ですが, あまりにも適応と長期生存に特化しました。. クイックを受けることができるため、人間指導の単純なアルゴリズムによる機械的な取引システムが最高, 簡単な進化と環境の変化への適応 (市場を読む).

シンプルな取引ルールがデータ マイニング バイアスの潜在的な影響を減らす. データ マイニングからバイアス それは歴史的な規則が将来の条件の下で適用されますどれだけ誇張するかもしれないので問題は, 機械的なトレーディング システムは、短い時間フレームに焦点を当てたときに特に. シンプルで堅牢な機械的な取引システムのテスト用に使用されるタイム フレームで影響を受けるべきではないです。. -履歴データの特定の範囲内にあるテスト ポイント数まだ証明またはテストされている取引ルールの有効性を反証するのに十分な大きさにする必要があります。. 言い方を変えれば, 単純です, データ マイニングのバイアスを上回る強力な機械的な取引システム.

トレーダーは、シンプルなデザイン パラメーターを持つシステムを使用している場合, よう、 QuantBar システム, 最長の適切な歴史的な期間を使用して、テストと, 他に重要なできるタスク システムの取引とその結果を今後監視の分野に固執することになります. 観測により、進化.

反対に, 「事前に進化して」システムの初期絶滅のリスクを実行する複数のパラメーターの複雑なセットから構築された機械的な取引システムを使用するトレーダー.

機械取引の最善を活用する堅牢なシステムを構築します。, 煩わされずその弱点に

機械的なトレーディング システムの堅牢性と適応性を混同しないことが重要です。. システムの開発は '堅牢な' として記述されている多数の歴史的な期間に- と -現在の観測期間中であっても取引を獲得につながったパラメーターに基づいて、します。ないですこのようなシステムが正常に一度調整すること保証彼らは過去の自分の"ハネムーン期間貿易されています。” その中に条件はシステムの基になる、特定の歴史的な期間に合わせて起こる初期取引期間であります。.

単純な機械的な取引システムは簡単に新しい条件に適応, でも、市場変化の根本的な原因は明白でなく残る, 複雑なシステムでは不十分と. 市場環境の変化, 彼らは継続的に行う, 獲得していく可能性が最も高い取引システムは、シンプルで最も簡単に新しい条件に適応; 本当に堅牢なシステムは、とりわけ長寿を持っています。.

クイックを受けることができるため、人間指導の単純なアルゴリズムによる機械的な取引システムが最高, 簡単な進化と環境の変化への適応 (市場を読む).

残念なことに, 過度に複雑な機械的な取引システムを使用する場合の利益の最初の期間を経験した後, 多くのトレーダーが成功にこれらのシステムを微調整しようとしての罠に陥る. 市場の不明, まだ変更, 条件がありますが既に機械的な取引システムの全体の種、絶滅する運命. もう一度, シンプルさと状況の変化への適応性を提供する任意のトレーディング システムの生存のための最善の希望.

成功と失敗を区別する客観的な計測を使用します。

トレーダーの最も一般的な落下は彼または彼女の取引システムへの心理的な添付ファイルです。. 取引システムの障害が発生した場合, それは通常のトレーダーは、客観的ではなく主観的な視点を採用しているので, 特定取引中に停止損失に関しては特に.

人間の本性はしばしば感情的な添付ファイルを特定のシステムを開発する業者を運転します。, 特にときトレーダーに投資している時間とお金のかなりの量機械取引システムを理解することは困難である多くの複雑なパーツ. しかし, それを客観的に検討するためにシステムの外のステップにトレーダーのため極めて重要です。.

いくつかのケースで, 貿易業者になるシステムの予想される成功の妄想, 主観的な分析よりもはるかに長く、明らかに失うシステムを取引を継続することのポイントにも許してしまう. または, 脂肪の wins の期間の後, トレーダーは、可能性がありますになる「結婚」旧歴システムにその美しさが損失の圧力の下でフェードしながらも. 悪化, トレーダーは、テストの期間をまたは既に失うことシステムの統計的パラメーターを選んでの罠に落ちることがあります。, システムの偽の希望を維持するためには、値の継続的します。.

客観的基準, 現在の失敗の確率を評価する標準偏差のメソッドを使用するなど, 機械的なトレーディング システムの真の失敗があるかどうかを決定するための唯一の勝利方法です。. 客観的な目で, 機械的なトレーディング システムで迅速にスポットまたは潜在的な障害にトレーダーの簡単です。, シンプルなシステムは、迅速かつ簡単に新鮮な歴システムをもう一度作成に適応させること、.

機械的な取引システムの障害はしばしば歴史的な損失やドローダウンに対する測定電流の損失の比較に基づいてを定量化します。. このような分析は、主観的につながる可能性があります。, 不正確な結論. 最大ドローダウンは、トレーダーがシステムを放棄するしきい値としてよく使用されます。. 方法システムまでドローダウン レベルを考慮せず, またはそのレベルに達するに必要な時間の長さ, トレーダーは、システムがドローダウンだけに基づいて敗者である結論とせず.

障害の指標としてドローダウンに対する標準偏差

実際, 受賞システムを破棄することを避けるために最善の方法は客観的測定標準を使用して実際にそれを取引しながら得られたシステムからリターンの現在または最近の分布を確認するには. バックテストからリターンの歴史的な分布に対して測定を計算比較します。, 確実性に従って固定しきい値値を割り当てる際の機械的な取引システムの現在の「失う」配布は確かに正常より, 予想される損失, したがって失敗として捨てられるべき.

だから, たとえば, 貿易業者が現在のドローダウン レベルでシグナル状態に問題あり、彼の調査をトリガーを無視することを前提としてください。. 代わりに, 歴史的テスト期間中そのシステムの取引中に発生と歴史的な損失に対して現在の連敗を比較します。. どのように保守的なトレーダーがによってください。, 彼または彼女は現在または最近の損失が超えていることに気付く場合があります。, 言う, 、 95% 「普通の」歴史的な損失レベルからの 2 つの標準偏差によって暗黙的に指定確実レベル. これは確かに強力な統計記号は、システムのパフォーマンスが低下するだろう, したがって失敗しましたと. 対照的に, リスクの大きい食欲と異なるトレーダーことがあります 3 つのことを客観的に決めることの規範からの標準偏差 (すなわち. 99.7%) 確実に適切なレベルとして取引システムを判断するため「失敗」

任意のトレーディング システムの最も重要な要因’ 成功, 手動または機械かどうか, 人間の意思決定能力は、常に. 良い機械的な取引システムの値は、します。, すべての良いマシンのような, 彼らは人間の弱点を最小限に抑え、マニュアルの方法をはるかに超えて達成可能な達成に力を与える. まだ, 正しく作成されたとき, 彼らはまだトレーダーの判断に従ってしっかりした制御を許可して、彼または彼女が障害物と潜在的な障害の明確な舵取りをできるように.

トレーダーが損失するかどうかを評価する標準分布の統計計算の形で数学を使用できますが、通常、歴史的な記録によると許容, 彼または彼女はまだ作る純粋な機械ではなく人間の判断に依存します。, 単独でのアルゴリズムに基づく数学ベースの意思決定.

トレーダーは、両方の世界のベストを楽しむことができます。. アルゴリズムとメカニカルトレーディングの力は、発注・施工に関する人間の感情や遅刻の影響を最小します。, ストップロスの規律を維持することに関しては特に. それはまだ取引のシステムを人間の制御を保持するために標準偏差の客観的な評価を使用してください。.

変更のために準備します。, 取引システムを変更しておいて

ときに検出する客観性とともに、機械的な取引システムは、敗者に勝者から変更します。, トレーダーでは、規律と先見性に進化し、彼らは新しい市場環境に勝つために続けることができますので、システムを変更する必要があります。. どのような環境変化でいっぱいに, シンプルなシステム, その進化になります迅速かつ容易に. 複雑な戦略が失敗した場合, 簡単にそれを変更するよりも交換することがあります。, いくつかの最も簡単な最も直感的なシステムの, よう、 QuantBar システム, 比較的容易に将来の市場状況に適応するためにその場でを変更.

要約すると, 正しく作成された機械的な取引システムはシンプルかつ適応可能であるべきといえよう, さまざまな市場条件の下で利益を生成するのに十分な堅牢なされるように、右のタイプとデータ量によるとテストと. と, 受賞システムは成功の適切な基準によって判断しなければなりません。. アルゴリズム取引ルールや最大ドローダウンのレベルに依存するだけではなく, システムが失敗したかどうかについての決定は、トレーダーの人間の判断によるとすべきであります。, システムの現在のパフォーマンスの歴史的なテスト損失に対して測定した場合の標準偏差の数の評価に基づいて. 機械的なトレーディング システムは実行に失敗した場合, トレーダーは、失うシステムにしがみつくのではなく必要な変更をする必要があります。.

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

によって 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.

system failure

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.

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

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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.

外国為替戦略

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.

The Difference Between Optimization and Curve-Fitting

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Optimization and curve-fitting are two terms that are very common among quantitative traders. They are so common that many traders confuse the terms, or use them as synonyms when they actually have very different meanings.

Michael Harris recently published a guest post on System Trader Success that broke down the meaning of each of these terms and explained how they interact with each other. He also shared a process for determining how likely a strategy was to be exposed to a curve-fitting bias that is based on how its parameters are utilized.

curve-fitting

Knowing the difference between optimization and curve-fitting can help you avoid exposing your strategy to backtesting biases.

Optimization vs. Curve-Fitting

Michael began by defining each of the two terms individually. What this shows us is that they have subtle differences with respect to each other. Here is how he explains it:

As already mentioned, curve fitting may involve optimization but the latter is a process with a much broader scope and includes many more possibilities than curve-fitting.

Michael looks at strategy optimization from the viewpoint of finding the best collection of entry and exit signals for a backtesting period. He explains that curve-fitting focuses more on the results than the signals that caused the result.

Is Curve-Fitting Really The Problem?

Another interesting point that Michael brings up is that there is no mathematical proof that optimized systems are more likely to fail because they are curve-fit. He suggests that it is possible for any optimized strategy to fail at any point, and that the strategy failure has nothing to do with what parameters the system uses.

He explains that a different form of bias is far more likely to cause a failure:

それにもかかわらず, optimization that causes selection of entry and exit collections is in general a problematic process because it introduces survivorship bias.

Michael argues that in almost every case where an optimized strategy fails, サバイバーシップ ・ バイアス is more likely to blame than a curve-fitting bias.

How To Gauge Optimized Trading Strategies

While Michael does not believe that curve-fitting failures are nearly as prevalent as many traders believe, he does discuss how some strategies are more likely to be exposed to curve-fitting than others. In order to gauge how likely an optimized strategy is to be exposed to curve-fitting, Michael divides them into three different classes.

The first class contains strategies where the optimized parameters define both the entry and exit signals. These strategies are the most vulnerable to curve-fitting.

The second class contains strategies where just the entry signals are defined by optimized parameters. These strategies are less likely to be exposed to curve-fitting than those in the first class.

The third class contains strategies where the optimized parameters define only the exit signals. These strategies are the least likely to be exposed to curve-fitting.

 

Have You Prepared For System Failure?

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One of the misconceptions that many quantitative traders fall prey to is neglecting to consider that their strategy will eventually stop working. We are led to believe that once we develop and backtest a profitable strategy, we will simply be able to print money indefinitely. しかし, this is almost never the case.

Due to the unpredictable nature of financial markets, all systems and strategies will eventually fail. 少なくとも, they will need to be adjusted. This means that developing a trading strategy is an ongoing process, not a one-time project.

system failure

Eventual system failure is inevitable for all types of strategies. Are you prepared for it to happen to you?

Daniel Fernandez from Mechanical Forex wrote a post on this topic earlier this week. He suggests that the ability to detect system failure with as little pain as possible is a pivotally important aspect of Forex strategy development. He explains why all quantitative strategies are bound to fail eventually:

Eventual system failure – what we can call system death – is an inevitable consequence of an edge developed on a finite amount of information on a market with potentially infinite variations.

Detecting System Failure

Fernandez made some particularly interesting points about the process of detecting potential system failure. In order to detect that a strategy no longer working, a trader will most likely have to go through a difficult losing period.

Through extensive バックテスト, walk-forward testing, and monte carlo simulations, a trader can establish parameters that describe a normal losing period for a given strategy. In order for that trader to determine system failure, they will have to trade through that normal losing period and then some.

The interesting concept that Fernandez brings up is that different strategies will have different conditions for those standard losing periods.

Low Win Ratio Strategies

Trading systems that are based on low winning percentages and high reward to risk ratios are expected to have long losing streaks. したがって, it would take an exceptionally long losing streak to signal that system failure is possible.

Fernandez also adds that these types of strategies often rely on a few very profitable trades to make up for lots of small losses. That means that missing one key trade could result in a false signal that the system has failed.

High Win Ratio Strategies

Strategies based on high winning percentages and low reward to risk ratios pose the exact opposite problem. They experience much shorter losing streaks, so they are able to identify system failure much sooner.

もちろんです, the losses that these types of systems do take are often very large. While it might be a short string of losses that identifies the system failure, those losses are likely going to be incredibly painful.

Best Strategies for Detecting Failure

Fernandez concludes that the systems that provide the least painful means of detecting system failure are strategies with moderate winning percentages and reward to risk ratios.

He suggests that systems with reward to risk ratios around 1 宛先 1 と winning percentages just over 50% are able to signal failure in the best manner. These types of strategies can signal failure quickly, without crippling the buying power of an account.

Trading Frequency

The last topic that Fernandez mentions is the trading frequency of a strategy. もう一度, he suggests targeting a middle-of-the-road approach.

The fact that high-frequency trading systems can run through long losing streaks quickly might be seen as an advantage. Fernandez points out that this can be a double edge sword. Short term disruptions in market behavior can lead to false signals of system failure.

 

転送最適化を歩く: A More Detailed Explanation

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Two weeks ago, we looked at an example of using walk forward optimization by VBO Systems that tested a volatility breakout system. While this article was interesting from a nuts and bolts aspect, it left a lot on the table in terms of explanation.

The author has since expanded that post to include a more detailed explanation of exactly what we are trying to do with walk forward optimization. This new introduction to the article provides us with some details and background on why walk forward optimization is so effective.

walk forward optimization

Walk forward optimization allows up to test how a strategy would have traded in a live environment and evaluate which parameters would have performed best.

The article starts by listing some of the reasons that systems can lose their edge:

  • The system is not based on a valid premise
  • Market conditions have changed in a dramatic way that invalids the theoretical premises on which the system was developed
  • The system has not been developed and tested with a sound methodology. 例えば, (、) lack of robustness in a system due to improper parameters, と (b) inconsistent rules and improper testing of the system using out-of-sample and in-sample data

It continues by explaining how a basic walk forward optimization is conducted:

Walk forward analysis is the process of optimizing a trading system using a limited set of parameters, and then testing the best optimized parameter set on out-of-sample data.

This process is similar to how a trader would use an automated trading system in real live trading. The in sample time window is shifted forward by the period covered by the out-of-sample test, and the process is repeated.

At the end of the test, all of the recorded results are used to assess the trading strategy.

In order to make sure the concept is understood, it is also explained another way:

他の言葉で, walk forward analysis does optimization on a training set; tests on a period after the set and then rolls it all forward and repeats the process.

We have multiple out-of-sample periods and look at these results combined. Walk forward testing is a specific application of a technique known as Cross-validation.

It means taking a segment of data to optimize a system, and another segment of data to validate. This gives a larger out-of-sample period and allows the system developer to see how stable the system is over time.

As we covered in the previous post, there are three main aspects of this process:

  1. Define in-sample and out-of-sample periods
  2. Define a robust parameters area
  3. Execute the walk forward

あなたが見ることができます。, performing a walk forward optimization on a system that you are developing will help you to gain an understanding about how a system will perform in real time, while at the same time finding the optimal parameters for the strategy.

転送最適化を歩く: Explained in Plain English

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Traders that gravitate towards quantitative strategies are typically nerds.

I don’t mean that in a negative way, because I consider myself one as well. しかし, nerds have a tendency to speak and write using far more complex words and sentences than actually necessary.

Because of that tendency, entry-level explanations can often be confusing for beginners to understand.

This comes across especially well on the topic of walk forward optimization. Most of the articles about the topic are very complex and involve some high level math. This can be extremely discouraging for someone just looking into systematic trading strategies.

walk forward optimization

Many quantitative traders have a hard time explaining walk forward optimization in simple terms.

VBO Systems posted a very helpful case study using walk forward optimization this week. They started by briefly explaining the three main steps of their walk forward optimization process:

  1. Define in-sample and out-of-sample periods
  2. Define a robust parameters area
  3. Execute the walk forward

That’s simple enough. 次, they specified the system and data that they would be using for the case study:

For this test we will use the FDAX and a volatility breakout (VBO) intraday trading system.

We will use NinjaTrader and CQG historical 1-minute data, assuming 3 points of slippage for each R/T trade to cover trading frictions.

The first step in their process was to identify the in-sample and out-of-sample periods. Here is how they explained it:

We will choose as in-sample 1/1/2001 宛先 12/31/2009 for system design and in-sample optimization and 1/1/2010 宛先 12/31/2012 as out-of-sample period to evaluate the in-sample optimization robustness and execute the walk forward. We will then use a 3:1 ratio for the WFO (walk forward optimization):

  • Optimize 2007 宛先 2009 and verify performance out-of-sample in 2010
  • Optimize 2008 宛先 2010 and verify performance out-of-sample in 2011
  • Optimize 2009 宛先 2011 and verify performance out-of-sample in 2012

The next step is to define the parameters that they are looking to optimize. Here are the three that they listed:

  • Lookback period of the fast average
  • Lookback period of the slow average
  • Volatility filter

これまでのところ, this has been a pretty simple process, and VBO Systems does a great job of keeping their explanations simple. In order to define the robust area for each of these parameters, the article uses a 3D chart to identify the moving average lookback periods that perform reasonably well over the course of the in-sample data. The same process is applied on a standard chart to get the volatility filter parameters.

The final step is to perform the walk forward using the identified data parameters on the defined data periods. 基本的には, they just see which moving averages and volatility filter would have worked best on each of the in-sample data periods, and then test those parameters on the out-of-sample data periods to see if the returns are in-line with expectations.

The result of this case study is that each of the walk forward optimization periods produce similar returns to the overall system returns for the entire in-sample data set. This gives reassurances that the strategy has a certain level of robustness.

AAPL の短期ボリンジャー バンドのブレイク アウト戦略アイデア

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人気の先物と外国為替市場に加えて, テストし、液体とアクティブな個々 の株式の戦略を貿易のような多くの定量的なトレーダー.

AAPL はいずれかの世界で最も活発な株式, 出来高とニュース報道. それはまた過去の 10 年にわたって信じられないほど強気にされて. AAPL があり、人気と報道をされている在庫はそれ用に設計されたいくつかの戦略のカスタム行き.

Paststat.com は、トレーダーが取るし、潜在的な戦略の開発を奨励するさまざまな定量的なアイデアに彼らのウェブサイトの全体のセクションを捧げる. 彼らの最近の記事の 1 つはのためのアイデアを紹介します。 短期ブレイク アウト システム AAPL の貿易のために特別に設計されています.

戦略の基本的な考え方は、時間がかかる AAPL のロング ポジションの在庫が勃発し、その上のボリンジャー バンドの上で閉まる. 戦略は、間の在庫を保持します。 1 と 5 販売する前に日. 日足のボリンジャー バンドの設定とに基づいて戦略 20 移動平均期間と 2 標準偏差.

ボリンジャー バンドのブレイク アウト

この単純なボリンジャー バンドのブレイク アウト戦略 AAPL のために設計されています、 82% 率を獲得し、 1.48 過去数年間で利益率.

それは非常に簡単に含まれているチャートについて説明します:

今後の $AAPL ロングのため取引の確率 1/2/3/4/5 時まで期間日取引 $AAPL すぐ上のボリンジャー バンドの上で交差します。

記事 backtests 12 月のブレイク アウトからこの戦略 2009 11 月まで 2013. 戦略のシンプルさを考慮しました。, 結果は、実際にはかなり印象的です.

1 日の保留期間を使用してください。, 戦略生成 22 受賞者のうち 28 取引. 勝ちトレードの平均利益します。 1.04% 負けトレードの平均損失が、 0.57%.

保持期間は 5 日間に増加したとき, 増資戦略 23 wins. これらの勝ちトレードの平均利益 2.69% 負けトレードの平均損失だったと 1.82%.

記事は、5 日間の開催期間のアプローチについてバックテスト期間にわたって記録された取引のそれぞれの結果を投稿するに行く. これは本当に勝ちトレード数が多いを強調し、負けトレードよりも大きいが、勝ちトレードという事実.

これは、サイズの小さいサンプルでは確かに, 初期のバックテスト結果は、さらにいくつか実際の戦略にこれを開発する時間を投資する価値があることを示す. リターンがトレンド フィルターまたはいくつかの他の確認のインジケーターを実装することによって上改善される可能性があります。.

非常に取引 AAPL のこの戦略のさらなるテストを見るは興味深いだろう. それはまた他の個別銘柄にそれをテストするのには興味深いだろう, さらにそれを改良しようとして.