Expert Advisor Builder Mitglied seit Oct 2009 Status: Mitglied 63 Beiträge 1. Identifiziert Trend Das Wave-Prinzip identifiziert die Richtung des dominierenden Trends. Ein Fünf-Wellen-Fortschritt identifiziert den Gesamttrend als hoch. Umgekehrt bestimmt eine Fünf-Wellen-Abnahme, dass der grßere Trend nach unten ist. Warum ist diese Informationen wichtig Weil es einfacher ist, in Richtung der dominanten Trend zu handeln, da es der Weg des geringsten Widerstands ist und zweifellos erklärt das Sprichwort, der Trend ist dein Freund. Einfach ausgedrückt, ist die Wahrscheinlichkeit eines erfolgreichen Rohstoffhandels viel größer, wenn ein Trader lange Sojabohnen ist, wenn die anderen Körner sammeln. 2. Identifiziert Countertrend Das Wave-Prinzip identifiziert Gegenbewegungen. Das Drei-Wellen-Muster ist eine Korrekturantwort auf die vorangehende Impulswelle. Wissend, dass eine kürzliche Kursbewegung nur eine Korrektur innerhalb eines größeren Trends ist, ist für die Händler besonders wichtig, da Korrekturen die Chancen für die Händler sind, sich in Richtung der größeren Marktentwicklung zu positionieren. 3. Bestimmt die Reife eines Trendes Wie Elliott beobachtet, bilden Wellenmuster größere und kleinere Versionen von sich. Diese Wiederholung in Form bedeutet, dass die Preisaktivität fraktal ist, wie in Abbildung 2-1 dargestellt. Welle (1) unterteilt sich in fünf kleine Wellen, ist aber Teil eines größeren Fünf-Wellen-Musters. Wie ist diese Information nützlich Es hilft Händler erkennen die Reife eines Trends. Wenn die Preise in Welle 5 eines Fünf-Wellen-Fortschritts zum Beispiel voranschreiten und Welle 5 bereits drei oder vier kleinere Wellen abgeschlossen hat, weiß ein Trader, dass dies nicht die Zeit ist, lange Positionen hinzuzufügen. Stattdessen kann es Zeit sein, Gewinne zu ergattern oder zumindest Schutzstopps zu erheben. Da das Wave-Prinzip Trend, Gegenströmung und die Reife eines Trends identifiziert, ist es nicht verwunderlich, dass das Wellenprinzip auch die Rückkehr des dominierenden Trends signalisiert. Sobald sich eine Gegenbewegung in drei Wellen entfaltet (ABC), kann diese Struktur den Punkt signalisieren, an dem die vorherrschende Tendenz wieder auftaucht, nämlich wenn die Preisaktion das Extrem der Welle B übersteigt Steigert die Wahrscheinlichkeit eines erfolgreichen Handels, der in Verbindung mit traditionellen Techniken weiter ausgebaut wird, wenn es von Traditionen begleitet wird: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage002.jpg/IMGal technische Studien. Abbildung 2-1 4. Bietet Preisziele Was traditionelle technische Studien einfach nicht bieten hohe Wahrscheinlichkeit Preisziele der Wave Principle wieder bietet. Wenn R. N. Elliott schrieb über das Wellenprinzip im Naturrecht, er erklärte, dass die Fibonacci-Sequenz die mathematische Grundlage für das Wellenprinzip sei. Elliott-Wellen, sowohl impulsiv als auch korrigierend, halten sich an spezifische Fibonacci-Proportionen, wie in Abbildung 2-2 dargestellt. Zum Beispiel sind gemeinsame Ziele für die Welle 3 1,618 und 2,618 Vielfache der Welle 1. Bei Korrekturen endet die Welle 2 typischerweise in der Nähe des .618-Rückzugs der Welle 1, und Welle 4 testet häufig die .382-Rückführung der Welle 3. Diese hohe Wahrscheinlichkeit Preis Zielvorgaben erlauben es den Wirtschaftsteilnehmern, Ertragsziele festzulegen oder Regionen zu identifizieren, in denen die nächste Kursrunde stattfindet. IMG-Datei: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage004.jpg/IMG Abbildung 2-2 5. Gibt spezifische Ruinpunkte an Zu welchem Zeitpunkt handelt ein Trade-Ausfall Viele Händler verwenden Money-Management Regeln, um die Antwort auf diese Frage zu bestimmen, weil technische Studien einfach nicht bieten. Das Wellenprinzip funktioniert jedoch in Form von Elliott-Wellenregeln. Regel 1: Welle 2 kann nie mehr als 100 der Welle 1 zurückverfolgen. Regel 2: Welle 4 darf niemals im Preisgebiet der Welle 1 enden. Regel 3: Von den drei Impulswellen 1, 3 und 5 Welle 3 kann niemals sein der kürzeste. Ein Verstoß gegen eine oder mehrere dieser Regeln impliziert, dass die Betriebswellenzählung nicht korrekt ist. Wie können Händler diese Informationen nutzen? Wenn eine technische Studie vor einem Aufschwung der Preise warnt und das Wellenmuster ein Second-Way-Pullback ist, weiß der Trader genau, an welcher Stelle der Trade eine Bewegung über den Ursprung der Welle 1 hinaus versagt Der Führung ist schwierig, ohne Rahmen wie das Wellenprinzip zu kommen. Was Trading Chancen macht das Wave-Prinzip Identes Heres, wo der Gummi die Straße trifft. Das Wellenprinzip kann auch hochwahrscheinliche Trades über Handelskonfigurationen identifizieren, die Händler ignorieren sollten, und zwar durch Ausnutzung der Wellen (3), (5), (A) und (C). Warum Seit fünf Wellenbewegungen bestimmen die Richtung der größeren Trend, bieten drei Wellenbewegungen Händler eine Gelegenheit, sich dem Trend. In Abb. 2-3 sind also die Wellen (2), (4), (5) und (B) für Hochwahrscheinlichkeiten in den Wellen (3), (5), (A) und (C) vorgesehen. Beispielsweise bietet ein Pullback der Welle (2) dem Händler die Möglichkeit, sich in der Richtung der Welle (3) zu positionieren, so wie die Welle (5) in der Welle (A) eine Shorting-Chance bietet. Durch die Kombination des Wave-Prinzips mit der traditionellen technischen Analyse können Händler ihren Handel verbessern, indem sie die Wahrscheinlichkeit eines erfolgreichen Handels erhöhen. Technische Studien können herausgreifen viele Handelschancen, aber das Wave-Prinzip hilft Händler, zu erkennen, welche haben die höchste Wahrscheinlichkeit, erfolgreich zu sein. Das ist, weil das Wellenprinzip das Rahmenwerk ist, das Geschichte, aktuelle Informationen und einen Blick in die Zukunft liefert. Wenn Händler ihre technischen Studien in diesem starken Rahmen platzieren, haben sie eine bessere Grundlage für das Verständnis der aktuellen Preispolitik. IMG-Datei: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage006.jpg/IMG Abbildung 2-3 So verwenden Sie das Wellenprinzip, um Schutzstopps festzulegen Ive beachtete, dass obwohl das Wellenprinzip hoch ist Als ein analytisches Instrument angesehen, verlassen viele Händler es, wenn sie in Echtzeit-Handel, weil sie es nicht die definierten Regeln und Leitlinien eines typischen Handelssystems. Aber nicht so schnell, obwohl das Wave-Prinzip ist kein Handelssystem, seine eingebaute Regeln zeigen Ihnen, wo Sie Schutz Stopps in Echtzeit-Handel statt. Und das werde ich dir in dieser Lektion zeigen. Im Laufe der Jahre, dass Ive mit Elliott Wellenanalyse gearbeitet, Ive gelernt, dass Sie viele der Informationen, die Sie benötigen, wie ein Trader wie, wo Sie Schutz oder nachgelagerten Stopps von den drei Kardinal Regeln des Wave-Prinzip: Kann nie mehr als 100 der Welle eins zurückverfolgen. 2. Welle vier darf nie im Preisgebiet der Welle eins enden. 3. Welle drei kann nie die kürzeste Impulswelle der Wellen eins, drei und fünf sein. Beginnen wir mit Regel Nr. 1: Welle zwei wird nie mehr als 100 der Welle eins zurückverfolgen. In Abbildung 4-1 haben wir einen fivewave-Fortschritt, gefolgt von einem Drei-Wellen-Abfall, den wir Wellen (1) und (2) nennen werden. Eine wichtige Sache, um über zweite Wellen erinnern ist, dass sie in der Regel mehr als die Hälfte der Welle ein, meist eine machen ein .618 Fibonacci Retracement der Welle ein. Also in Erwartung einer dritten Welle Rallye ist, wo die Preise normalerweise reisen die am weitesten in der kürzesten Zeit, die Sie suchen sollten, um bei oder in der Nähe der .618 Retracement der Welle eins zu kaufen. Wo die Haltestelle. Sobald eine lange Position eingeleitet wird, kann ein Tick unterhalb des Ursprungs der Welle (1) gesetzt werden. Wenn Welle 2 mehr als 100 der Welle 1 zurückverfolgt, kann die Bewegung nicht mehr als Welle 2 bezeichnet werden. IMG-Datei: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage008.jpg/IMG Abbildung 4-1 Nun untersuchen wir Regel Nr. 2: Welle vier wird nie im Preisgebiet der Welle enden eins. Diese Regel ist nützlich, weil sie Ihnen helfen, schützende Stopps in Erwartung der Fang eine fünfte Welle bewegen zu neuen Höhen. Die häufigsten Fibonacci Retracement für vierte Wellen ist .382 der Welle drei. Also nach einem erheblichen Preisvorteil in Welle drei, sollten Sie schauen, um lange Positionen nach einem Drei-Wellen-Rückgang, der bei oder in der Nähe der .382 Retracement der Welle drei endet. Wo die Haltestelle. Wie in Abbildung 4-2 gezeigt, sollte der Schutzstopp ein Tick unterhalb des Extremwerts der Welle (1) gehen. Etwas ist falsch mit der Wellenzahl, wenn das, was Sie als Welle vier Köpfe in das Preisgebiet der Welle eins markiert haben. IMG-Datei: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage010.jpg/IMG Abbildung 4-2 Und schließlich Regel Nr. 3: Welle drei wird nie die kürzeste Impulswelle von sein Wellen eins, drei und fünf. Typischerweise ist Welle drei die Welle, die am weitesten in einer Impulswelle oder fünf-Wellen-Bewegung bewegt, aber nicht immer. In bestimmten Situationen (wie z. B. innerhalb eines diagonalen Dreiecks) bewegt sich Welle weiter als Welle drei. Wo die Haltestelle. Wenn dies geschieht, können Sie eine kurze Position mit einem Schutz-Stop ein Häkchen über dem Punkt betrachten, an dem Welle (5) länger als Welle (3) wird (siehe Abbildung 4-3). Warum Wenn Sie Preis-Aktion richtig markiert haben, wird Welle fünf nicht übergehen Welle drei in der Länge, wenn Welle drei ist bereits kürzer als Welle eins, kann es nicht auch kürzer als Welle fünf. Also, wenn Welle fünf deckt mehr Abstand in Bezug auf Preis als Welle drei so brechen Elliotts dritten Kardinal Regel dann seine Zeit, um Ihre Welle zu überdenken. IMG-Datei: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage012.jpg/IMG Abbildung 4-3 Über den Autor: Jeffrey Kennedy ist ein aktiver Trader und Erzieher bei Elliot Wave International. Er hilft, viele der von EWI angebotenen Unterrichtsmaterialien zu schaffen. Zusätzliche Unterrichtsmaterialien finden Sie auf der Website von EWI. Das Wellenprinzip setzt voraus, dass die kollektive Investorenpsychologie (oder die Psychopathologie) vom Optimismus zum Pessimismus und zurück in eine natürliche Sequenz geht, die spezifische Elliott-Wellenmuster in Kursbewegungen schafft. Diese Schaukel schaffen Muster, wie in den Preisbewegungen eines Marktes bei jedem Grad der Trend belegt. IMGfile: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage014.gif/IMG IMG-Datei: /// C: / Benutzer / Luke / AppData / Local / Temp / msohtml1 / 01 / clipimage015.gif / IMG Von RN Praktisch alle Entwicklungen, die sich aus (menschlichen) sozioökonomischen Prozessen ergeben, folgen einem Gesetz, das sie dazu veranlasst, sich in einer ähnlichen und ständig wiederkehrenden Reihe von Wellen bestimmter Anzahl und Muster zu wiederholen. R. N. Elliotts-Modell, in Natures Law: Das Geheimnis des Universums sagt, dass die Marktpreise wechseln zwischen fünf Wellen und drei Wellen in allen Graden innerhalb eines Trends, wie die Abbildung zeigt. Während sich diese Wellen entwickeln, entfalten sich die größeren Preismuster in einer selbstähnlichen Fraktalgeometrie. Innerhalb des vorherrschenden Trends sind die Wellen 1, 3 und 5 quadratische Quottwellen, und jede Motivwelle selbst unterteilt sich in fünf Wellen. Die Wellen 2 und 4 sind als rechteckige Wellen dargestellt und unterteilen sich in drei Wellen. In einem Bärenmarkt der vorherrschende Trend ist nach unten, so dass das Muster ist umgekehrt fünf Wellen nach unten und drei oben. Motivwellen bewegen sich immer mit dem Trend, während sich korrigierende Wellen dagegen bewegen. Grad Die Muster verbinden sich zu fünf - und drei - wellenförmigen Strukturen, die selbst selbstähnlichen Wellenstrukturen mit zunehmender Größe oder höherem Quotient zugeordnet sind. Beachten Sie die unteren drei idealisierten Zyklen. In der ersten kleinen Fünf-Wellen-Sequenz sind die Wellen 1, 3 und 5 treibend, während die Wellen 2 und 4 korrigierend sind. Dies signalisiert, dass die Bewegung der Welle um einen Grad höher ist. Es signalisiert auch den Beginn der ersten kleinen 3-Wellen-Korrektursequenz. Nach den anfänglichen fünf Wellen und drei Wellen nach unten beginnt die Sequenz wieder, und die selbstähnliche Fraktalgeometrie beginnt sich nach der Fünf - und Dreiwellenstruktur zu entfalten, die sie um einen Grad höher liegt. Das abgeschlossene Motivmuster umfasst 89 Wellen, gefolgt von einem abgeschlossenen Korrekturmuster von 55 Wellen. Jeder Grad eines Musters in einem Finanzmarkt hat einen Namen. Praktiker verwenden Symbole für jede Welle, um sowohl Funktion als auch Gradzahlen für Bewegungswellen anzuzeigen, Buchstaben für Korrekturwellen (gezeigt in der höchsten der drei idealisierten Reihen von Wellenstrukturen oder Graden). Degrees sind relativ, sie werden durch Form definiert, nicht durch absolute Größe oder Dauer. Wellen gleichen Grades können von sehr unterschiedlicher Größe und / oder Dauer sein. Die Klassifizierung einer Welle in einem bestimmten Ausmaß kann variieren, obwohl Praktiker in der Regel auf der Standardreihenfolge von Grad übereinstimmen (ungefähre Dauer): Superzyklus. Multi-Jahrzehnt (ca. 40-70 Jahre) Zyklus. Ein Jahr bis mehrere Jahre (oder sogar mehrere Jahrzehnte unter einem Elliott Extension) Primary. Ein paar Monate bis ein paar Jahre Intermediate. Wochen bis Monate Minor. Wochen Minute. Tage Minuette. Stunden Subminute. Minuten Elliott-Wellenquotientalityquot und Verhaltensmerkmale Elliott beschrieb jede Welle als ein charakteristisches Volumenverhalten und eine Quotientalitätsquote in Bezug auf die damit verbundene Impuls - und Investorenstimmung. Der Quotient einer Welle reflektiert die Psychologie des Augenblicks. Das Verständnis, wie und warum die Wellen entwickeln, ist der Schlüssel zur Anwendung des Wellenprinzips und bestätigt eine korrekte Wellenzahl. Dieses Verständnis beinhaltet die Erkennung der nachfolgend beschriebenen Merkmale. Diese Wellencharakteristiken gehen von einem Bullenmarkt in Aktien aus. Die Merkmale gelten umgekehrt in Bärenmärkten. Fünf Wellenmuster (dominierender Trend) Drei Wellenmuster (Korrekturtrend) Welle 1: Welle eins ist selten an seinem Anfang offensichtlich. Wenn die erste Welle eines neuen Bullenmarktes beginnt, ist die fundamentale Nachricht fast allgemein negativ. Der bisherige Trend gilt als noch stark in Kraft. Fundamentalanalysten weiterhin ihre Ertragsschätzungen zu senken die Wirtschaft wahrscheinlich nicht stark aussehen. Sentiment-Umfragen sind ausgesprochen bärisch, Put-Optionen sind in Mode, und implizite Volatilität auf dem Optionsmarkt ist hoch. Das Volumen könnte etwas steigen, wenn die Preise steigen, aber nicht genug, um viele technische Analytiker zu alarmieren. Welle A: Korrekturen sind normalerweise schwerer zu erkennen als Impulsbewegungen. In der Welle A eines Bärenmarktes sind die fundamentalen Nachrichten meist noch positiv. Die meisten Analysten sehen den Rückgang als Korrektur in einem noch aktiven Stiermarkt. Einige technische Indikatoren, die Welle A begleiten, beinhalten ein erhöhtes Volumen, eine steigende implizite Volatilität auf den Optionsmärkten und möglicherweise eine höhere Zinshöhe an verbundenen Futures-Märkten. Welle 2: Welle 2 korrigiert Welle 1, kann aber nie über den Ausgangspunkt der Welle 1 hinausgehen. In der Regel ist die Nachricht immer noch schlecht. Da die Preise die vorherigen niedrigen testen, bärige Gefühle schnell baut, und quotthe crowdquot hochmütig daran erinnert, dass der Bärenmarkt noch tief umsorgt ist. Dennoch sind einige positive Signale für diejenigen, die suchen: Volumen sollte niedriger sein, während der Welle zwei als während der Welle ein, Preise in der Regel nicht mehr als 61,8 (siehe Fibonacci Abschnitt weiter unten) der Welle eine Gewinne, und die Preise sollten in einem fallen Drei Wellenmuster. Welle B: Die Preise reverse höher, was viele als eine Wiederaufnahme des nun längst vergangenen Hauses sehen. Jene, die mit klassischer technischer Analyse vertraut sind, können die Spitze als die rechte Schulter eines Kopf - und Schulterumkehrmusters sehen. Das Volumen während der Welle B sollte niedriger sein als in Welle A. In diesem Punkt sind die Fundamentaldaten wahrscheinlich nicht mehr besser, aber sie sind höchstwahrscheinlich noch nicht negativ. Welle 3: Welle drei ist in der Regel die größte und stärkste Welle in einem Trend (obwohl einige Forschung deutet darauf hin, dass in Rohstoffmärkten Welle fünf ist der größte). Die Nachrichten sind jetzt positiv und grundlegende Analytiker beginnen, Einkommenschätzungen zu erheben. Die Preise steigen schnell, Korrekturen sind kurzlebig und flach. Jedermann, das schaut, um in einem Pullbackquot zu quoten, vermißt vermutlich das Boot. Als Welle drei beginnt, ist die Nachricht wahrscheinlich noch bärisch, und die meisten Marktteilnehmer negativ bleiben, sondern durch Welle drei Mittelpunkt, der Prechter Punkt. Quotthe Crowdquot wird oft den neuen bullish Trend beitreten. Welle drei verlängert häufig Welle eins durch ein Verhältnis von 1.618: 1. Welle C. Die Preise bewegen sich impulsiv in fünf Wellen. Volumen nimmt auf, und durch das dritte Bein der Welle C, fast jeder erkennt, dass ein Bärenmarkt fest verankert ist. Die Welle C ist typischerweise mindestens so groß wie die Welle A und erstreckt sich oft auf das 1,618-fache der Welle A oder darüber hinaus. Welle 4: Welle 4 ist typischerweise eindeutig korrigierend. Die Preise können seitwärts für einen längeren Zeitraum schlängeln, und Welle vier typischerweise zurückverfolgt weniger als 38.2 von Welle drei. Das Volumen liegt deutlich unter dem von Welle drei. Dies ist ein guter Ort, um einen Rückzug zu kaufen, wenn Sie das Potential vor der Welle 5 zu verstehen. Dennoch ist die wichtigste Eigenschaft der vierten Wellen, dass sie oft sehr schwer zu zählen. Wave 5: Wave five ist das letzte Bein in Richtung des dominanten Trends. Die Nachrichten sind fast allgemein positiv und jeder ist bullish. Leider ist dies, wenn viele durchschnittliche Investoren schließlich kaufen, direkt vor der Spitze. Das Volumen ist in Welle fünf niedriger als in Welle drei, und viele Impulsindikatoren beginnen, Divergenzen zu zeigen (Preise erreichen ein neues Hoch, der Indikator erreicht keinen neuen Höchststand). Am Ende eines großen Bullenmarktes, können Bären sehr gut lächerlich gemacht werden (erinnern Sie sich, wie Vorhersagen für eine Oberseite an der Börse während des 2000 empfangen wurden). Mustererkennung und Fraktale Elliotts Marktmodell stützt sich stark auf das Betrachten von Preis-Charts. Praktiker studieren die Entwicklung von Preisbewegungen, um die Wellen und Wellenstrukturen zu unterscheiden und zu unterscheiden, welche Preise als nächstes tun können, ist die Anwendung des Wellenprinzips eine Form der Mustererkennung. Die beschriebenen Strukturen von Elliott entsprechen auch der allgemeinen Definition eines fraktalen (selbstähnliche Muster, die bei jedem Grad der Tendenz auftauchen). Elliott-Welle Praktiker sagen, dass ebenso wie natürlich vorkommende Fraktale oft erweitern und wachsen komplexer im Laufe der Zeit, zeigt das Modell, dass kollektive menschliche Psychologie entwickelt sich in natürlichen Mustern, über Kauf und Verkauf von Entscheidungen in den Marktpreisen wider: quotts, als ob wir irgendwie von programmiert sind Mathematik. Elliott Wellen Regeln und Richtlinien für die richtige Welle zählt Bei der Zählung Elliott Wellen gibt es drei Regeln, die nie in einem richtigen Zähler gebrochen werden können: Regel 1: Welle 2 kann nicht Gehen Sie unter das Tief der Welle 1. Regel 2: Von den drei Impulswellen1,3 und 5wave 3 kann nie die kürzeste sein. Regel 3: Wave 4 cant Ende im Bereich der Welle 1, außer im seltenen Fall eines diagonalen Dreiecks. Ein Verstoß gegen diese Regeln impliziert, dass die Arbeitswellenzählung nicht korrekt ist. Lt5 gt Elliott bemerkte auch zusätzliche technische Aspekte von Wellen, um eine Richtlinie zur Korrektur von Wellenzählungen verwendet zu werden: Die meisten Motivwellen haben die Form eines Impulses, dh eines Fünfwellenmusters (wie in der obigen Tabelle gezeigt), in dem sich die Teilwelle 4 nicht überlappt Subwave 1 und subwave 3 ist nicht die kürzeste Subwave. Eine Motivwelle in einem Impuls d. h. 1,3 oder 5 wird typischerweise verlängert, d. h. viel länger als die beiden anderen. Es gibt zwei seltene Bewegungsvariationen, diagonale Dreiecke, die keilförmige Muster sind, die in einem Fall am Anfang auftreten (Welle 1 oder A und im anderen Fall nur am Ende (Welle 5 oder C von größeren Formen. Korrekturwellen haben zahlreiche Bei den Impulsen bewegen sich die Wellen 2 und 4 fast immer in der Form, wobei eine Korrektur typischerweise von der Zickzackfamilie und der Zickzackfamilie abhängt Lt6 gt Fibonacci Beziehungen RN Elliotts Analyse der mathematischen Eigenschaften von Wellen und Mustern schließlich führte ihn zu dem Schluss, dass die Fibonacci Summation Series Basis ist die Grundlage Des Wave Principle. quot2 Zahlen aus der Fibonacci-Sequenz-Oberfläche wiederholt in Elliott-Wellenstrukturen, einschließlich Motivwellen (1, 3, 5), einem einzigen vollen Zyklus (5 Up, 3 Down 8 Waves) und dem fertigen Motiv (89 Wellen ) Und Korrekturmustern (55 Wellen). Elliott entwickelte sein Marktmodell, bevor er realisierte, dass es die Fibonacci-Sequenz widerspiegelt. Wenn ich das Wave-Prinzip-Handeln der Markttrends entdeckte, hatte ich noch nie von der Fibonacci-Reihe oder dem pythagoreischen Diagramm gehört. Die Fibonacci-Sequenz ist ebenfalls eng mit dem Goldenen Verhältnis (ca. 1,618) verbunden. Praktiker verwenden dieses Verhältnis und die entsprechenden Verhältnisse häufig, um Unterstützung und Widerstandswerte für Marktwellen festzulegen, nämlich die Preispunkte, die dazu beitragen, die Parameter eines Trends festzulegen. Siehe Fibonacci Retracement. Finanzprofessor Roy Batchelor und Forscher Richard Ramyar, ein ehemaliger Direktor der United Kingdom Society of Technical Analysts und Leiter der britischen Asset Management Research bei Reuters Lipper, untersucht, ob Fibonacci-Verhältnisse nicht zufällig in der Börse erscheinen, wie Elliotts Modell prognostiziert. Die Forscher sagten, die Quote, dass die Preise auf ein Fibonacci-Verhältnis oder einen runden Bruchteil des vorherigen Trends zurückgehen, fehlt eindeutig an keiner wissenschaftlichen Begründung. Sie sagten auch, dass es keinen signifikanten Unterschied zwischen den Frequenzen gibt, mit denen Preis - und Zeitverhältnisse in Zyklen im Dow Jones auftreten Industrieller Durchschnitt, und Frequenzen, die wir erwarten würden, zufällig in einer solchen Zeitreihe auftreten. Robert Robert Prechter antwortete auf die BatchelorRamyar-Studie, dass es nicht die Gültigkeit eines Aspekts des Wellenprinzips in Frage stellen. Es unterstützt Wellen-Theoretiker Beobachtungen, und dass, weil die Autoren untersucht haben Verhältnisse zwischen Preisen in gefilterten Trends statt Elliott Wellen erreicht, quottheir Methode nicht auf tatsächliche Ansprüche von Wellen-Theoretiker. quotThe Socionomics Institute auch überprüft Daten in der BatchelorRamyar-Studie, und sagte Diese Daten zeigen quotFibonacci-Verhältnisse treten öfter an der Börse auf, als es in einer zufälligen Umgebung zu erwarten wäre. Beispiel für das Elliott-Wellenprinzip und die Fibonacci-Beziehung IMGfile: /// C: / Users / Luke / AppData / Local / Temp /msohtml1/01/clipimage016.jpg/IMG IMGfile: /// C: /Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage015.gif/IMG Von sakuragiindofx, quotTrading nie so einfach eh, Dezember 2007 Das GBP / JPY-Währungsdiagramm gibt ein Beispiel für einen vierten Wave-Retracement, der offenbar zwischen den 38,2 und 50,0 Fibonacci-Retracements einer abgeschlossenen dritten Welle anhält. Das Diagramm zeigt auch, wie das Elliott-Wellenprinzip mit anderen technischen Analyse-Tendenzen gut funktioniert, da die vorherige Unterstützung (der Boden der Welle-1) als Widerstand gegenüber Welle-4 wirkt. Die im Diagramm dargestellte Wellenzahl wäre ungültig, wenn sich GBP / JPY oberhalb der Welle-1 niedrig bewegt. Nach Elliott Nach Elliotts Tod im Jahr 1948, andere Markt-Techniker und Finanz-Profis weiterhin die Welle-Prinzip verwenden und Prognosen für Investoren. Charles Collins, der Elliotts quotWave Principlequot veröffentlicht hatte und half, Elliotts Theorie zur Wall Street einzuführen. Rangiert Elliotts Beiträge zur technischen Analyse auf einer Ebene mit Charles Dow. Hamilton Bolton, Gründer der Bank Credit Analyst. Lieferte Wellenanalyse zu einer breiten Leserschaft in den 1950er und 1960er Jahren. Bolton führte Elliotts-Wellenprinzip zu A. J. Frost, der in den achtziger Jahren wöchentliche Finanzkommentare zum Financial News Network lieferte. Frost co-authored Elliott-Wellenprinzip mit Robert Prechter 1979. Wiederentdeckung und gegenwärtiger Gebrauch Robert Prechter stieß auf Elliotts Arbeiten während der Arbeit als Markttechniker bei Merrill Lynch (Merrill Lynch). Er veröffentlicht The Elliott Wave Theorist monatlich seit 1979 und schrieb 25 Bücher über die Theorie. Sein Ruhm als Prognostiker während der Bullenmarkt der 1980er Jahre. George Soros sagte Ende Oktober 1987, Herr Prechters Umkehrung erwies sich als der Sprung, der die Lawine begann, brachte die größte Exposition bis heute Elliotts Theorie, und heute Prechter bleibt der bekannteste Elliott Analytiker. Unter den Markttechnikern wird die Wellenanalyse weithin als Bestandteil ihres Handels akzeptiert. Elliott-Wellen-Theorie ist unter den Methoden, die auf der Prüfung eingeschlossen werden, daß Analytiker übergeben müssen, um die Kennzeichnung des Chartered Market Technician (CMT) zu verdienen, die professionelle Akkreditierung, die durch die Market Technicians Association (MTA) entwickelt wird. Robin Wilkin, Global Head von FX und Commodity Technical Strategy bei JPMorgan Chase. Sagt Quotthe Elliott Wave-Prinzip bietet eine Wahrscheinlichkeit Rahmen, wann, um einen bestimmten Markt und wo man raus, ob für einen Gewinn oder ein loss. quot Jordan Kotick, Globale Leiter der technischen Strategie bei fx Capital und vergangenen Präsidenten der Markt-Techniker Association, hat gesagt, dass RN Elliotts quotdiscovery war weit vor seiner Zeit. In der Tat, in den letzten zehn Jahren oder zwei, viele prominente Wissenschaftler haben Elliotts Idee umarmt und haben aggressiv befürworten die Existenz der Finanzmarkt fraktals. quot Ein solcher Akademiker ist der Physiker Didier Sornette. Gastprofessor am Institut für Erd - und Raumwissenschaften und Institut für Geophysik und Planetenphysik an der UCLA. Sornette sagte: "Es ist faszinierend, dass die hier dokumentierten log-periodischen Strukturen eine Ähnlichkeit mit den Elliott-Wellen der technischen Analyse aufweisen. In der Finanzbranche wurden sowohl akademische als auch handelnde Institutionen und in jüngster Zeit von Physikern (unter Verwendung einiger ihrer statistischen Werkzeuge, die für komplexe Zeitreihen entwickelt wurden) analysiert, um vergangene Daten zu analysieren, um Informationen über die Zukunft zu erhalten. Die Elliott-Wellentechnik ist wohl die bekannteste in diesem Bereich. Wir spekulieren, dass die Elliott-Wellen, so stark in der Finanzanalysten-Folklore verwurzelt, könnte eine Signatur einer zugrunde liegenden kritischen Struktur der Börse. quot Paul Tudor Jones. Der Milliardär-Rohstoffhändler, nennt Prechter und Frosts Standardtext auf dem Elliott-Kontingent-Klassiker, eine der vier Bibeln des Businessquot - McGee und Edwards Technical Analysis of Stock Trends und The Elliott Wave Theorist geben beide sehr spezifische und systematische Wege Ansatz, der große Lohn - / Risiko-Verhältnisse entwickelt, um einen Geschäftsvertrag mit dem Markt zu schließen, was jeder Handel sein sollte, wenn er richtig und sorgfältig ausgeführt wird. Die Märkte bewegen sich nur mit einem scheinbaren Kampf gegen den Trend eines höheren Grades. Widerstand von der größeren Tendenz scheint eine Korrektur von der Entwicklung einer vollen impulsiven Struktur zu verhindern. Der Kampf zwischen den beiden entgegengesetzten Graden macht in der Regel Korrekturwellen weniger deutlich als impulsive Wellen, die immer mit vergleichbarer Leichtigkeit in Richtung des einen größeren Trends fließen. Als weiteres Ergebnis des Konflikts zwischen den Trends sind die Korrekturwellen ziemlich viel abwechslungsreicher als impulsive Wellen. Korrektive Muster fallen in vier Hauptkategorien: Zickzack (5-3-5 enthält drei Variationen: Einzel-, Doppel-, Dreifach-) Flats (3-3-5 enthält drei Variationen: regelmäßig, erweitert, läuft) Triangles (3-3-3- 3-3 vier Arten: aufsteigend, absteigend, kontrahierend, expandierend) Doppelte und dreifache Dreier (kombinierte Strukturen). ZIGZAGS (5-3-5) Ein einzelner Zickzack in einem Bullenmarkt ist ein einfaches, dreifach abfallendes Muster mit der Bezeichnung A-B-C und unterteilt 5-3-5. Die Spitze der Welle B ist merklich niedriger als der Beginn der Welle A, wie in den Fig. 11 und 12 dargestellt. Gelegentlich treten Zickzacke zweimal oder höchstens dreimal hintereinander auf, insbesondere wenn der erste Zickzack ein normales Ziel unterschreitet. In diesen Fällen wird jeder Zickzack durch einen dazwischenliegenden Zehnerknoten (markiert X) getrennt, was einen Doppelzickzack (siehe Abbildung 13) oder einen Dreifachzickzack erzeugt. Die Zickzacke sind W und Y (und Z, wenn ein Tripel). FLATS (3-3-5) Eine flache Korrektur unterscheidet sich von einem Zickzack dadurch, daß die Teilwellensequenz 3-3-5 ist, wie in den Fig. 14 und 15 gezeigt. Da die erste Aktionswelle Welle A keine ausreichende Abwärtskraft zum Entfalten aufweist Wie es bei einem Zickzack der Fall ist, scheint die B-Wellenreaktion diesen Mangel an Gegentratendruck zu erben und verläuft nicht unerwarteterweise nahe dem Beginn der Welle A. Die Welle C endet im Gegenzug in der Regel nur geringfügig über das Ende hinaus Der Welle A als deutlich größer als in Zickzack. Flache Korrekturen reflektieren gewöhnlich weniger von vorhergehenden Impulswellen als Zickzacke. Sie beteiligen sich an Perioden mit einem starken Trend und damit fast immer vor oder folgen Erweiterungen. Je stärker die zugrunde liegende Tendenz, desto kürzer die Wohnung ist. Innerhalb von Impulsen, vierte Wellen häufig Sportflächen, während zweite Wellen selten zu tun. Drei Arten von 3-3-5 Korrekturen wurden durch Unterschiede in ihrer Gesamtform identifiziert. Bei einer regelmäßigen flachen Korrektur endet die Welle B etwa auf dem Niveau des Beginns der Welle A, und die Welle C endet ein geringfügiges Bit am Ende der Welle A, wie wir in den Fig. 14 und 15 gezeigt haben. Ist die Sorte genannt eine erweiterte Wohnung. Die einen Preis weit über dem der vorhergehenden Impulswelle enthält. In ausgedehnten Ebenen endet die Welle B des 3-3-5-Musters über dem Startpegel der Welle A und die Welle C endet wesentlich stärker über den Endpegel der Welle A hinaus, wie in den 16 und 17 gezeigt Das 3-3-5-Muster, das wir eine laufende Ebene nennen, beendet sich die Welle B weit über den Anfang der Welle A hinaus, wie bei einer flachen Expansion, aber die Welle C verlässt ihre volle Strecke nicht und unterschreitet den Pegel, bei dem die Welle A liegt Beendet. Es gibt kaum Beispiele für diese Art von Korrektur im Preisrekord. HORIZONTALE TRÄGEL (Dreiecke) Dreiecke überlappen fünf Wellenbereiche, die 3-3-3-3-3 unterteilen. Sie scheinen ein Gleichgewicht der Kräfte zu reflektieren, was zu einer seitlichen Bewegung führt, die gewöhnlich mit abnehmender Lautstärke und Volatilität verbunden ist. Dreiecke fallen in vier Hauptkategorien, wie in 18 dargestellt. Diese Darstellungen zeigen die ersten drei Typen, wie sie im Bereich der vorangegangenen Preisaktion stattfinden, in sogenannten regelmäßigen Dreiecken. Es ist jedoch sehr häufig, insbesondere bei kontrahierenden Dreiecken, daß die Welle b den Anfang der Welle a überschreitet, wie es in Fig. 19 als ein laufendes Dreieck bezeichnet wird. Obwohl bei extrem seltenen Fällen eine zweite Welle in einem Impuls erscheint Nehmen die Dreiecke fast immer in Positionen vor der letzten Wirkwelle im Muster eines größeren Grades, dh als Welle vier in einem Impuls, Welle B in einem ABC, oder die abschließende Welle X in einem Doppel - oder Dreieck auf Dreifach-Zickzack oder Kombination (siehe nächster Abschnitt). COMBINATIONS (DOUBLE AND TRIPLE THREES) Elliott nannte sideways Kombinationen von Korrekturmustern doppelten threesquot und dreifach threes. quot Während ein einzelner drei irgendein Zickzack oder flach ist, ist ein Dreieck ein zulässiges Endbestandteil solcher Kombinationen und in diesem Zusammenhang wird ein quotthree genannt. Eine doppelte oder dreifache drei, dann ist eine Kombination von einfacheren Arten von Korrekturen, einschließlich der verschiedenen Arten von Zickzack, Ebenen und Dreiecke. Ihr Auftreten scheint die flachen Korrekturen zu sein, die sich seitwärts bewegen. Wie bei doppelten und dreifachen Zickzacken wird jedes einfache Korrekturmuster mit W, Y und Z bezeichnet. Die mit X bezeichneten reaktionären Wellen können die Form eines beliebigen Korrekturmusters annehmen, sind aber am häufigsten Zickzacke. Die Fig. 20 und 21 zeigen zwei Beispiele von Doppel-Dreien. Größtenteils sind Doppel-Dreier und Dreifach-Dreien horizontalen Charakter. One reason for this trait is that there is never more than one zigzag in a combination. Neither is there more than one triangle. Recall that triangles occurring alone precede the final movement of a larger trend. Combinations appear to recognize this character and sport triangles only as the final wave in a double or triple three. All the patterns illustrated here take the same form whether within a larger rising or falling trend. In a falling trend, they are simply inverted. The Broad Concept The basic rhythm of the market unfolds as five waves up and three waves down. Each advancing wave (1, 3 and 5) is called an impulse wave and each down wave (2 and 4) is referred to as a corrective wave. The diagram below shows how the basic cycle can be further dissected to reveal each impulse wave containing five waves, including the bear market impulse moves. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage028.gif/IMG The basic tenets of the theory are: 1. Action is followed by reaction 2. Movements in the direction of the trend divide into five waves and moves against the trend corrective waves divide into three waves 3. The cycle keeps repeating it self in ever expanding magnitude from Sub-minuette through nine categories to the Grand Super-cycle 4. The wave pattern exists irrespective of time. Waves may be stretched or compressed, but the underlying pattern is constant. As a result of Prechters work in the 1970s two more tenets emerged. They are: Wave three is never the shortest. Wave four never overlaps wave 1. If wave four does overlap wave 1 then the move is probably not an impulse move, but an ABC correction. Impulse Waves - Variations Extensions Any of the five impulse waves in one cycle, three up and two down, can result in an extension. Extensions are more common in wave five and three than wave one, but nevertheless can exists in any impulse wave. Not only primary impulse waves can extend but extensions within extensions can also exist, in the case of a telescopic type market. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage029.gif/IMG The above diagram illustrates a fifth wave extension of a fifth wave extension, in descending magnitude. By the same token the same sort of extension within extension can exist in wave three, which is the next most common wave to make an extension. Diagonal Triangles These occur in the fifth wave generally as a result of the third wave going too far too fast. They borrow from the classic charting patterns of old and are essentially wedges, with each sub wave dividing into three. A rising wedge is bearish and often results in a sharp decline at least back to the inception point of the triangle began. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage030.gif/IMG Failures Elliott used the word failure to describe the failure of the market to make a new high (or low) as a result of the fifth wave. The fifth wave falling short of the third wave is a failure, which, is an exceptionally bearish signal. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage031.gif/IMG Elliott Wave Principle - Variations There are more variations in corrective waves than there are in impulse waves. Their complexity is often a function of the degree. That is as they become more varied they become more complex. Corrections Elliott suggested that market corrections generally fall into four categories: Zigzag Correction A zigzag A-B-C type correction has 5-3-5 sub waves. This includes the double zigzag variation. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage032.gif/IMG The zigzag correction is the most basic a-b-c type correction. The above example is obviously a bull market correction, with the impulse moves being against the trend in the next degree. The waves have been broken into their subcomponents. Flat Correction A flat correction is also an A-B-C type correction and has 3-3-5 sub waves. It also has variations called irregular and running corrections. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage033.gif/IMG The flat correction above is also a correction to a bull market. Wave C in any flat correction generally terminates very close to wave A rather than significantly lower as in a zigzag. the tell tale characteristic is of course the wave count, which is 3-3-5. This then gives rise to several variations while still adhering to the basic wave count. Irregular Variations There are two variations to the flat correction and both are referred to as irregular corrections. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage034.gif/IMG The B wave could actually make a new high, in the case of a bull market and the C wave could extend beyond wave A as in the above diagram. Flat corrections are seen as mild corrections as they do less damage to the underlying trend than their zigzag cousins. If wave C drops below wave A (as in diagram 51) it can be interpreted as having a negative, dampening effect on the next impulse move, in this case higher. If on the other hand, the C wave were to fall short of a wave A, (as in Diagram 52) it could be interpreted as strengthening, supportive for the underlying trend and the next impulse wave. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage035.gif/IMG Triangle Correction A triangle correction is made up of five waves labelled as an a-b-c-d-e type correction having 3-3-3-3-3 sub waves. There are four variations: ascending, descending, contracting and expanding. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage036.gif/IMG The above diagram is an example of an ascending horizontal triangle. It has a flat top and a rising bottom. The other variations are descending, contracting and expanding, which should be self explanatory. The important thing to look for is the 3 wave sub-count within each wave of the triangle. Double threes and triple threes A single three is any zigzag or flat consisting of three basic waves A-B-C. Double threes are as the name implies a combination of two threes, two zigzags or two flats. Triple threes are yet another three tacked on. The intermediate wave is referred to as an X wave. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage037.gif/IMG The example above is a double three made up of two flat corrections. Complex Combinations It is important to keep in mind which particular degree of complexity the structure is in. A correction may start off as a simple flat correction as in the example below (see diagram 55) the first a-b-c is merely the A part of the correction. Its not until the C leg breaks below wave a of B, that we can actually say that the centre three wave, B wave, is complete and we can then look for a C wave decline. The C leg can be a 3 or 5 wave decline, as it is against the underlying trend of the market. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage038.gif/IMG During any correction the waves in the same direction as the underlying trend are always in 3s while the counter move waves, against the trend can be either 3 wave or 5 wave legs. Associated Rules and Guidelines The Rule of Alternation This rule should be kept in mind at all times when analysing wave formations and projecting targets. Alternating patterns should be expected in all wave formations. The most basic aspect of this rule can be applied to corrections and it virtually states that no two sequential corrections of the same magnitude will ever be the same type. For example if wave two is a simple correction, then wave four will most likely be complex. Simple corrections are usually zigzags or basic flat corrections, while complex corrections are more likely to be triangles, double threes, intricate flats or any other complex pattern. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage039.gif/IMG IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage040.gif/IMG The same rule can be applied to large magnitude complex corrections. A complex three for example will probably alternate between patterns. For example a large correction might start with a flat ABC type correction and then be followed with a zigzag. Alternatively if the correction started with a zigzag we could expect a flat to follow. Strength of Trends Corrective patterns provide a lot of information about the strength of an underlying or subsequent trend. The slope of the impulse waves can also be revealing with respect to the underlying trend. In general zigzags indicate ordinary or normal conditions and therefore normal trend strength. Complex corrections on the other hand denote a strong trend and often occur prior to or immediately after an extension. Extensions are also a sign of strength. Zigzag or double zigzags indicate ordinary strength Flat and Irregular corrections indicate a strong trend Running corrections reveal an unusually strong trend Double and triple threes indicate a strong trend Triangles indicate thrust, swift but short Depth of corrective Waves How low can you go A vital piece of information is provided by Elliott wave theory when it comes to market corrections. The rule is that corrections, especially wave four corrections, tend to terminate within the range of the previous wave four by lessor degree. The most likely level of retracement is the bottom of wave for by lessor degree. Wave equality It is one of the tenets of Elliott wave theory that two impulse waves will always tend toward the same size in both time and price. It generally holds true for two non-extended waves and especially true if wave 3 is an extension. If two of the waves are not perfectly equal then the relationship is probably a multiple of 0.618. Correct counting - Overlaps and Wave Length Wave four should never overlap wave one Wave three is often the longest and never the shortest IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage041.gif/IMG Alternative Counting There are often alternative counts, which is a tool that can be employed to isolate the correct count. Hindsight will always provide the ultimate resolution. But it is very handy to have a quotif this happens, then it cant be this count and therefore it must be the alternative onequot type of tool in our arsenal. Therefore there is an alternative to the above count. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage042.gif/IMG Time Frames The most common time frame for an intra day chart is an hourly chart, however when analysing large samples of time an hourly chart can become unworkable. Percentage charts or semi-log charts are best when analysing large market moves that may span decades. Daily charts are of course most apt and a standard when applying Elliott wave principles. The foremost aim of wave classification under the Elliott wave theory system is to determine where we are in the cycle. This is easy to do in a clear wave count, which is often the case for fast moving or extending markets. As long as you can see fives waves youve got a start. Its not so easy in tired and choppy markets. Complexity and lethargy are often frustrating for any analyst, but especially for the Elliott wave analyst. It has often been suggested that when the market rests the trader should also take a rest. Channels Parallel trend channels can be employed to assist in determining a potential target and/or market developments. One can start drawing the channel at the termination of wave 2. The bottom trend line is an extension of the trend line drawn from the inception point of the new wave formation and the bottom of wave two. A parallel trend line can then be extrapolated from the top of wave one and provides a logical target fro the termination of wave three. Quite often wave three will extend beyond the high side of the original channel, in which case the channel should be revised. A new top line should be drawn through the top of wave one and wave three, a parallel line is then drawn through the bottom of wave two and a new channel should be the result. IMGfile:///C:/Users/Luke/AppData/Local/Temp/msohtml1/01/clipimage043.gif/IMG i have tried today my first experiment to build a simple quotMoving Average cross EAquot. it is a simple EA only to try the builder. the EA should buy when the 5 EMA cross the 20 SMA cross up - gt long cross down - gt short close positions with TP or SL and with the cross in the other direction. so far so good it opens the positions as it should be. but what i dont understand why doesnt it close the position when there is a cross in the other direction it only closes the position with the TP or SL. what did i wrong with the close logics Just for fun I did the simulation of 5 minutes EURUSD of your strategy and this is what I got - u can use the same settings for your EA - it is profitable in the last 5 months - 23 profit. Simplicity is the ultimate sophistication (Leonardo da Vinci)Butterfly effect Figure 1: Evolution (in steps of 5 years) of the one-day forecast error in meters (dashed line) and doubling time of the initial error in days (full line) of the 500(hPa) Northern Hemisphere winter geopotential height - a representative measure of the state of the atmosphere - as obtained from the ECMWF operational weather forecasting model. The Butterfly Effect is a concept invented by the the American meteorologist Edward N. Lorenz (1917-2008) to highlight the possibility that small causes may have momentous effects. Initially enunciated in connection with the problematics of weather prediction it became eventually a metaphor used in very diverse contexts, many of them outside the strict realm of science. A brief history On December 29, 1972 Lorenz presented a talk in the 139th meeting of the American Association for the Advancement of Science held in Washington, D. C. entitled Predictability: Does the Flap of a Butterflys Wings in Brazil Set a Tornado in Texas The principal message of the talk was that the behavior of the atmosphere is unstable with respect to perturbations of small amplitude. In its frailty the butterfly - actually introduced in the title by the convener of the session, unable to reach Lorenz at the time of the release of the program - seemed to provide the ideal illustration of smallness, as opposed to the overwhelming character of phenomena like tornados encountered in our natural environment and interfering decisively with our everyday experience. From the very start Lorenz was fully aware of the danger of confusions that might potentially arise by such a title, in view of the disproportionateness between the butterfly and the tornado. He stressed that in its literal form the question formulated in the title was by no means claimed to have an affirmative answer. The issue was, rather, whether two particular weather situations differing by as little as the influence of the flap of the wings of a single butterfly will experience in the long run two different sequences of occurrences of events of a certain type (such as tornados): what matters, in the end, is thus the instability of the atmosphere. Lorenz also warned against the confusion of identifying the butterfly effect and the butterfly looking strange attractor he had discovered in 1963, when studying the three-mode truncation of the Boussinesq equations describing the Rayleigh-B233nard flow beyond the thermal convection threshold. As it often happens in the history of ideas the possibility that small causes may have large effects in general and in the context of weather in particular was anticipated by a number of researchers before Lorenz, from Henri Poincar233 to Norbert Wiener. The merit of Lorenzs work has been to put the concept of instability of the atmosphere on a solid, quantitative basis and to link it to the properties of large classes of systems undergoing nonlinear dynamics and deterministic chaos. Error growth and the prediction of complex systems A physical system such as the atmosphere is inevitably subjected to small uncertainties in the initial conditions, that need to be specified when running a model providing information on its future evolution. Such uncertainties are inherent in the process of experimental measurement, which even in its most sophisticated form is limited by a finite precision. An observation under given ambient conditions entails thus that instead of a single system represented by an isolated point in the state space (the phase space, in the terminology of dynamical systems theory) one deals in reality with an ensemble of systems contained within an uncertainty ball occupying a finite volume in this space. The system of interest lies somewhere inside this ball but we are unable to specify its exact position, since for the observer all of its points represent one and the same state. In short, physical systems are subjected to a universal source of perturbations related to the presence of initial errors . The question is, then, whether they will respond by keeping errors under control (in which case they will be deemed to be stable) or, on the contrary, they will amplify them in the course of their evolution (in which case they will be deemed to be unstable). For the atmosphere, this question can be answered by resorting to realistic models describing the evolution of the relevant atmospheric fields in time. One of the best known and most widely used operational models of this kind is the model developed at the European Centre for Medium Range Weather Forecasts (ECMWF), designed to produce weather forecasts in the range extending from a few days to a few weeks. This involves daily preparation of a (N)-day forecast of the global atmospheric state (typically, (N10) days), using the present days state as the initial condition. Since the equations are solved by stepwise integration, intermediate range (1, 2, (cdots)) day forecasts are routinely achieved as well. Capitalizing on the fact that the model produces rather good 1-day forecasts, one expects that the state predicted for a given day, 1 day in advance, may be regarded as equal to the state subsequently observed on that day, plus a relatively small error. By comparing the 1- and 2- day forecasts for the following day, the 2- and 3- day forecasts for the day after, and so on, one can then determine upon averaging over several consecutive days how the mean error evolves in time. The result for the period 1982-2002 (in steps of 5 years), summarized in Figure 1. establishes the presence of error growth as a result of sensitive dependence on the initial conditions in the atmosphere. What is more this dependence leads here to the strongest possible form of growth, namely, exponential growth. The full line in the figure depicts the doubling time of the initial error. In 1982 this value was about 2 days, as determined by Lorenz himself in a seminal work in which the ECMWF data were first analyzed in this perspective. As can be seen, this value has dropped to about 1.2 days in 2002. This decrease appears at first sight as paradoxical, since during this 20-year period one has witnessed significant technological advances such as an increase of spatial resolution, a substantial improvement of parameterization schemes and an almost three-fold decrease of initial (1-day forecast) error (dashed line in Figure 1 ). It reflects the fact that although the accuracy of forecasts for a few days ahead is considerably increasing detailed forecasting of weather states at sufficiently long range with models of increasing sophistication may prove impracticable, owing to the complexity inherent in the dynamics of the atmosphere. Part of this complexity is due to the coexistence of processes unfolding on a wide range of space and time scales: errors in the coarser structure of a weather pattern would tend to double much more slowly than errors in the finer structure (e. g. the positions of individual clouds) if it were not for the fact that the latter, having attained an appreciable size, tend to induce errors in the coarser structure as well. Figure 2: Illustration of the phenomenon of sensitivity to the initial conditions in a model system giving rise to deterministic chaos. Black line denotes the trajectory of the reference system. Red and blue lines denote the trajectories emanating from two initial conditions differing from the reference trajectory by (epsilon110 ) (red curve) and (epsilon210 ) (blue curve). Clearly, as soon as the distance between two instantaneous states separated initially by a very small error will exceed the experimental resolution the states will cease to be indistinguishable for the observer. As a result, it will be impossible to predict the future evolution of the system at hand beyond this temporal horizon. This raises the fundamental question of predictability of the phenomena underlying the behavior of the atmosphere. Now, the exponential sensitivity to the initial conditions turns out to be the principal signature of deterministic chaos . a well-known behavior underlying large classes of deterministic dynamical systems governed by nonlinear evolution laws. This opens the way to an analysis of error growth and the understanding of the butterfly effect in the atmosphere using tools from chaos theory. Furthermore, since deterministic chaos is known to occur not only in systems involving large numbers of intricately coupled variables but also in ordinary looking systems amenable to few variables, error growth and butterfly type effects would in fact appear to be concepts of universal validity. This rules out once for all the idea that the butterfly effect could reflect incomplete knowledge of the atmosphere in connection with the presence of huge numbers of variables (up to (107) or so for the ECMWF forecasting model) and parameters masking some underlying regularities: systems obeying to evolution laws known to their least detail, subjected to perfectly well controlled parameters, could still turn out to be unpredictable beyond a certain temporal horizon. The black line of Figure 2 depicts a time series generated by a prototypical 1-variable discrete time dynamical system giving rise to chaotic behavior. The red and blue colored lines of this figure correspond to the succession in time of variable (x) emanating from two initial conditions differing from the reference trajectory (black line) by errors of amplitudes (epsilon110 ) and (epsilon210 ,) respectively. As can be seen, the reference and perturbed trajectories practically follow each other up to a certain time (ti) which decreases with (epsiloni (i1,2)) and are subsequently diverging by amounts comparable to the entire range of variation of (x .) Using the data of the figure one may deduce the instantaneous value of the error ut(epsilon, x0)x(t, x0epsilon)-x(t, x0) For a given (x0 ,) this quantity displays a very pronounced variability. Averaging over all possible initial states (x0) compatible with the dynamics (keeping the values of (epsiloni) as above) leads to the logistic-like mean quadratic error growth curve of Figure 1. Three different stages may be distinguished: an initial (short time) induction stage during which errors increase exponentially but remain (for (epsilon) small enough) small an intermediate explosive stage displaying an inflexion point situated at a value (t) of (t) depending logarithmically on (epsilon ,) (tapprox ln1/epsilon) where errors suddenly attain appreciable values and a final stage, where the mean error reaches a saturation level of the order of the size of the attractor and remains constant thereafter. The mechanism ensuring this saturation is the reinjection of the trajectories that would first tend to escape owing to the instability of motion, back to a subset of phase space that is part of the attractor. Figure 3: Time dependence of the mean quadratic error of the system of Fig. 2 starting from 100,00 initial conditions scattered on the attractor and two different initial quadratic errors, (epsilon110 ) and (epsilon210 .) (t1) and (t2) stand for the location of the inflexion points characterizing the explosive stage of the growth of errors. Notice that the initial states (reference as well as perturbed) considered in Figure 2 and Figure 1 lie on a single and same attractor, uniquely defined once the evolution law and the parameter values are specified. The trajectories depicted in Figure 2 describe thus the same type of behavior, differing only in the way events succeed in time. This substantiates Lorenzs warning (cf. preceding section) on how the butterfly effect is to be understood. An additional important feature pertains to the weak (logarithmic) dependence of the error explosion time (t) on the initial value (epsilon :) decreasing (epsilon) from, say, (10 ) to a value like (10 ) close to a typical thermal fluctuation relative to the mean, would only increase (t) from about 5 to about 30. Naturally, once on scales as small as those for which thermal noise begins to be manifested other effects are likely to take over and mask the butterfly effect. The dynamical systems connection outlined above also allows one to identify a number of intrinsic quantities, determined entirely by the evolution law and the parameter values, providing quantitative measures of the butterfly effect. Most prominent among them is the (maximum) Lyapunov exponent. defined as in the double limit of (in the indicated order) infinitely small initial errors (epsilon) and infinitely long times (t .) In this setting error growth and butterfly effect are consequences of the positivity of (sigma ,) and (sigma ) (along with (t) above) defines the time horizon beyond which predictions become essentially random. Typical dynamical systems live in a multi-dimensional phase space and possess thus several Lyapunov exponents. some of which are negative. For short times all these exponents are expected to take part in the error dynamics. Since a typical attractor associated to a chaotic system is fractal. a small error displacing the system from an initial state on the attractor may well project it outside the attractor. Error dynamics may then involve a transient stage prior to the re-establishment of the attractor, during which errors would decay in time. An important class of multivariate systems are spatially extended systems, Here it is often convenient to expand the quantities of interest in terms of the different spatial scales along which the phenomenon of interest can develop and, in particular, the different scales along which an initial error can occur. The ideas outlined above imply, then, that the predictability properties of a phenomenon depend on its spatial scale. In summary, error growth dynamics is subjected to strong variability since not all initial errors grow at the same rate. As a result, the different predictability indexes such as (sigma ,) the saturation level and the time (t) to reach the inflexion point provide only a partial picture, as in reality the detailed evolution depends upon the way the different possible error locations and directions are weighted. A situation worth mentioning is that of errors increasing in a subexponential fashion, e. g. as powers of (t .) In a chaotic dynamical system this happens transiently along the directions associated to its vanishing Lyapunov exponents, prior to the stage where the directions associated to the positive exponents are taking over. In non-chaotic systems such as systems undergoing non-uniform periodic or quasi-periodic motion power law transient growth is the rule, and is associated to increasingly large phase shifts between the reference and the perturbed system. Such errors do not count when dealing with the butterfly effect: on the average their magnitude will remain close to the initial value (epsilon ,) although in some particular realizations one may temporarily witness an abrupt growth stage. Initial errors, model errors and environmental variability Much like experiment, the modeling of a physical phenomenon has also its limitations. First, once a certain level of description is chosen small scale processes (like e. g. local turbulence in the context of atmospheric dynamics) are automatically overlooked, since they exceed the adopted (finite) resolution. Furthermore, many of the parameters built in the model may not be known to a great precision. In addition to initial errors prediction must thus cope with model errors . reflecting the fact that a model is only an approximate representation of reality. The key question is, then, to what extent model errors will be amplified in time to a point compromising the quality of the prediction. This raises the problem of a parametric butterfly effect associated with the sensitivity of a system with respect to changes of the underlying evolution laws, referred to in nonlinear dynamics as structural stability . We emphasize that if the dynamics were simple, initial or model errors would not matter. But this is not manifestly the case in large classes of systems. Initial and model errors can thus be regarded as probes revealing the underlying instability and complexity of the system at hand. Natural complex systems like the atmosphere can reasonably be expected to be structurally stable, as they are the result of an evolution during which they have adapted to the ambient conditions. From the standpoint of predictability this means that the attractors of the reference, real system and of the approximate model will have similar structures and lie relatively close to each other in phase space, differing only in their quantitative properties. The relevant question here is, then, how the perturbed system (the model) will deviate as time proceeds from its initial state on the unperturbed attractor (the real system) prior to reaching the final attractor. Theoretical developments along with simulations on model systems lead to the following conclusions: In the short time regime, mean quadratic model errors start at zero level and increase as (t2 ,) with a proportionality coefficient depending on the magnitude of the error in the parameters or of the terms omitted given the adopted resolution. Contrary to initial errors (which start with a nonzero value at (t0)), instability of motion and the largest Lyapunov exponent in particular do not play here a crucial role. For longer times model errors grow much like in the curve of Figure 1. Interestingly, the saturation level attained is again finite, practically independent of the quality of the model, as it reflects to first approximation the average of typical quadratic distances between any two points of the reference attractor which tend to be increasingly phase shifted time going on. In summary, the initial stage of the dynamics of global (initial plus model) errors is bound to be dominated by the growth of initial errors, since model errors are initially zero. For long times both initial and model errors attain a finite level, depending on the nature of the attractor of the reference system. As a rule between these two extremes one witnesses a crossover between the growth of the two types of error, as illustrated in Figure 3. Beyond the crossover time (overline ,) then, the classical butterfly effect is superseded by an effect reflecting the sensitivity of the evolution laws themselves towards small errors. As models play an essential role in most forecasting schemes, this constitutes an additional irreducible limitation in the prediction of complex systems. Figure 4: Typical time dependence of the mean quadratic error of a model dynamical system giving rise to deterministic chaos starting from 100,00 initial conditions scattered on the attractor in the presence of initial condition errors (red line), model errors (blue line) and both initial condition and model errors (green line). (overline ) denotes the crossover time whereby both sources of errors attain a comparable magnitude. In the preceding discussion it was understood that the values of characteristic parameters (real or model ones) remained fixed. There is growing interest in the response of a complex system in general and of the atmosphere in particular to external forcings and varying parameters - for instance, as a result of anthropogenic effects. The main results on the status of the butterfly effect under these conditions can be summarized as follows: External forcings of even weak amplitude may induce qualitatively new effects in the form of enhanced sensitivity (stochastic resonance. etc.) or of transitions between states that would otherwise remain separated. This complicates further the task of prediction. A systematic slow variation of a parameter in time can enhance the stability of a state that would otherwise tend to undergo an instability and can even lead to situations where the system becomes for all practical purposes frozen in a state that could otherwise not be sustained. At the same time, however, the fluctuations around the mean tend to increase and as a result the occurrence of extreme events is enhanced. For very short times the effect of stochastic forcings (however small, including thermal noise) dominates over that of the (deterministic) evolution laws. As a result the stage of exponential growth characteristic of deterministic chaos occurs only beyond some characteristic time depending on the noise strength, as initially quadratic errors grow only linearly in time. During this time regime the butterfly effect is, then, attenuated. Taming the butterfly: the probabilistic approach to prediction The sensitivity and intrinsic randomness of complex systems symbolized by the butterfly effect signals the limitations of the traditional deterministic description, in which one focusses on a detailed, pointwise evolution of individual trajectories. Now as seen earlier, owing to the finite precision of the process of measurement in nature an instantaneous state is in reality to be understood as a small region in phase space. In the presence of the butterfly effect this region will subsequently be deformed and the individual points within it will be increasingly delocalized. To the observer this will signal the inability to predict the future beyond a certain transient period on the basis of the knowledge of the present conditions. These elements constitute a compelling motivation for searching for an alternative description capable of coping in a natural fashion with irregular successions of events, delocalization in state space and build-in uncertainties. The probabilistic approach offers this natural alternative. A fundamental point is that the evolution of systems composed of several subunits and undergoing complex dynamics can be mapped into a probabilistic description in a self-consistent manner, free of heuristic approximations. The probabilistic and deterministic views become thus two facets of the same reality, and this allows one to sort out regularities of a new kind. One of the novelties brought by the probabilistic description is that the evolution of the underlying probability distributions (described by Liouville, master or Fokker-Planck equations) - which now become the principal quantities of interest - is linear and displays strong stability and uniqueness properties. This is in sharp contrast with the deterministic description in which nonlinearity and instability are prominent. As we see presently, this provides the basis of a new approach to prediction. When implemented on a mathematical model representing a concrete system like e. g. the atmospheric circulation the probabilistic approach amounts to choosing a set of initial conditions compatible with the available data to integrate the model equations for each of these initial conditions and to evaluate the averages (or higher moments) of the quantities of interest over these individual realizations . In the context of atmospheric dynamics this procedure is known as ensemble forecasts . Its principal merit is to temper the strong fluctuations associated with a single realization and to sort out quantitative trends in relation with the indicators of the intrinsic dynamics of the system at hand. Figure 1 illustrates schematically the nature of ensemble forecasts. The full circle in the initial phase space region (delta Gamma0) stands for the best initial value available. Its evolution in phase space, first after a short lapse of time (region (delta Gamma1)) and next at the time of the final forecast projection (region (delta Gamma2)) is represented by the red line. Now, the initial position is only one of several plausible initial states of the atmosphere, in view of the errors inherent in the analysis. There exist other plausible states clustered around it, represented in the figure by open circles. As can be seen, the trajectories emanating from these ensemble members (blue lines) differ only slightly at first. But between the intermediate and the final time they diverge markedly, presumably because the predictability time has been exceeded: there is a subset of the initial ensemble including the best guess that produces similar forecasts, but the remaining ones predict a rather different atmospheric state. This dispersion is indicative of the uncertainty of the forecast. It constitutes an important source of information that would not be available if only the best initial condition had been integrated, especially when extreme situations are suspected to take place in a very near future. It should be pointed out that such uncertainties frequently reflect local properties of the dynamics such as local expansion rates and the orientation of the associated phase space directions. Figure 5: Illustrating the nature of ensemble forecasts. The phase space regions (delta Gamma0 ,) (delta Gamma1) and (delta Gamma2) represent three successive snapshots of an ensemble of nearby initial conditions (left) as the forecasting time increases. The red line represents the traditional deterministic single trajectory forecast, using the best initial state as obtained by advanced statistical and data analysis techniques. The blue lines represent the trajectories of other ensemble members, which remain close to each other for intermediate times (middle) but subsequently split into two subensembles (right), suggesting that the deterministic forecast becomes unrepresentative. Notice the deformation of the phase space volumes accompanying the underlying instability. The probabilistic approach can also be applied for developing predictive models on the sole basis of data. An example of how this is achieved pertains to the transition between atmospheric regimes such as the onset of drought. The main idea is that to be compatible with such data, the underlying system should possess (as far as its hydrological properties are concerned) two coexisting attractors corresponding, respectively, to a regime of quasi-normal precipitation and a regime of drought. In a deterministic setting the system would choose one or the other of these attractors depending on the initial conditions and would subsequently remain trapped therein. In reality under the influence of the fluctuations generated spontaneously by the local transport and radiative mechanisms, or of the perturbations of external origin such as, for instance, surface temperature anomalies, the system can switch between attractors and change its climatic regime. This is at the origin of an intermittent evolution in the form of a small scale variability around a well-defined state followed by a jump toward a new state, which reproduces the essential features of the record. The idea can of course apply to a host of other problems, including the transition between the well-known zonal and blocked atmospheric flows. In their quantitative form the models belonging to this family appear in the form of evolution equations for the underlying probability distributions, from which a number of relevant quantities such as the lifetime of a given atmospheric regime can be evaluated. Butterfly effect, causality and chance The ubiquity of the butterfly effect in large classes of complex systems prompts one to reflect on the connection between two concepts that have been regarded as quite distinct throughout the history of science and of ideas in general, namely, causality and chance. Classical causality relates two qualitatively different kinds of events, the causes and the effects, on which it imposes a universal ordering in time. From the early Greek philosophers to the founders of modern science causality has been regarded as a cornerstone, guaranteeing that nature is governed by objective laws and imposing severe constraints on the formulation of theories aiming to explain natural phenomena. Technically, causes may be associated to the initial conditions on the variables describing the system, or to the constraints (more generally, the parameters) imposed on it. In a deterministic setting this fixes a particular trajectory (more generally, a particular behavior) and it is this unique cause to effect relationship that constitutes the expression of causality and is ordinarily interpreted as a dynamical law. But suppose that one is dealing with a complex system displaying sensitivity to the initial conditions as it occurs in deterministic chaos or sensitivity to the parameters and to modeling errors in general. Minute changes in the causes produce now effects that look completely different from a deterministic standpoint, thereby raising the question of predictability of the system at hand. Clearly, under these circumstances the causes acquire a new status. Without putting causality in question, one is lead to recognize that its usefulness in view of making predictions needs to be reconsidered. It is here that statistical laws offer a natural alternative. While being formally related to the concept of chance, the point stressed in the previous section is that they need not require extra statistical hypotheses: when appropriate conditions on the dynamics are fulfilled statistical laws are emergent properties, that not only constitute an exact mapping of the underlying (deterministic) dynamics but also reveal key features of it that would be blurred in a traditional description in terms of trajectories. In a sense one is dealing here with a deterministic randomness of some sort. In fact, the equations governing the probability distributions associated to a complex system, are deterministic and causal as far as their mathematical structure is concerned: they connect an initial probability (the cause ) to a time-dependent or an asymptotic one (the effect ) in a unique manner. By its inherent linearity and stability properties the probabilistic description re-establishes causality and allows one to still make predictions, albeit in a perspective that is radically different from the traditional one. From facts to fiction The concept of the butterfly effect refers to a real world phenomenon of universal bearing, well beyond the framework of atmospheric physics in which it was initially proposed. It highlights the fact that science is not in the position to predict everything once sufficient information is gathered, owing to the existence of intrinsic limitations. In this respect, it has contributed to the advent of a new, post-Newtonian scientific paradigm nowadays referred to as the complexity paradigm . Little things in the past can also make big differences in everyday life when ideas and trends cross a threshold, tip and spread as discussed in an interesting book by Malcolm Gladwell. The author, correctly, makes no attempt whatsoever to relate this type of phenomenon to the butterfly effect in the sense of Lorenz. In a similar vein biological evolution and Darwinian adaptation can be viewed as the accumulation of small changes over a long period, which at some stage produce momentous effects that cannot be predicted in advance. Again, this is not to be confused with the butterfly effect. Rather, as pointed out by some authors we are here actually in the impossibility to know in advance the state space of the evolving biosphere. The sensitivity of large classes of systems and the concomitant difficulty to issue long term predictions is a well established fact in very diverse fields beyond the strict realm of physical science, such as sociology and finance. The butterfly effect constitutes here a powerful analogy that can be used fruitfully to raise questions and to transpose techniques that would otherwise be impossible to imagine. The picture begins, unfortunately, to be blurred from the moment one switches from facts to metaphors invoked in an uncontrollable way. This is what happened repeatedly in the last decades, when the butterfly effect was transposed in mass culture to explain that a chain of events of apparently no importance can change History and forge destinies. Most if not all of these transpositions are, simply, dubious science and what is more, are highly misleading for the public to which they are addressed. Indeed, the essence of the butterfly effect is that, on the contrary, following small changes in the past one would never be in a position to fully evaluate the consequences for the present in view of the highly complex and intricately correlated sequences of events separating the reference and the modified paths. Summing up Classical science has emphasized stability and permanence. Developments spanning the last decades show, on the contrary, that instability, sensitivity and unpredictability underlie large classes (if not most) of phenomena occurring on macroscopic time and space scales - the scales of our everyday experience. There is a need for the decision makers, for the public and even for a part of the scientific community to adapt to this state of affairs and to the modes of reasoning required by it. Many systems of concern, from the atmosphere to the stock market need to be observed, monitored, modeled and predicted in a way that does justice to their intrinsic complexity otherwise essential features are likely to be missed. The butterfly effect stands as a symbol of this new rationality. References History of the butterfly effect concept P. Duhem, La Th233orie physique: son objet, sa structure . Marcel Rivi233re, Paris (1906). J. Hadamard, Les surfaces 225 courbures oppos233es et leurs lignes g233od233siques, J. Math. Pures et Appl. 4, 27-73 (1898). R. Hilborn, Sea gulls, butterflies and grass shoppers: a brief history of the butterfly effect in nonlinear dynamics, Amer. J. Phys. 72, 425-427 (2004). E. N. Lorenz, Deterministic non-periodic flow, J. Atmos. Sci. 20, 130-141 (1963). E. N. Lorenz, The essence of chaos . University of Washington Press (1993). H. Poincar233, Science et m233thode . Flammarion, Paris (1908). N. Wiener, Nonlinear prediction and dynamics . in Proc. 3rd Berkeley Symp. on Math. Statistics and Probability, Vol. 3, University of Berkeley Press (1954). Error growth and predictability E. N. Lorenz, Atmospheric predictability as revealed by naturally occurring analogues, J. Atmos. Sci. 26, 636-646 (1969). E. N. Lorenz, Atmospheric predictability experiments with a large numerical model, Tellus, 34, 505-513 (1982). C. Nicolis, Probabilistic aspects of error growth in atmospheric dynamics, Q. J.R. Meteorol. Soc. 118, 553-568 (1992). G. Nicolis and C. Nicolis, Foundations of complex systems . World Scientific, Singapore (2007). Model error and time dependent forcings C. Nicolis, Transient climatic response to increasing (CO2) concentration: some dynamical scenarios, Tellus, 40A, 50-60 (1988). C. Nicolis, Dynamics of model errors: some generic features, J. Atmos. Sci. 60, 2208-2218 (2003). C. Nicolis, Dynamics of model errors: the role of unresolved scales, J. Atmos. Sci. 61, 1749-1753 (2004). C. Nicolis and G. Nicolis, Passage through a barrier with a slowly increasing control parameter, Phys. Rev. E 62, 197-203 (2000). G. Nicolis, Introduction to nonlinear science . Cambridge University Press, Cambridge (1995). G. Demar233e and C. Nicolis, Onset of Sahelian drought viewed as a fluctuation-induced transition, Q. J.R. Meteorol. Soc.116, 221-238 (1990). E. Kalnay, Atmospheric modeling, data assimilation and predictability . Cambridge University Press, Cambridge (2003). G. Nicolis and P. Gaspard, Towards a probabilistic approach to complex systems, Chaos, Solitons and Fractals, 4, 41-57 (1994). Ya Sinai, Introduction to ergodic theory . Princeton University Press, Princeton (1977). Beyond physical science The Boston Globe . taken up in Le Monde . Courrier International n0 936, October 9 to 15 (2008). M. Gladwell, The tippling point . Abacus, London (2001). S. Kauffman, Reinventing the sacred . Basic Books, New York (2008). John W. Milnor (2006) Attractor. Scholarpedia. 1(11):1815. Edward Ott (2008) Attractor dimensions. Scholarpedia, 3(3):2110. Jan A. Sanders (2006) Averaging. Scholarpedia, 1(11):1760. Olaf Sporns (2007) Complexity. Scholarpedia, 2(10):1623. Gregoire Nicolis and Catherine Rouvas-Nicolis (2007) Complex systems. Scholarpedia, 2(11):1473. James Meiss (2007) Dynamical systems. Scholarpedia, 2(2):1629. Giovanni Gallavotti (2008) Fluctuations. Scholarpedia, 3(6):5893. Anatoly M. Samoilenko (2007) Quasiperiodic oscillations. Scholarpedia, 2(5):1783. Philip Holmes and Eric T. Shea-Brown (2006) Stability. Scholarpedia, 1(10):1838. David H. Terman and Eugene M. Izhikevich (2008) State space. Scholarpedia, 3(3):1924. Catherine Rouvas-Nicolis and Gregoire Nicolis (2007) Stochastic resonance. Scholarpedia, 2(11):1474. Charles Pugh and Maurcio Matos Peixoto (2008) Structural stability. Scholarpedia, 3(9):4008. James Murdock (2006) Unfoldings. Scholarpedia, 1(12):1904. Further reading External linksIm Doc Severson, creator and author of the Fractal Energy for Weekly Options program. As you know, certain trading knowledge gives you an instant advantage that can multiply your trading profits. The information youre about to see has this potential. Now, I know you hear these types of promises all the time. So I wont insult your intelligence by boasting how I finally discovered the holy grail of trading. But what I can honestly say is your trading future could get a bit uncomfortable (if it hasnt already), especially if you rely on traditional oscillators and momentum tools such as Stochastics, MACD and RSI. Several factors within the market many out of your control are working against you right now. If youve recently noticed your go-to trading strategy failing you more times than not, heres how to maintain your edge in todays market. How would you change your trading strategy if you knew the market was working against you A crazy question, perhaps. But no doubt, at one time or another (or many times), you felt like the market wasnt on your side. Maybe it was because after repeated research and making calculated trades, you didnt see the profits you wanted. Even though you knew you did everything right Or maybe, even now, you feel like you win some. but then end up giving it all back. Lets face it. sometimes it seems like you just cant get ahead no matter how hard you try. Well, let me remind you that youre not alone in your frustration. In fact, if youve been actively trading during the last 21 months or so, you might be surprised to know that several factors many out of your control have been working against you. The uncomfortable truth is, the market made a dramatic shift in 2013 and nobodys talking about it. So if youve been trying strategies that worked in the past and you arent seeing the profits you want, your lack of success isnt all your fault. What Caused the Market Shift You see, in an effort to lower interest rates and spur economic growth, new currency has been thrust into our nations money supply. The Federal Reserve is currently buying 75 billion a month in bonds from its member banks. As you can see, this financial activity has continued for years and shows no signs of stopping. So it was inevitable that this new money would hit the stock market. And little did anyone know what kind of impact this would have on your trading. But starting in 2013, traders worldwide felt the effect. The problem, though, is few recognized the shift. And if they did, since they couldnt pinpoint the problem, they continued relying on the same strategies and tools ones created for markets that are much different than they are today . Ill explain more on this topic shortly. But can you now see why you arent the only one feeling frustrated In todays market, trends last longer. Furthermore, price continues to rise faster than it corrects. These factors the result of the government activity I mentioned earlier, as well as computerized trading combine to create hidden challenges for traders stuck using traditional strategies. So what is the solution to racking up predictable profits nowadays Are the right strategies from years past now the wrong approach Perhaps the Most Overlooked Options Secret for Trading Todays Market What Im about to show you is a remarkably effective (and surprisingly simple) way to identify moves in todays market. The added bonus is this process also ensures you get paid every 3 days. But first, let me explain how I made this discovery because I sense we share the same frustrations. You see, initially I didnt know what caused the market shift. Of course, as is likely the case for you, I noticed the effect on my trading. So much so that I found myself awake many nights, anxiously pacing around my living room. I couldnt shake the mystery from my mind. After all, Im a full-time trader. thousands of students rely on my advice for consistent income. and my tips are shared in widely read trading publications. So you can imagine my concern. After all, Im expected to have all the answers. Still, for whatever reason (my stubborn behavior), I was reluctant to stray from strategies that created such a successful trading business. As a trader, you live or die on your edge whether its discipline, a specific routine, lack of emotional trading or a proven strategy. So I was set on sticking with my tried-and-tested game plan. For decades, I relied on the same analysis methods as everyone else. Looking at charts, using trendlines, searching for patterns and noting certain indicators. Still, like most traders, I constantly crave reliable ways to predict market movement. As such, Im always researching new ideas. After all, popular indicators such as stochastics, MACD and RSI were first implemented many decades years ago. For example: RSI was developed by J. Welles Wilder and popularized in the 1978 book, New Concepts in Technical Trading Systems Stochastics was created by George Lane in the 1950s, and In the late 1970s, Gerald Appel created the MACD indicator. Markets changed considerably since this time. So why hasnt the process for predicting movement Thats why two years ago I was surprised to stumble on a discovery using research Id compiled for at least 10 years. That day I saw something different in my charts. something new. It sort of just popped out at me. Like when you look at those autostereograms you know those 3D images that appear once you know how to look at them. Suddenly, I saw a concept with the power to make incremental improvements in predicting market movement. The more I tested my theory, the more I believed it was the key to reading todays markets. the ones distorted by government intervention. What I saw that day were fractal patterns. Repeated configurations that display at every scale. From trees and rivers to clouds and seashells, nature is filled with these never-ending patterns. See what I mean Fortunately, the markets share this natural phenomenon too. Since discovering how fractals appear in the markets, Ive incorporated the principle into every level of my trading. And its become my main method for analysis and reducing risk. Let me show you. Once I discovered the presence of fractals within the markets, my biggest challenge was to figure out how to teach my process for producing predictable profits. It took me about two years to fine-tune the method. The Result is Fractal Energy Trading for Weekly Options. Enjoy Fast Profits with Minimal Risk Now, heres the deal: Using fractals as indicators is not a trick or technique. These are naturally occurring patterns that never end. Theyre clear in almost every chart. And once you know how to find them, youll actually see (not just predict) changes before they happen. When you know how to go into any trade situation and identify the fractal patterns, you can positively predict almost any movement on any timeframe. Best of all, theres nothing complex about the process. Keep in mind, anyone can tell you how to place a trade. What my training does is show you the best probable entry and exit both for maximum profits. It also reveals when markets change, so you know the times to get in and out. So if youre tired of trying techniques and never getting off the trading treadmill, this is your ticket to success. The fact is, markets change every couple years. Its up to you to recognize these adjustments and react to them. You saw earlier how last years market challenges were a proving ground for using fractals. And judging from the profits Ive made since, this process is a valid, viable and valuable trading tool. Along the way, I fine-tuned and perfected my method to create Fractal Energy Trading for Weekly Options. I quickly learned what to look for to manage risk so you protect your profits. Furthermore, instead of waiting several weeks or months to take profits using traditional Options methods, I adapted the process so you collect profits in only days 3 to be exact. Imagine having one go-to strategy that consistently delivered profits every 30 hours. Here are a few recent examples. Today, I invite you to join in on these profits. When you become a student in the Fractal Energy Trading for Weekly Options program, youll see when the markets are changing. before they actually change. Youll gain your edge back. youll match the velocity and duration of todays market. youll collect profits that youre leaving behind in the market right now money that belongs to you. Youll watch profits roll in whether the market goes up or down. After all, fractal patterns dont change they last forever. Why Keep Guessing the Markets Direction The Fractal Energy Trading for Weekly Options training is delivered in 21 modules over five weeks. Each one includes a written PDF and a training video. So whether you like learning by reading, watching or listening, you get the materials in a way that works best for you. And heres the thing. Each module also includes social commentary. That way you can interact with instructors, as well as other students. Review trades. share challenges. gather feedback. Whatever you need. you have a community of high-level traders willing to help. Its like having your own mastermind group of top performers. If, as the saying goes, two brains are better the one. Imagine what hundreds could do for your trading success. Also, because everyone learns at different speeds, you have lifetime access to the entire platform, including all the training modules. Ill even send you updates every time I add material. That way you always have the latest tips and strategies for making consistent profits. Of course, even with all this in place to ensure your success, you still may be skeptical. And thats understandable. Youve likely heard about mentors who disappear the moment you buy their course. Heck, maybe you experienced this nightmare yourself. The truth is, Im committed to your success and Ill prove it. To Ensure You Stay on Top of Every Trade. First, so you see trading with fractal indicators in real-time, Im holding four live webinars. Well go over charts and examples of current opportunities. That way you know exactly how I trade the markets to collect consistent profits. And, of course, youre free to ask me any questions during this time. Or, if you prefer, you can ask me a question directly inside the members-only platform. After all, when youre my student, you get an invitation to ask me any question to improve your trading. Other than my mentoring programs, the only other people who have direct access to me are my one-on-one coaching clients who must pay me 1,000 an hour for advice. You get it for simply being a student in the Fractal Energy Trading for Weekly Options program. Now, since were starting to talk numbers, youre probably wondering what this training costs. So lets just cut to the chase. A trading system this powerful, this exclusive one that took so much time, resources and research to create does require a considerable investment. Of course, considerable is a relative term. From what students who beta tested the program told me, the fee is a fraction of what they expected. And my guess is youll feel the same way, especially when you see in just a minute how I guarantee your success. To become a student today and immediately receive your 21 training modules. with written PDFs and step-by-step videos. entry into my community of high-performing traders. access to all four live webinars. lifetime access and support. and free updates every time I release them. You could probably expect to pay 4,995. But I wont ask you for that. Heck, 1,997 would be a steal and thats how much this course will cost during its next release. But because this is the first time Im releasing it to the public and I want to start the foundation of this community mastermind, Im willing to let this first group of traders in for just 997. or three payments of 397. Yes, your investment is less than what my private clients pay me for just one hour of time. And you get lifetime access Two. three. five. 10 years from now, you can still access the course and ask me any questions you want. When you become a student today, Ill also throw in three months of my Daily Income Rx newsletter. Alone, this is a 591 value. That way you can follow me, real-time, to see how I apply these strategies on a daily basis. Youll see a detailed description of each trade, when I enter, when I exit, and other important trade management and adjustment decisions I make. And heres what else I propose. I know your hard-earned income is on the line every time you trade. From a mentor perspective, its easy to give advice when your own money isnt at risk. Well, thats why Im throwing some skin in the game and guaranteeing your success with the Fractal Energy Trading for Weekly Options program. Join the training today, and Ill immediately give you access to everything so you can try it out. The videos and printed modules. the trading community. the webinars. the coaching from me. the regular updates. the newsletter. and much more. In fact, you can try it all for the next 30 days.
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