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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.

atr filter

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.

 

以下の下でファイルさ: 戦略の取引のアイデア タグが付いて: atr, フィルター, market conditions

When Does Adding A Secondary Filter Increase Performance?

2 月 13, 2014 によって アンドリュー ・ セルビー Leave a Comment

Earlier posts this week addressed the need to keep a strategy simple in order to avoid exposure to curve-fitting bias. While that is true in a general sense, there are also times when adding a level of complication to a strategy can improve its performance without significantly increasing its risk of ruin.

We saw an example of this with Shaun’s Euro Scalping Strategy. When a timing filter is applied that only trades the strategy during slower trading times, the performance metrics shot up, giving us a very interesting system. What would happen if we added a second layer to this filter?

フィルター

Adding multiple filters can help a strategy single out the very best trades, but it can also expose a strategy to curve-fitting bias.

Jeff from System Trader Success took his research on Shaun’s Euro Scalping Strategy a step further this week. He decided to experiment with introducing a volatility filter to the strategy. Once he determined the optimal parameters for the filter, he tested how it would improve the base scalping strategy as well as the time filtered version.

Optimizing the Volatility Filter

Jeff’s goal was to use daily price action to determine how Shaun’s Euro Scalping Strategy performed on days where volatility was high and compare that with how the strategy performed on days where volatility was low.

The biggest lesson here is that in order to optimize the filter, Jeff went back to the original scalping strategy. In order to get the most amount of data and limit curve-fitting, he scrapped the time filter for now.

The lesson here is that if you are going to use multiple filters, it is a good idea to build, テスト, and optimize them separately. Then you can stack them together. Stacking the filters while optimizing them can skew the data in a way that confuses which filters are actually working.

The Volatility Filter

Applying the volatility filter to the original scalping strategy slightly improved the strategy. It increased the profit factor from 1.38 宛先 1.43 while significantly lowering the maximum drawdown. The average profit per trade rose from $24.58 宛先 $25.45.

The win rate was kept pretty much the same, and the overall net profit was reduce because 128 trades were filtered out. あなたが見ることができます。, there was definitely improvement, but not nearly as much as with the time filter.

Combining Filters

While the results of applying the volatility filter to the original strategy were not impressive, the original plan was to apply the filter to the time filtered strategy. Putting the two filters together produces the best version of the scalping strategy yet.

The combined filters improve the profit factor to 3.44 and the average profit per trade to $56.74. The maximum drawdown is reduced to less than half of the maximum drawdown of the original system.

The new combined filter strategy reduces the total trades to 164, where the time filtered strategy had 233 and the original strategy had 456. Despite how few trades the combined filtered strategy makes, it returns almost as much net profit as the original strategy.

What we have done with these combined filters is find a way to isolate the very best of the best trades from the original strategy.

 

 

以下の下でファイルさ: 外国為替市場のしくみ?, 戦略の取引のアイデア タグが付いて: euro scalping, フィルター, 戦略

取引をフィルタするために、より高い時間フレームとトレンドの方向を使用する方法

5 月 14, 2013 によって エドワード ・ ロマックス Leave a Comment

最近, I’ve been going over the characteristics of a solid trading strategy. The idea is to help you build a profitable trading strategy that can be programmed into an expert advisor. これまでのところ, I’ve gone over choosing the time frame you want to trade and determining trend direction. If you missed the previous posts, here are the links:

  • Deciding Trading Strategy Time Frame
  • Deciding Trading Strategy Trend Direction

Before I go forward with other characteristics of a solid trading system, I think we should go over using higher time frames and trend direction to filter trades. But first I want to answer a question some of you might be asking…

Why filter trades when using an expert advisor for automated trading?

Through many conversations with up and coming traders, I feel some people get the wrong idea about automated trading and using expert advisors. Here are two themes that come up often…

Automated Trading Is BETTER Than Manual Trading. When people hear words like “robot” they often think “better than human”. The truth is, the robot is only as good as the strategy programmed into it. Automated trading can perform the tasks of trading better than an human (24 日時間, no emotions, など), but the strategy still comes from a real human being and needs to mimic what a human would do according to the rules of the trading strategy.

Automated Trading Means You Are In The Market More. There is a difference between taking more trades, (being in the market more often), and not missing high probability trading opportunities. The point of automating your trading strategy is to monitor the market on autopilot and not miss excellent trading opportunities (which can happen to a human trader). It does not mean you should try to be in the market all the time or take many trades at one time.

The point is this…

Your trading strategy should strive to give your trading an edge and only enter the market with the highest probability trade setups. 多くの場合, this means trading LESS. You need a way to filter the trades and keep you out of the market when the conditions are not correct.

Examples Of Using Higher Time Frames And Trend Direction To Filter Trades

Independent of which time frame you choose to make your trading decisions, you have to admit higher time frames give you a better perspective on overall market activity. Looking at the Daily time frame gives you a better view of the market than looking at the 15 Minute time frame. だから, even if you are taking trades off a lower time frame, it is a good idea to look at the higher time frames to make sure you are making a wise decision.

Here is an example of how you can use higher time frames to filter trades on a lower time frame. Let’s say we are trading a Moving Average Crossover on the Hour charts. It is a good idea to use the 4 Hour Charts to filter your trades.

In this photo, you see the H4 charts and where the Moving Average crossover occurs. From this point on, you should only look for SHORT opportunities on the H1 time frame.

H4 filter

A 4 hour chart helps filter trades using H1 charts

In the next photo we are on the H1 time frame. You can plainly see where we should be looking for short trades. Using the higher time frame filtering method would have kept you out of a losing long trade.

Time frame filter

Using a higher time frame filter eliminated a losing trade

This was just a very simple example using Moving Average Crossovers. The truth is, any indicator you are using can be used in the same way.

If you were manually trading, you could simply look at the Daily charts and visually see the trend direction, use trendlines, など. しかし, if you are programming an expert advisor, you need some way of programming in the higher time frame filter, and indicators work better for this.

Will This Method Filter Out Good Trades As Well?

[はい], もちろんです. Filtering out good trades is going to happen. And I think this really is the problem people have with using a filtering system for their trading strategy. They just don’t want to miss ANY opportunity to get into the market.

私の考えでは, this is a question of Maximization vs Optimization. Most people want a trading strategy (or expert advisor) to maximize profits. This means somehow figuring out how to take advantage of EVERY trading opportunity.

I would argue that optimizing your trading strategy (or expert advisor) by using a higher time frame filter is better. [はい], you are going to miss some trades that would have been winners. But sitting on the sidelines during periods that are not right for trading is easier to handle emotionally than suffering through a string of losses. In the long run, filtering can greatly increase your win rate and produce a smoother equity curve.

If you’ve already programmed a trading strategy into an expert advisor, could it be improved by adding a higher time frame trend filter?

シリーズの次の記事を読みます: Picking trade entry signals

以下の下でファイルさ: 戦略の取引のアイデア タグが付いて: フィルター, higher time frame

ボラティリティ & 分岐解説

2 月 17, 2012 によって ショーンオバートン 2 コメント

この週はずっと考え 1 週間. クライアントの異常な数にするアイデアに私の意見を求めています。 専門家アドバイザーにプログラム. 発散とボラティリティ現れ続けるテーマとして 1 週間.

単純なボラティリティ フィルター

ボラティリティ取引で無視できないそれらの要因の一つは、します。. 市場の全体的なリスク状況を示し、いくつかの車輪を取得する貿易のための可能性について何か言う.

ボラティリティを勉強するツールの数は残念ながら非常に限られました。. ATR を使用するほぼ全員, 平均該当範囲であります。. それの計算は非常に基本的です. 本当の範囲は単に低いマイナス高です。. ATR は一定期間にわたってすべての true の範囲の平均単にであります。. ほとんどのトレーダーが使用して、 14 慣例では ATR の期間.

下のグラフをボラティリティ フィルターの任意のアイデアがあれば質問昨日オーストラリアでクライアントに送信されます。. ATR を使用してウィンドウを高速と低速のボラティリティを比較します。. 赤い線を表します、 14 ATR の期間, 高速の電話を. 青い線を表します、 300 ATR の期間, 低速の電話を. 彼は遅い回線上高揮発性の指標として表示される高速ラインを使用できる期間を提案しました。. 反対の徴候を示す低揮発性.

ATR Trend

傾向に信号を送るかもしれない ATR の 2 行, 彼らは方向を示しませんが.

ドラッグし、グラフに ATR カスタム インジケーターをドロップで上記のグラフを作成. それから最初の ATR インジケーター上にドラッグし、2 番目の ATR インジケーター. その方法を行う重複行. 0読み進めるうちに, 別のウィンドウで 2 つの行が表示されます。.

再度、この朝にメタト レーダーを開く, 同じグラフが開いた残っていた. 私はすぐに、長期トレンドのいくつかと一致するラインの交差が登場したことに気づいた. それはトレンドの方向を示していないが, ATR 踏切がトレンド検出の指標として役に立つ. このアイデアを研究にする場合, 下記のブログのページにあなたのコメントや観察を残してください。. 私の読者から公聴会を楽しむ.

発散

私はアイデアを市場に他のものよりより関連している価格ポイントが含まれていることを購入します。. 私が扱う数学の多くを含む自己相関, 多くは長期メモリ関数としてを参照してください。. それは信号のノイズの束の中の隠された統計パターンを見つける私のようなオタクができる数学的なツール.

発散と同様の考えを取るし、それを指標に適用, 最も一般的なものは、MACD です。, RSI やストキャスティックス. 以前の臨界点とインジケーターを価格が上回ったときはその前の重要なポイントを超えない, 分岐が存在するし、. ほとんどのトレーダーは、発散傾向の潜在的な終了が通知されることを主張します。.

発散と私の最大の不満はトレンドの長さがランダムな期間を示すこと. このトピックの独立した研究の多くを行ってきた. 市場のトップとボトムスや傾向を定義する方法を選択するために使用するメソッドにかかわらず, 測定トレンドの期間は常にランダム. それは確率密度を持っています。, それは間違いなくセット数はありません。.

発散は完全にこの問題に対処するため失敗します。. なぜ有することができない理由はないです。 2 発散あるいは 5 傾向の相違. 発散はトレーダー、トレンドまたは継続的トレンドの終わりの間で区別を助けない. 発散傾向の検出ツールとして使用できます。, その時点で一部のトレーダーは既に終了するために呼んでいるが、. 私の個人的な意見はそれがあまり便利ではありません。.

発散と私の他の不満は臨界点を選ぶための方法は全く不定. あなたが置く場合 10 部屋でトレーダー、トレンド ラインを描画するように依頼, 表示されます。 10 別の回答. このような基本的な考え方のコンセンサスの欠如は、主観的な解釈の価値についてずいぶんと言うべき.

トレーダーもスイングの高値と安値の間のポイントを描画しようとすると. そのタスクを明らかにする必要があります。, それはないが、. 私は常にお客様がダウン スイングの取引ルートに行きたい場合、ジグザグ ジグザグ インジケーターを使用してお勧めします. 彼らはすぐに同じ問題を発見します。 – どのように区別する必要があります設定します。. もう一度, 我々 は期間の長さの問題に戻ってサークルします。. ボブのジグザグ設定が市場のスイング高値にノイズ、Alex のように見えるを描く高スイングを描画します.

私の意見は相違から離れて、他の技術を探す.

以下の下でファイルさ: メタト レーダーのヒント, 戦略の取引のアイデア タグが付いて: atr, 自己相関, 平均該当範囲, 発散, 専門家アドバイザー, フィルター, MACD, RSI, ストキャスティックス, 高いスイングします。, 低スイングします。, ボラティリティ, ジグザグ

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