# ----------------------------------------------------
# 0. Bibliotheken importieren
import ccxt
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, timezone

# ----------------------------------------------------
# 1. Binance initialisieren
exchange = ccxt.binance()

# ----------------------------------------------------
# 2. Dynamisches 'since' (3 Jahre zurück)
days_back = 3 * 365
since_ms = int((datetime.now(timezone.utc) - timedelta(days=days_back)).timestamp() * 1000)

# ----------------------------------------------------
# 3. Daten abrufen & aufbereiten
def fetch_ohlcv_since(symbol='ETH/USDT', timeframe='1d', since=since_ms):
    all_rows, since_ptr = [], since
    while True:
        rows = exchange.fetch_ohlcv(symbol, timeframe, since=since_ptr, limit=1000)
        if not rows:
            break
        all_rows += rows
        since_ptr = rows[-1][0] + 1
        if len(rows) < 1000:
            break

    df = pd.DataFrame(all_rows, columns=['timestamp','open','high','low','close','volume_eth'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)

    # Volumenberechnung
    df['volume_usd'] = df['volume_eth'] * df['close']
    df['volume_usd_mio'] = df['volume_usd'] / 1e6
    df['volume_usd_fmt'] = df['volume_usd_mio'].apply(lambda x: f"{x:,.2f} Mio $")
    df['volume_eth_fmt'] = df['volume_eth'].apply(lambda x: f"{x:,.2f} ETH")

    return df

# ----------------------------------------------------
# 4. Daten laden
df = fetch_ohlcv_since()

# ----------------------------------------------------
# 5. RSI & MA50 berechnen
delta = df['close'].diff()
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(window=14).mean()
avg_loss = loss.rolling(window=14).mean()
rs = avg_gain / avg_loss
df['RSI'] = 100 - (100 / (1 + rs))

df['MA50'] = df['close'].rolling(window=50).mean()

# ----------------------------------------------------
# 6. BUY / SELL Signale definieren (angepasste Logik)
df['signal'] = 'HOLD'
df.loc[(df['RSI'] < 45) & (df['close'] > df['MA50']), 'signal'] = 'BUY'
df.loc[(df['RSI'] > 55) & (df['close'] < df['MA50']), 'signal'] = 'SELL'

# Übersicht
print(df['signal'].value_counts())

signal
HOLD    979
BUY      60
SELL     56
Name: count, dtype: int64

import ta# RSI und MA berechnen
df['RSI'] = ta.momentum.RSIIndicator(close=df['close']).rsi()
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
df.tail()

# Kurs und gleitende Durchschnitte
plt.figure(figsize=(14,6))
plt.plot(df['timestamp'], df['close'], label='Close')
plt.plot(df['timestamp'], df['MA50'], label='MA50')
plt.plot(df['timestamp'], df['MA200'], label='MA200')
plt.title('ETH/USDT Preis & MA – Tageschart')
plt.xlabel('Datum')
plt.ylabel('Preis (USDT)')
plt.legend()
plt.grid(True)
plt.show()

# RSI Plot
plt.figure(figsize=(14,4))
plt.plot(df['timestamp'], df['RSI'], label='RSI')
plt.axhline(30, color='green', linestyle='--', label='Überverkauft')
plt.axhline(70, color='red', linestyle='--', label='Überkauft')
plt.title('RSI – ETH/USDT')
plt.legend()
plt.grid(True)
plt.show()

# ----------------------------------------------------
# 7. Chart mit Signalen
plt.figure(figsize=(14,6))
plt.plot(df['timestamp'], df['close'], label='Close Price')
plt.plot(df['timestamp'], df['MA50'], label='MA50')

buy_signals = df[df['signal'] == 'BUY']
sell_signals = df[df['signal'] == 'SELL']

plt.scatter(buy_signals['timestamp'], buy_signals['close'], label='BUY', marker='^', color='green')
plt.scatter(sell_signals['timestamp'], sell_signals['close'], label='SELL', marker='v', color='red')

plt.title("Swing Trading Signale (angepasst)")
plt.xlabel("Datum")
plt.ylabel("Preis")
plt.legend()
plt.grid(True)
plt.show()

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	timestamp	open	high	low	close	volume_eth	volume_usd	volume_usd_mio	volume_usd_fmt	volume_eth_fmt	RSI	MA50	signal	MA200
1090	2025-06-10 00:00:00+00:00	2680.13	2827.00	2655.61	2816.40	1.138405e+06	3.206205e+09	3206.205187	3,206.21 Mio $	1,138,405.48 ETH	67.169034	2312.3156	HOLD	2657.78485
1091	2025-06-11 00:00:00+00:00	2816.41	2879.22	2743.75	2771.61	8.502109e+05	2.356453e+09	2356.453122	2,356.45 Mio $	850,210.93 ETH	63.942553	2332.6226	HOLD	2654.67335
1092	2025-06-12 00:00:00+00:00	2771.60	2784.83	2616.24	2642.65	6.995934e+05	1.848780e+09	1848.780434	1,848.78 Mio $	699,593.38 ETH	55.653396	2349.5742	HOLD	2651.08060
1093	2025-06-13 00:00:00+00:00	2642.66	2645.45	2436.98	2579.19	1.202176e+06	3.100641e+09	3100.641084	3,100.64 Mio $	1,202,176.30 ETH	52.075840	2365.7650	HOLD	2646.90410
1094	2025-06-14 00:00:00+00:00	2579.19	2579.43	2518.51	2536.09	1.405074e+05	3.563393e+08	356.339332	356.34 Mio $	140,507.37 ETH	49.737331	2380.7948	HOLD	2642.96090