From 702cf36d93309d4bc64857935abd8aa478d5f741 Mon Sep 17 00:00:00 2001 From: ogerly Date: Sat, 21 Sep 2024 14:55:02 +0200 Subject: [PATCH] git init --- BTC__USD_daily.csv | 4278 ++++++++++++++++++++++++++++++++++++++++++++ README.md | 52 + app.py | 373 ++++ index.html | 113 ++ 4 files changed, 4816 insertions(+) create mode 100644 BTC__USD_daily.csv create mode 100644 README.md create mode 100644 app.py create mode 100644 index.html diff --git a/BTC__USD_daily.csv b/BTC__USD_daily.csv new file mode 100644 index 00000000..082e0df7 --- /dev/null +++ b/BTC__USD_daily.csv @@ -0,0 +1,4278 @@ +"Datum","Zuletzt","Eröffn.","Hoch","Tief","Vol.","+/- %" +"16.09.2024","58.465,8","59.126,2","59.204,3","58.138,6","59,03K","-1,14%" +"15.09.2024","59.138,5","59.995,6","60.377,6","58.717,9","47,01K","-1,43%" +"14.09.2024","59.995,4","60.514,8","60.609,4","59.494,3","44,47K","-0,85%" +"13.09.2024","60.511,6","58.134,4","60.625,4","57.656,8","102,12K","4,09%" 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mithilfe von gleitenden Durchschnitten (Moving Averages, MAs). Es erkennt Muster wie Bodenzonen, Spitzen, Auf- und Abwärtstrends sowie Kauf- und Verkaufsignale. + +## Funktionen + +1. **Datenverarbeitung**: + - Liest BTC-USD Tageskurse aus einer CSV-Datei + - Berechnet gleitende Durchschnitte (9, 20, 50, 100, 200, 400 Tage) + +2. **Mustererkennung**: + - `detect_bottoms()`: Identifiziert potenzielle Bodenzonen + - `detect_peaks()`: Erkennt Kursspitzen + - `detect_uptrends()` und `detect_downtrends()`: Analysieren Trendphasen + - `detect_buy_sell_signals()`: Generiert Kauf- und Verkaufssignale + +3. **Gewinnberechnung**: + - Simuliert Trades basierend auf den erkannten Signalen + - Berechnet den potenziellen Gewinn für einen gegebenen Investitionsbetrag + +4. **Visualisierung**: + - Erstellt ein interaktives Plotly-Diagramm mit zwei Untergrafiken: + 1. BTC-Kurs mit gleitenden Durchschnitten, Bodenzonen, Spitzen und Handelssignalen + 2. Flächendiagramm der Abstände zwischen den MAs + +## Verwendung + +1. Stellen Sie sicher, dass die erforderlichen Bibliotheken installiert sind: + ``` + pip install pandas plotly + ``` + +2. Legen Sie die BTC-USD Tageskursdaten als 'BTC__USD_daily.csv' im gleichen Verzeichnis ab. + +3. Führen Sie das Skript aus und geben Sie den gewünschten Investitionsbetrag ein. + +4. Das Skript generiert ein interaktives Diagramm und gibt den berechneten Gesamtgewinn aus. + +## Anpassungsmöglichkeiten + +- Passen Sie die `ma_windows`-Liste an, um andere gleitende Durchschnitte zu verwenden +- Ändern Sie den `distance_threshold` für die Bodenerkennung +- Modifizieren Sie die Funktionen zur Mustererkennung für andere Strategien + +## Hinweise + +- Dies ist ein Analysewerkzeug und keine Anlageempfehlung +- Vergangene Performance garantiert keine zukünftigen Ergebnisse +- Berücksichtigen Sie stets Risiken und führen Sie eigene Recherchen durch, bevor Sie investieren + +Möchten Sie, dass ich noch weitere Details zum Code oder zur Funktionsweise hinzufüge? \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 00000000..fd25ea3c --- /dev/null +++ b/app.py @@ -0,0 +1,373 @@ +import pandas as pd +import plotly.graph_objects as go +from plotly.subplots import make_subplots + +# --------------------------- +# Anpassbare Parameter +# --------------------------- + +# Liste der gleitenden Durchschnitte und Farben +ma_windows = [9, 20, 50, 100, 200, 400] +ma_colors = ['yellow', 'red', 'orange', 'green', 'purple', 'turquoise'] +ma_labels = [f'MA {window}' for window in ma_windows] + +# Schwellenwert für minimale Abstände zwischen den MAs bei der Bodenerkennung +distance_threshold = 0.005 # Kann angepasst werden + +# --------------------------- +# Daten einlesen und vorbereiten +# --------------------------- + +columns = ['Datum', 'Zuletzt', 'Eröffn.', 'Hoch', 'Tief', 'Vol.', '+/- %'] +url = 'BTC__USD_daily.csv' +df = pd.read_csv(url, skiprows=1, names=columns) +df['Datum'] = pd.to_datetime(df['Datum'], format='%d.%m.%Y') +df['Zuletzt'] = df['Zuletzt'].str.replace('.', '').str.replace(',', '.').astype(float) +df = df.iloc[::-1].reset_index(drop=True) + +# Den tatsächlichen BTC-Kurs hinzufügen +df['Preis'] = df['Zuletzt'] + +# Berechnung der gleitenden Durchschnitte +for window, label in zip(ma_windows, ma_labels): + df[label] = df['Preis'].rolling(window=window).mean() + +# Abstände zwischen den MAs berechnen +df['Dist_MA9_MA20'] = df['MA 9'] - df['MA 20'] +df['Dist_MA20_MA50'] = df['MA 20'] - df['MA 50'] +df['Dist_MA50_MA100'] = df['MA 50'] - df['MA 100'] +df['Dist_MA100_MA200'] = df['MA 100'] - df['MA 200'] +df['Dist_MA200_MA400'] = df['MA 200'] - df['MA 400'] + +# --------------------------- +# Funktionen zur Mustererkennung +# --------------------------- + +# Funktion zur Erkennung von Bodenzonen +def detect_bottoms(df): + bottoms = [] + for i in range(1, len(df)): + # Prüfen, ob die Abstände zwischen den MAs minimal sind + distances = [ + abs(df['MA 9'].iloc[i] - df['MA 20'].iloc[i]), + abs(df['MA 20'].iloc[i] - df['MA 50'].iloc[i]), + abs(df['MA 50'].iloc[i] - df['MA 100'].iloc[i]), + abs(df['MA 100'].iloc[i] - df['MA 200'].iloc[i]), + abs(df['MA 200'].iloc[i] - df['MA 400'].iloc[i]) if 'MA 400' in df.columns else 0 + ] + if all(dist / df['Preis'].iloc[i] < distance_threshold for dist in distances): + # Prüfen, ob MA20 die höheren MAs von unten nach oben durchbricht + ma20_crossed_above = all( + df['MA 20'].iloc[i] > df[ma].iloc[i] and df['MA 20'].iloc[i - 1] <= df[ma].iloc[i - 1] + for ma in ['MA 50', 'MA 100', 'MA 200', 'MA 400'] if ma in df.columns + ) + # Prüfen, ob MA50 die höheren MAs von unten nach oben durchbricht + ma50_crossed_above = all( + df['MA 50'].iloc[i] > df[ma].iloc[i] and df['MA 50'].iloc[i - 1] <= df[ma].iloc[i - 1] + for ma in ['MA 100', 'MA 200', 'MA 400'] if ma in df.columns + ) + if ma20_crossed_above and ma50_crossed_above: + bottoms.append(df['Datum'].iloc[i]) + return bottoms + +# Funktion zur Erkennung von Spitzen +def detect_peaks(df): + peaks = [] + i = 1 + while i < len(df): + # Anfang der Spitze: MA9 kreuzt MA20 nach oben + if df['MA 9'].iloc[i] > df['MA 20'].iloc[i] and df['MA 9'].iloc[i - 1] <= df['MA 20'].iloc[i - 1]: + start_date = df['Datum'].iloc[i] + # Suche nach dem Ende der Spitze + for j in range(i+1, len(df)): + if df['MA 20'].iloc[j] < df['MA 50'].iloc[j] and df['MA 20'].iloc[j - 1] >= df['MA 50'].iloc[j - 1]: + end_date = df['Datum'].iloc[j] + peaks.append((start_date, end_date)) + i = j # Fortfahren ab dem Ende der Spitze + break + else: + # Kein Ende der Spitze gefunden + i += 1 + else: + i += 1 + return peaks + +# Funktion zur Erkennung von Aufwärtstrends +def detect_uptrends(df): + uptrends = [] + in_uptrend = False + for i in range(len(df)): + if (df['MA 9'].iloc[i] > df['MA 20'].iloc[i] and + df['MA 20'].iloc[i] > df['MA 50'].iloc[i] and + df['MA 50'].iloc[i] > df['MA 100'].iloc[i]): + if not in_uptrend: + in_uptrend = True + start_date = df['Datum'].iloc[i] + else: + if in_uptrend: + in_uptrend = False + end_date = df['Datum'].iloc[i] + uptrends.append((start_date, end_date)) + if in_uptrend: + uptrends.append((start_date, df['Datum'].iloc[-1])) + return uptrends + +# Funktion zur Erkennung von Abwärtstrends +def detect_downtrends(df): + downtrends = [] + in_downtrend = False + for i in range(len(df)): + if (df['MA 9'].iloc[i] < df['MA 20'].iloc[i] and + df['MA 20'].iloc[i] < df['MA 50'].iloc[i]): + if not in_downtrend: + in_downtrend = True + start_date = df['Datum'].iloc[i] + else: + if in_downtrend: + in_downtrend = False + end_date = df['Datum'].iloc[i] + downtrends.append((start_date, end_date)) + if in_downtrend: + downtrends.append((start_date, df['Datum'].iloc[-1])) + return downtrends + +# Funktion zur Erkennung von Kauf- und Verkaufsignalen +def detect_buy_sell_signals(df): + signals = [] + for i in range(1, len(df)): + if df['MA 20'].iloc[i] > df['MA 50'].iloc[i] and df['MA 20'].iloc[i - 1] <= df['MA 50'].iloc[i - 1]: + signals.append((df['Datum'].iloc[i], 'Kauf')) + elif df['MA 20'].iloc[i] < df['MA 50'].iloc[i] and df['MA 20'].iloc[i - 1] >= df['MA 50'].iloc[i - 1]: + signals.append((df['Datum'].iloc[i], 'Verkauf')) + return signals + +# --------------------------- +# Investitionsbetrag erfassen +# --------------------------- + +# Eingabe des Investitionsbetrags +investitionsbetrag = float(input("Bitte geben Sie den Investitionsbetrag in USD ein: ")) + +# --------------------------- +# Mustererkennung durchführen +# --------------------------- + +bottoms = detect_bottoms(df) +peaks = detect_peaks(df) +uptrends = detect_uptrends(df) +downtrends = detect_downtrends(df) +signals = detect_buy_sell_signals(df) + +# --------------------------- +# Gewinnberechnung +# --------------------------- + +def calculate_profit(df, signals, investitionsbetrag): + balance = investitionsbetrag + btc_holding = 0 + last_action = None + trade_history = [] + + for date, signal in signals: + preis = df.loc[df['Datum'] == date, 'Preis'].values[0] + if signal == 'Kauf' and last_action != 'Kauf': + # Kaufen + btc_holding = balance / preis + balance = 0 + last_action = 'Kauf' + trade_history.append({'Datum': date, 'Aktion': 'Kauf', 'Preis': preis, 'BTC': btc_holding, 'Balance': balance}) + elif signal == 'Verkauf' and last_action == 'Kauf': + # Verkaufen + balance = btc_holding * preis + btc_holding = 0 + last_action = 'Verkauf' + trade_history.append({'Datum': date, 'Aktion': 'Verkauf', 'Preis': preis, 'BTC': btc_holding, 'Balance': balance}) + + # Am Ende alles verkaufen, falls noch BTC gehalten werden + if btc_holding > 0: + preis = df['Preis'].iloc[-1] + balance = btc_holding * preis + trade_history.append({'Datum': df['Datum'].iloc[-1], 'Aktion': 'Verkauf (Ende)', 'Preis': preis, 'BTC': 0, 'Balance': balance}) + btc_holding = 0 + + gesamtgewinn = balance - investitionsbetrag + return gesamtgewinn, trade_history + +gesamtgewinn, trade_history = calculate_profit(df, signals, investitionsbetrag) + +print(f"\nGesamtgewinn: {gesamtgewinn:.2f} USD") + +# --------------------------- +# Visualisierung +# --------------------------- + +# Subplots erstellen: 2 Reihen (Hauptdiagramm und Flächendiagramm) +fig = make_subplots( + rows=2, cols=1, + shared_xaxes=True, + vertical_spacing=0.05, + subplot_titles=( + 'BTC Kurs mit gleitenden Durchschnitten und Signalen', + 'Flächendiagramm der Abstände zwischen den MAs' + ) +) + +# Den tatsächlichen BTC-Kurs als weiße Linie hinzufügen (erstes Diagramm) +fig.add_trace( + go.Scatter( + x=df['Datum'], + y=df['Preis'], + mode='lines', + name='BTC Kurs', + line=dict(color='white', width=2), + hoverinfo='x+y', + ), + row=1, col=1 +) + +# Gesamt-MAs hinzufügen (sichtbar) +for label, color in zip(ma_labels, ma_colors): + fig.add_trace( + go.Scatter( + x=df['Datum'], + y=df[label], + mode='lines', + name=label, + line=dict(color=color, width=2), + hoverinfo='x+y', + visible=True, + ), + row=1, col=1 + ) + +# Bodenzonen markieren +for date in bottoms: + preis = df.loc[df['Datum'] == date, 'Preis'].values[0] + fig.add_trace( + go.Scatter( + x=[date], + y=[preis], + mode='markers', + marker=dict(symbol='triangle-up', color='green', size=12), + name='Boden', + showlegend=False, + hovertemplate=f'Boden: {date.strftime("%d.%m.%Y")}' + ), + row=1, col=1 + ) + +# Spitzen markieren +for start_date, end_date in peaks: + preis_start = df.loc[df['Datum'] == start_date, 'Preis'].values[0] + preis_end = df.loc[df['Datum'] == end_date, 'Preis'].values[0] + # Anfang der Spitze + fig.add_trace( + go.Scatter( + x=[start_date], + y=[preis_start], + mode='markers', + marker=dict(symbol='triangle-down', color='red', size=12), + name='Spitze Beginn', + showlegend=False, + hovertemplate=f'Spitze Beginn: {start_date.strftime("%d.%m.%Y")}' + ), + row=1, col=1 + ) + # Ende der Spitze + fig.add_trace( + go.Scatter( + x=[end_date], + y=[preis_end], + mode='markers', + marker=dict(symbol='triangle-down', color='orange', size=12), + name='Spitze Ende', + showlegend=False, + hovertemplate=f'Spitze Ende: {end_date.strftime("%d.%m.%Y")}' + ), + row=1, col=1 + ) + +# Kauf- und Verkaufsignale markieren +for date, signal in signals: + preis = df.loc[df['Datum'] == date, 'Preis'].values[0] + color = 'green' if signal == 'Kauf' else 'red' + symbol = 'arrow-up' if signal == 'Kauf' else 'arrow-down' + fig.add_trace( + go.Scatter( + x=[date], + y=[preis], + mode='markers', + marker=dict(symbol=symbol, color=color, size=12), + name=signal, + showlegend=False, + hovertemplate=f'{signal}: {date.strftime("%d.%m.%Y")}' + ), + row=1, col=1 + ) + +# Gestapeltes Flächendiagramm der Abstände zwischen den MAs hinzufügen (zweites Diagramm) +abstand_traces = [ + ('Dist_MA9_MA20', 'yellow', 'Abstand MA 9-20'), + ('Dist_MA20_MA50', 'red', 'Abstand MA 20-50'), + ('Dist_MA50_MA100', 'orange', 'Abstand MA 50-100'), + ('Dist_MA100_MA200', 'green', 'Abstand MA 100-200'), + ('Dist_MA200_MA400', 'purple', 'Abstand MA 200-400') +] + +for dist_label, color, name in abstand_traces: + fig.add_trace( + go.Scatter( + x=df['Datum'], + y=df[dist_label], + mode='lines', + name=name, + line=dict(color=color, width=0), + fill='tozeroy', + stackgroup='one', + hoverinfo='x+y', + visible='legendonly', # Standardmäßig ausgeblendet + ), + row=2, col=1 + ) + +# Layout anpassen +fig.update_layout( + title='BTC Kurs und Abstände zwischen den gleitenden Durchschnitten (MAs)', + xaxis=dict( + rangeslider=dict(visible=True), + type='date' + ), + yaxis=dict( + title='Preis in USD' + ), + yaxis2=dict( + title='Abstand' + ), + hovermode='x unified', + legend=dict( + title='Legende', + orientation='v', + x=1.02, + y=1, + bordercolor='white', + borderwidth=1, + ), + template='plotly_dark', + autosize=True, +) + +# Gewinnanzeige hinzufügen +fig.add_annotation( + xref='paper', yref='paper', + x=0.5, y=-0.2, + text=f"Investitionsbetrag: {investitionsbetrag:.2f} USD
Gesamtgewinn: {gesamtgewinn:.2f} USD", + showarrow=False, + font=dict(size=14, color='white'), + align='center' +) + +# Responsives Design aktivieren +config = {'responsive': True} + +# Diagramm anzeigen +fig.show(config=config) diff --git a/index.html b/index.html new file mode 100644 index 00000000..34329465 --- /dev/null +++ b/index.html @@ -0,0 +1,113 @@ + + + + + + MarketWave Analyzer - Fortschrittliche Kursanalyse + + + +
+

MarketWave Analyzer

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Fortschrittliche Kursanalyse für informierte Entscheidungen

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Entdecken Sie die Kraft der Daten

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Der MarketWave Analyzer ist ein leistungsstarkes Tool zur Analyse von Kursdaten verschiedener Finanzinstrumente. Mit unserer fortschrittlichen Technologie können Sie Markttrends erkennen, potenzielle Einstiegs- und Ausstiegspunkte identifizieren und fundierte Handelsentscheidungen treffen.

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Flexible Analyse

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Analysieren Sie Kursdaten von Kryptowährungen, Aktien, Rohstoffen und mehr.

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Musterkennung

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Identifizieren Sie automatisch wichtige Marktmuster und Trendwenden.

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Nutzen Sie interaktive Diagramme für eine intuitive Datenvisualisierung.

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Warum MarketWave Analyzer?

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MarketWave Analyzer ist ein Open-Source-Projekt, das von einer engagierten Gemeinschaft von Entwicklern und Finanzexperten unterstützt wird. Wir glauben an Transparenz und kollaborative Entwicklung.

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+ + \ No newline at end of file