cbazza/Place-Order-Trading-Bot

Fork 0

Files

T

cbazza f7b72f0145 Trading Bot V1.3 added

2025-09-11 17:34:32 +02:00

132 KiB

Raw Blame History

Import Libaries

In [1]:


def dynamic_update_sl_tp(symbol, atr_mult=1.5, rrr_factor=None, check_tf='m5', trail_buffer=0.5, max_retries=2):
    """
    Dynamisches Nachziehen von SL/TP für offene Trades.
    Läuft idealerweise bei jedem neuen Kerzenende von check_tf (z.B. m5) oder bei Intrabar-ATR-Sprüngen.
    """
    # load recent bars for the timeframe
    df = get_rates(check_tf).iloc[-3:]
    if df is None or df.empty or len(df) < 2:
        return
    atr = float(df['atr'].iloc[-1]) if 'atr' in df.columns else None
    if atr is None or atr == 0:
        return
    # quick check for intrabar move (previous close -> current close)
    prev = float(df['close'].iloc[-2])
    curr = float(df['close'].iloc[-1])
    price_move = abs(curr - prev)
    # only update on new bar or large intrabar move (>= 0.5 * ATR)
    if price_move < 0.5 * atr and False:
        return

    positions = mt.positions_get(symbol=symbol) or []
    for pos in positions:
        # normalize pos interface (support dict-like or object)
        try:
            ticket = pos.ticket
            entry_price = pos.price_open
            pos_type = pos.type  # 0=buy,1=sell
            cur_bid = mt.symbol_info_tick(symbol).bid
            cur_ask = mt.symbol_info_tick(symbol).ask
        except Exception:
            # fallback for dict-like
            ticket = pos.get('ticket', None)
            entry_price = pos.get('price_open', pos.get('entry_price'))
            pos_type = pos.get('type')
            cur_bid = mt.symbol_info_tick(symbol).bid
            cur_ask = mt.symbol_info_tick(symbol).ask

        current_price = cur_bid if pos_type == 0 else cur_ask

        # compute desired new SL/TP using update_trailing_sl_tp logic
        # profit in ATR
        profit_atr = (current_price - entry_price) / atr if pos_type == 0 else (entry_price - current_price) / atr
        # base sl
        if pos_type == 0:
            base_sl = current_price - atr_mult * atr
            new_sl = max((pos.sl or 0), base_sl)
            if profit_atr > 2:
                new_sl = max(new_sl, entry_price + 1.5 * atr)
            elif profit_atr > 1:
                new_sl = max(new_sl, entry_price + 1.0 * atr)
            elif profit_atr > 0.5:
                new_sl = max(new_sl, entry_price + 0.5 * atr)
            new_tp = entry_price + (entry_price - new_sl) * (rrr_factor or 2.0)
        else:
            base_sl = current_price + atr_mult * atr
            new_sl = min((pos.sl or 999999), base_sl)
            if profit_atr > 2:
                new_sl = min(new_sl, entry_price - 1.5 * atr)
            elif profit_atr > 1:
                new_sl = min(new_sl, entry_price - 1.0 * atr)
            elif profit_atr > 0.5:
                new_sl = min(new_sl, entry_price - 0.5 * atr)
            new_tp = entry_price - (new_sl - entry_price) * (rrr_factor or 2.0)

        # respect broker stops level and rounding
        info = mt.symbol_info(symbol)
        digits = info.digits
        point = info.point
        stops_level = info.trade_stops_level * point

        tick = mt.symbol_info_tick(symbol)
        bid, ask = tick.bid, tick.ask
        if pos_type == 0:
            if new_sl >= bid - stops_level:
                new_sl = bid - stops_level
            if new_tp <= ask + stops_level:
                new_tp = ask + stops_level
        else:
            if new_sl <= ask + stops_level:
                new_sl = ask + stops_level
            if new_tp >= bid - stops_level:
                new_tp = bid - stops_level

        new_sl = round(new_sl, digits)
        new_tp = round(new_tp, digits)

        # only update if change larger than point
        if (pos.sl is None or abs(new_sl - pos.sl) > point) or (pos.tp is None or abs(new_tp - pos.tp) > point):
            for attempt in range(max_retries):
                req = {
                    'action': mt.TRADE_ACTION_SLTP,
                    'symbol': symbol,
                    'position': ticket,
                    'sl': new_sl,
                    'tp': new_tp
                }
                res = mt.order_send(req)
                if getattr(res, 'retcode', None) == mt.TRADE_RETCODE_DONE:
                    print(f"Updated SL/TP for {symbol} #{ticket}: SL={new_sl}, TP={new_tp}")
                    break
                elif getattr(res, 'retcode', None) == mt.TRADE_RETCODE_INVALID_STOPS:
                    adjust = 2 * stops_level
                    if pos_type == 0:
                        new_sl = round(bid - adjust, digits)
                        new_tp = round(ask + adjust, digits)
                    else:
                        new_sl = round(ask + adjust, digits)
                        new_tp = round(bid - adjust, digits)
                    continue
                else:
                    print('SL/TP update failed', getattr(res, 'retcode', None), getattr(res, 'comment', None))
                    break



def linreg_slope(series):
    """Central linear regression slope helper (returns slope as float)."""
    from sklearn.linear_model import LinearRegression
    import numpy as np
    if series is None or len(series) < 3:
        return 0.0
    X = np.arange(len(series)).reshape(-1, 1)
    y = series.values.reshape(-1, 1)
    model = LinearRegression().fit(X, y)
    return float(model.coef_[0][0])


#!pip install ta_lib-0.6.5-cp311-cp311-win_amd64.whl

#%pip install talib

#!pip install ta
from ta.trend import ADXIndicator, EMAIndicator
from ta.momentum import RSIIndicator
from talib import CDLHAMMER, CDLSHOOTINGSTAR

import pandas as pd
import matplotlib.pyplot as plt
import mplfinance as mpf
import keyring as kr
import MetaTrader5 as mt
import requests
import re
from time import sleep
import sqlite3 as db
#import matplotlib.pyplot as plt
import pandas_ta as ta
import numpy as np

from sklearn.linear_model import LinearRegression
from scipy.signal import savgol_filter
from scipy.signal import find_peaks




# login to your Trading Account - sign up in the description
mt.initialize()
    
login = 10800246
server = 'VantageInternational-Demo'
password = kr.get_password(server, str(login))


mt.login(login, password, server)

project = "trading-" + server[-4::1]
project = project.lower()
project

pause_trading = 0


symbols = ['XAUUSD']
#symbols = ['BTCUSD']

#'BTCUSD', 'ETHUSD', 
            #'XRPUSD', , 'EURNZD', 'EURUSD'

volume_dict = {
    'BTCUSD' : 0.1,
    'BTCUSD_short' : 0.1,

    'ETHUSD' : 1.0,
    'ETHUSD_short' : 1.0,
    
    'XRPUSD': 0.1,
    'XRPUSD_short': 0.1,

    'XAUUSD': 0.1,
    'XAUUSD_short': 0.1,

    'EURUSD': 0.1,
    'EURUSD_short': 0.1,

    'EURNZD': 0.1,
    'EURNZD_short': 0.1,

}

periods_dict = {
    'BTCUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],
    
    'ETHUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],

    
    'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
    
    'XAUUSD':  [ 'm15'],

    
    'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],


    'EURNZD': ['m5', 'm2', 'm1'],

}

def get_symbol():
    global symbols, pause_trading, periods_dict
    pricemovement = {}

    for s in symbols:
        items = mt.symbol_info(s)
        pricemovement[s] = round(items.price_change,2)
        #print(s, round(items.price_change,2))

    #percentage = pricemovement[max(pricemovement, key=pricemovement.get)]
    #symbol = max(pricemovement, key=pricemovement.get)
    #volume = volume_dict[symbol]

    sorted_pricemovement = sorted(pricemovement.items(), key=lambda x:x[1], reverse=True)
    converted_dict = dict(sorted_pricemovement)

    print(converted_dict)

    percentage = converted_dict[max(converted_dict, key=converted_dict.get)]
    symbol = max(converted_dict, key=converted_dict.get)
    volume = volume_dict[symbol]


    print(symbol, percentage, volume)
    # if percentage > 0: # and percentage < 0.8:
    #    periods_dict[symbol] =  ['m15', 'm5', 'm2', 'm1']
    # elif percentage > 0.8:
    #     periods_dict[symbol] = ['h1', 'm30', 'm15', 'm5', 'm1']

    #

    #print(periods_dict)

    # if percentage > 0 and mt.positions_total() == 0:
    #     pause_trading = 0 #0 no puase
    #     return symbol, percentage, volume, periods_dict
    # elif percentage < 0.1 and mt.positions_total() == 0:
    #     print("aktuell kein neues Symbol, pause Trading")
    #     pause_trading = 1 #1 pause
    #     return None

    return symbol, percentage, volume, periods_dict

get_symbol()

def set_symbol():
    global symbol, volume, volume_dict
    symb = get_symbol()
    if symb != None:
        symbol = symb[0]
        volume = volume_dict[symb[0]]
    

get_symbol()

set_symbol()


symbol, volume, pause_trading

pos = mt.positions_get()
for i in pos:
    #if i.comment == 'Retracement Bot':
    #    print(i.ticket)

    if bool(re.search('^BuyStop[0-9]{2}', i.comment)):
        print(i.ticket)

mt.positions_total()

strategy_name = 'Retracement Bot'
pos = mt.positions_get()
for p in pos:
    if p.comment == strategy_name:
        print(p.comment)
print(pos)

def market_order(symbol, volume, order_type, deviation=20, magic=30, stoploss=0.0, take_profit=0.0,
                 strategy_name='Retracement Bot'):

    global project, pause_trading

    project_id_dict = {
        'trading-demo': 'a3f3ae',
        'trading-live': '747543'
    }

    order_type_dict = {
        'buy': mt.ORDER_TYPE_BUY,
        'sell': mt.ORDER_TYPE_SELL
    }

    price_dict = {
        'buy': mt.symbol_info_tick(symbol).ask,
        'sell': mt.symbol_info_tick(symbol).bid
    }

    buypos = []

    pos = mt.positions_get()
    for p in pos:
        if p.comment == strategy_name:
            buypos.append('true')
    
    activepos = buypos.count('true')

    if order_type == 'buy' and activepos == 0 and pause_trading == 0: # and mt.positions_total() == 0:
        
        request = {
            "action": mt.TRADE_ACTION_DEAL,
            "symbol": symbol,
            "volume": volume,  # FLOAT
            "type": order_type_dict[order_type],
            "price": price_dict[order_type],
            "sl":  stoploss,  # FLOAT
            "tp":  take_profit,  # FLOAT
            "deviation": deviation,  # INTERGER
            "magic": magic,  # INTERGER
            "comment": strategy_name,
            "type_time": mt.ORDER_TIME_GTC,
            "type_filling": mt.ORDER_FILLING_IOC,  # mt.ORDER_FILLING_FOK if IOC does not work
        }

        requests.post('https://api.mynotifier.app', {
            "apiKey": 'beafb52e-3cb6-477a-92ef-2f10bff50e20',
            "message": "Es wrude ein Handel eröffnet!",
            "description": "Bitte kontrolliere die Position",
            "type": "info",#"info", # info, error, warning or success
            "project": project_id_dict[project]
        })

        order_result = mt.order_send(request)
        #return (order_result)
    
    elif order_type == 'sell' and mt.positions_total() > 0:
        pos = mt.positions_get()
        for p in pos:
            if p.comment == strategy_name:
                # while schleife ?
                positions = mt.positions_get()
                ticket = p.ticket
                request = {
                    "action": mt.TRADE_ACTION_DEAL,
                    "symbol": symbol,
                    "volume": volume,  # FLOAT
                    "type": order_type_dict[order_type],
                    "price": price_dict[order_type],
                    "position": ticket,
                    "sl": stoploss,  # FLOAT
                    "tp": take_profit,  # FLOAT
                    "deviation": deviation,  # INTERGER
                    "magic": magic,  # INTERGER
                    "comment": strategy_name,
                    "type_time": mt.ORDER_TIME_GTC,
                    "type_filling": mt.ORDER_FILLING_IOC,  # mt.ORDER_FILLING_FOK if IOC does not work
                }

                requests.post('https://api.mynotifier.app', {
                    "apiKey": 'beafb52e-3cb6-477a-92ef-2f10bff50e20',
                    "message": "Es wrude ein Handel geschlossen!",
                    "description": "Bitte prüfe die Position",
                    "type": "info",#"info", # info, error, warning or success
                    "project": project_id_dict[project]
                })

                order_result = mt.order_send(request)
                #return (order_result)
    

debug = False

trend_dict = {
    #'m5': '',
    #'m10': '',
    'm15': '',
    #'m30': '',
    #'h4': '',
}

timeframes_dict = {
    'm1': mt.TIMEFRAME_M1,
    'm2': mt.TIMEFRAME_M2,
    'm3': mt.TIMEFRAME_M3,
    'm5': mt.TIMEFRAME_M5,
    'm15': mt.TIMEFRAME_M15,
    'm20': mt.TIMEFRAME_M20,
    'm30': mt.TIMEFRAME_M30,
    'h1': mt.TIMEFRAME_H1,
    'h4': mt.TIMEFRAME_H4,
    'd1': mt.TIMEFRAME_D1,
}


# timeframes = {
#         'm1': mt.TIMEFRAME_M1,
#         'm2': mt.TIMEFRAME_M2,
#         'm3': mt.TIMEFRAME_M3,
#         'm5': mt.TIMEFRAME_M5,
#         'm15': mt.TIMEFRAME_M15,
#         'm20': mt.TIMEFRAME_M20,
#         'm30': mt.TIMEFRAME_M30,
#         'h1': mt.TIMEFRAME_H1,
#     }

print(trend_dict)

def get_rates(periode, bars=300):
    #global symbol

    # OHLC abrufen
    ohlc = mt.copy_rates_from_pos(symbol, timeframes_dict[periode], 0, bars)
    df = pd.DataFrame(ohlc)
    df['time'] = pd.to_datetime(df['time'], unit='s')

    # Umwandeln in float
    df["open"] = df["open"].astype(float)
    df["high"] = df["high"].astype(float)
    df["low"] = df["low"].astype(float)
    df["close"] = df["close"].astype(float)

    # ATR berechnen (klassisch 14)
    df["atr"] = ta.atr(high=df["high"], low=df["low"], close=df["close"], length=14)

    # leichte Glättung (optional, um Rauschen zu reduzieren)
    df["atr"] = df["atr"].rolling(window=5).mean()

    # Index setzen
    df.set_index("time", inplace=True)

    # NaNs entfernen (anfangs durch ATR-Berechnung)
    df = df.dropna()

    return df


get_rates('h4', 200)


def get_trend(timeframe="h4", lookback=150):
    """
    Bestimme Trendrichtung per Linear Regression.
    Liefert ein dict mit keys: trend (uptrend/downtrend/sideways), slope, atr, slope_threshold, price.
    """
    import numpy as np
    df = get_rates(timeframe, bars=lookback)
    if df is None or df.empty:
        return {"trend": "sideways", "slope": 0.0, "atr": 0.0, "slope_threshold": 0.0, "price": None}

    slope = linreg_slope(df['close'].iloc[-lookback:])

    # Ensure ATR exists
    if 'atr' not in df.columns or df['atr'].isnull().all():
        # fallback ATR calc
        hl = df['high'] - df['low']
        hc = (df['high'] - df['close'].shift()).abs()
        lc = (df['low'] - df['close'].shift()).abs()
        tr = pd.concat([hl, hc, lc], axis=1).max(axis=1)
        atr_series = tr.rolling(14, min_periods=1).mean()
        atr = float(atr_series.iloc[-1])
    else:
        atr = float(df['atr'].iloc[-1])

    current_price = float(df['close'].iloc[-1])

    slope_threshold = (atr / current_price) * 1.2 if current_price and atr else 0.0

    if abs(slope) < slope_threshold:
        trend = 'sideways'
    else:
        trend = 'uptrend' if slope > 0 else 'downtrend'

    return {"trend": trend, "slope": float(slope), "atr": atr, "slope_threshold": float(slope_threshold), "price": current_price}


def get_top_down_signal(symbol=symbol):
    """
    Top-Down Ansatz:
      - D1 / H4: Trend bestimmen
      - H1 / M30: Setup identifizieren
      - M15 / M5: Einstiege optimieren (Signale ohne Tradeausführung)
    """
    global trend_dict

    # --- Höhere Timeframes ---
    d1_trend_info = get_trend("d1", lookback=200)
    h4_trend_info = get_trend("h4", lookback=150)

    # --- Mittlere Timeframes für Setups ---
    h1_trend_info = get_trend("h1", lookback=100)
    m30_trend_info = get_trend("m30", lookback=60)

    # --- Niedrigere Timeframes für Einstiege (nur Signal, kein Trade) ---
    m15_trend_info = get_trend("m15", lookback=50)
    m5_trend_info = get_trend("m5", lookback=20)

    # --- Konsolidierte Trendanalyse ---
    top_down_trend = "sideways"
    if d1_trend_info["trend"] == h4_trend_info["trend"]:
        top_down_trend = d1_trend_info["trend"]

    # --- Setup-Bedingungen ---
    setup_ready = False
    if top_down_trend != "sideways":
        if h1_trend_info["trend"] == top_down_trend and m30_trend_info["trend"] == top_down_trend:
            setup_ready = True

    # --- Einstiegsberechnung ---
    entry_signal = 0
    if setup_ready:
        if m15_trend_info["trend"] == top_down_trend and m5_trend_info["trend"] == top_down_trend:
            entry_signal = 1 if top_down_trend == "uptrend" else -1

    # --- Speichern ---
    trend_dict["top_down"] = {
        "D1": d1_trend_info,
        "H4": h4_trend_info,
        "H1": h1_trend_info,
        "M30": m30_trend_info,
        "M15": m15_trend_info,
        "M5": m5_trend_info,
        "top_down_trend": top_down_trend,
        "setup_ready": setup_ready,
        "entry_signal": entry_signal
    }

    return trend_dict["top_down"]


get_trend("h1")

get_top_down_signal(symbol)

get_trend()

def get_trend_fast(period):
    global trend_dict, periods_dict, symbol

    df2 = get_rates(period).iloc[-200:]
    df2["close_smooth"] = savgol_filter(df2.close, 15, 5)  # kleinere Glättung

    atr = df2.atr.iloc[-1]

    # Weniger strenge Peak-Erkennung
    peaks_idx, _ = find_peaks(df2.close_smooth, distance=1, width=2, prominence=atr*0.5) #evtl kleiner 0.5
    troughs_idx, _ = find_peaks(-1*df2.close_smooth, distance=1, width=2, prominence=atr*0.5)

    # Trend über Peaks/Troughs
    if len(peaks_idx) > 0 and len(troughs_idx) > 0:
        if peaks_idx[-1] > troughs_idx[-1]:
            trend = "downtrend"
        else:
            trend = "uptrend"
    else:
        # Falls keine klaren Peaks gefunden wurden → Slope nutzen
        slope = df2.close_smooth.diff().iloc[-5:].mean()
        trend = "downtrend" if slope < 0 else "uptrend"

    trend_dict[period] = trend
    return trend


get_trend_fast('m15')

def set_trend():

    global pause_trading, periods_dict, trend_dict

    for k,v in trend_dict.items():
        get_trend_fast(k) 
        print(k)
    
    for k,v in reversed(trend_dict.items()):

        if v == 'uptrend':
            print(k,v)
            periods_dict[symbol] = [k]
            pause_trading = 0
            print(f"Pause Trading: {pause_trading}")
            break
        elif v == 'downtrend':
            periods_dict[symbol] = [k] #['m15']
            pause_trading = 1
            print(f"Pause Trading: {pause_trading}")

set_trend()

pause_trading, trend_dict, periods_dict, symbols[0]

set_trend()

trend_dict[periods_dict[symbol][0]]

periods_dict[symbol], pause_trading

def get_mt5_data(symbol=symbol, timeframe=mt.TIMEFRAME_M15, n_bars=500):
    rates = mt.copy_rates_from_pos(symbol, timeframe, 0, n_bars)
    df = pd.DataFrame(rates)
    df["time"] = pd.to_datetime(df["time"], unit="s")
    return df

def get_m5_trade_signal(symbol, atr_mult=1.5):
    global trend_dict, periods_dict

    # Hole die letzten M5-Daten
    df = get_rates('m5').iloc[-200:]

    # Smoothen
    df["close_smooth_std"] = savgol_filter(df.close, 25, 5)
    df["close_smooth_fast"] = savgol_filter(df.close, 15, 5)

    atr = df.atr.iloc[-1]

    # --- Standard-Trend ---
    peaks_std, _ = find_peaks(df.close_smooth_std, distance=1, width=2, prominence=atr)
    troughs_std, _ = find_peaks(-df.close_smooth_std, distance=1, width=2, prominence=atr)

    if len(peaks_std) > 0 and len(troughs_std) > 0:
        if peaks_std[-1] > troughs_std[-1]:
            trend_standard = "downtrend"
        else:
            trend_standard = "uptrend"
    else:
        trend_standard = "neutral"

    # --- Fast-Trend ---
    peaks_fast, _ = find_peaks(df.close_smooth_fast, distance=1, width=2, prominence=atr*0.5)
    troughs_fast, _ = find_peaks(-df.close_smooth_fast, distance=1, width=2, prominence=atr*0.5)

    if len(peaks_fast) > 0 and len(troughs_fast) > 0:
        if peaks_fast[-1] > troughs_fast[-1]:
            trend_fast = "downtrend"
        else:
            trend_fast = "uptrend"
    else:
        slope = df.close_smooth_fast.diff().iloc[-5:].mean()
        trend_fast = "downtrend" if slope < 0 else "uptrend"

    # --- Kombiniertes Signal ---
    signal = 0  # 0 = neutral, 1 = long, -1 = short
    stop_loss = None

    if trend_standard == "uptrend" and trend_fast == "uptrend":
        signal = 1
        stop_loss = df.close.iloc[-1] - atr_mult * atr
    elif trend_standard == "downtrend" and trend_fast == "downtrend":
        signal = -1
        stop_loss = df.close.iloc[-1] + atr_mult * atr

    # Speichern
    trend_dict['m5'] = {"standard": trend_standard, "fast": trend_fast, "signal": signal}

    return {
        "signal": signal,
        "price": df.close.iloc[-1],
        "stop_loss": stop_loss,
        "trends": trend_dict['m15']
    }


def generate_signal(df = get_mt5_data(symbol=symbol, timeframe=timeframes_dict['m15']), confirm_window=3):
    """
    Berechnet drei Signalarten:
    - fast_signal: schnelle, aggressive Variante
    - standard_signal: konservative Basisstrategie
    - optimized_signal: zusätzliche Filter (ATR, ADX, strengerer RSI)
    """

    # Indikatoren
    df["ema21"] = df["close"].ewm(span=3).mean()
    df["ema50"] = df["close"].ewm(span=9).mean()
    df["rsi9"] = ta.rsi(df["close"], length=9)
    df["rsi14"] = ta.rsi(df["close"], length=14)
    df["trend"] = savgol_filter(df["close"], 25, 3)

    # Zusätzliche Filterindikatoren
    df["atr"] = ta.atr(df["high"], df["low"], df["close"], length=14)
    df["adx"] = ta.adx(df["high"], df["low"], df["close"], length=14)["ADX_14"]

    # Spalten für Signale
    df["fast_signal"] = 0
    df["standard_signal"] = 0
    df["optimized_signal"] = 0

    for i in range(1, len(df)):
        # -----------------------
        # FAST SIGNAL (früh/aggressiv)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["rsi9"].iloc[i] > 30
        ):
            df.at[i, "fast_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["rsi9"].iloc[i] < 60
        ):
            df.at[i, "fast_signal"] = -1

        # -----------------------
        # STANDARD SIGNAL (konservativ, ursprüngliche Logik)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] <= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] < 70
            and df["rsi9"].iloc[i] > 40
            and df["trend"].iloc[i] > df["trend"].iloc[i - 1]
        ):
            df.at[i, "standard_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] >= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] > 40
            and df["rsi9"].iloc[i] < 70
            and df["trend"].iloc[i] < df["trend"].iloc[i - 1]
        ):
            df.at[i, "standard_signal"] = -1

        # -----------------------
        # OPTIMIZED SIGNAL (mit ADX + ATR + strengerem RSI)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] <= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] < 65                          # strengerer Filter
            and df["rsi9"].iloc[i] > 45                           # Momentum klarer
            and df["adx"].iloc[i] > 20                            # Trendstärke vorhanden
            and df["atr"].iloc[i] > df["atr"].rolling(50).mean().iloc[i]  # Volatilität über Durchschnitt
        ):
            df.at[i, "optimized_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] >= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] > 35                          # strengerer Filter unten
            and df["rsi9"].iloc[i] < 55
            and df["adx"].iloc[i] > 20
            and df["atr"].iloc[i] > df["atr"].rolling(50).mean().iloc[i]
        ):
            df.at[i, "optimized_signal"] = -1

    return df


generate_signal()

pos = mt.positions_total()

if pos > 0:
    open_positions = mt.positions_get() 
    trail_factor = 0.5
    entry_price = open_positions[0].price_open
    tp_current = open_positions[0].tp
    current_price = open_positions[0].price_current
    profit = open_positions[0].profit

    profit = current_price - entry_price
    if profit > 0:
        new_tp = current_price - entry_price * trail_factor
    
    if profit < 0:
        new_tp = current_price - profit * trail_factor        

    if new_tp > current_price:
        #update tp from open pos
        new_tp
        print(f"Trailing +TP for BUY {symbol}: {new_tp} CP: {current_price}")
    if new_tp < current_price:
        #update tp from open pos
        new_tp
        print(f"Trailing -TP for BUY {symbol}: {new_tp} CP: {current_price}")

#open_positions


def update_trailing_sl_tp(pos, atr, rrr=2.0, atr_mult=1.5, max_retries=2):
    """
    Aktualisiert SL und TP für offene Positionen:
    - ATR-basiertes Trailing
    - Gewinn-stufenweises Nachziehen
    - Dynamische Anpassung mit Validierung
    - Fallback-Mechanismus bei RETCODE 1016
    """

    symbol = pos.symbol
    info = mt.symbol_info(symbol)
    digits = info.digits
    point = info.point
    stops_level = info.trade_stops_level * point  # Mindestabstand vom Broker

    entry_price = pos.price_open
    current_tick = mt.symbol_info_tick(symbol)
    bid, ask = current_tick.bid, current_tick.ask
    current_price = bid if pos.type == 0 else ask
    pos_type = pos.type  # 0 = BUY, 1 = SELL

    # Gewinn in ATR berechnen
    if pos_type == 0:  # LONG
        profit_atr = (current_price - entry_price) / atr
        base_sl = current_price - atr_mult * atr
        new_sl = max(pos.sl or 0, base_sl)

        if profit_atr > 2:
            new_sl = max(new_sl, entry_price + 1.5 * atr)
        elif profit_atr > 1:
            new_sl = max(new_sl, entry_price + 1.0 * atr)
        elif profit_atr > 0.5:
            new_sl = max(new_sl, entry_price + 0.5 * atr)

        new_tp = entry_price + (entry_price - new_sl) * rrr

        # Validierung BUY
        if new_sl >= bid - stops_level:
            new_sl = bid - stops_level
        if new_tp <= ask + stops_level:
            new_tp = ask + stops_level

    else:  # SHORT
        profit_atr = (entry_price - current_price) / atr
        base_sl = current_price + atr_mult * atr
        new_sl = min(pos.sl or 999999, base_sl)

        if profit_atr > 2:
            new_sl = min(new_sl, entry_price - 1.5 * atr)
        elif profit_atr > 1:
            new_sl = min(new_sl, entry_price - 1.0 * atr)
        elif profit_atr > 0.5:
            new_sl = min(new_sl, entry_price - 0.5 * atr)

        new_tp = entry_price - (new_sl - entry_price) * rrr

        # Validierung SELL
        if new_sl <= ask + stops_level:
            new_sl = ask + stops_level
        if new_tp >= bid - stops_level:
            new_tp = bid - stops_level

    # Runden auf gültige Stellen
    new_sl = round(new_sl, digits)
    new_tp = round(new_tp, digits)

    # --- Nur updaten, wenn sich Werte geändert haben ---
    if (pos.sl is None or abs(new_sl - pos.sl) > point) or \
       (pos.tp is None or abs(new_tp - pos.tp) > point):

        for attempt in range(max_retries):
            request = {
                "action": mt.TRADE_ACTION_SLTP,
                "symbol": symbol,
                "sl": new_sl,
                "tp": new_tp,
                "position": pos.ticket
            }
            result = mt.order_send(request)

            if result.retcode == mt.TRADE_RETCODE_DONE:
                print(f"🔄 Updated {symbol} | SL: {new_sl:.5f} | TP: {new_tp:.5f}")
                break
            elif result.retcode == mt.TRADE_RETCODE_INVALID_STOPS:
                # Fallback: Stops korrigieren
                print(f"⚠️ RETCODE 1016 (Invalid stops) – Versuch {attempt+1}/{max_retries}")
                adjust = 2 * stops_level  # mehr Abstand
                if pos_type == 0:  # BUY
                    new_sl = bid - adjust
                    new_tp = ask + adjust
                else:  # SELL
                    new_sl = ask + adjust
                    new_tp = bid - adjust

                new_sl = round(new_sl, digits)
                new_tp = round(new_tp, digits)
                continue  # retry
            else:
                print(f"❌ SL/TP Update Fehler: {result.retcode} ({result.comment})")
                break

    return {"new_sl": new_sl, "new_tp": new_tp, "profit_atr": profit_atr}


def manual_update_trailing_sl_tp(atr_mult=1.5, slope_factor=1.5):
    # --- Hole die letzten M5-Daten ---
    df = get_rates('m5').iloc[-200:]

    # --- ATR Berechnung ---
    df["hl"] = df["high"] - df["low"]
    df["hc"] = (df["high"] - df["close"].shift()).abs()
    df["lc"] = (df["low"] - df["close"].shift()).abs()
    df["tr"] = df[["hl","hc","lc"]].max(axis=1)
    df["atr"] = df["tr"].rolling(14).mean()
    atr = df["atr"].iloc[-1]

    current_price = df["close"].iloc[-1]

    # --- Trendrichtung per Linear Regression ---
    def linreg_slope(series):
        X = np.arange(len(series)).reshape(-1, 1)
        y = series.values.reshape(-1, 1)
        model = LinearRegression().fit(X, y)
        return model.coef_[0][0]

    slope_long = linreg_slope(df["close"].iloc[-50:])  # 50 Balken (~4h)
    slope_short = linreg_slope(df["close"].iloc[-15:]) # 15 Balken (~1h)

    # --- Dynamischer Seitwärtsfilter ---
    slope_threshold = slope_factor * atr / df["close"].iloc[-1]

    # --- Dynamische RRR-Berechnung ---
    atr_norm = atr / current_price          # relative Volatilität
    slope_strength = abs(slope_short)       # Trendstärke aus Regression

    rrr_base = 1.2
    rrr_from_vol = atr_norm * 1500          # skaliert ATR-Einfluss
    rrr_from_slope = slope_strength / slope_threshold  # Slope-Einfluss

    rrr = rrr_base + rrr_from_vol + rrr_from_slope
    rrr = max(1.2, min(rrr, 3.0))           # Begrenzung


    open_positions = mt.positions_get(symbol=symbol)
    for pos in open_positions:
        atr_value = df["atr"].iloc[-1]
        update_trailing_sl_tp(pos, atr=atr_value, rrr=rrr, atr_mult=atr_mult)
       

manual_update_trailing_sl_tp()


def get_m5_trade_signals(symbol=symbol, atr_mult=1.5, base_rrr=2.0, atr_min=0.0005, slope_factor=1.5, place_orders=False):
    """
    M5/M15 analysis-only signal generator. By default it does NOT place orders.
    Set place_orders=True to allow execution (not recommended when called from top-down analysis).
    Returns a dict with signal, price, stop_loss, take_profit, Risk Reward, atr and slopes.
    """
    # use m15 data for setup if available, otherwise m5
    df = get_rates('m15').iloc[-200:]
    if df is None or df.empty or len(df) < 30:
        return {"signal": 0, "reason": "not enough data"}

    # ATR fallback (ensure column exists)
    if 'atr' not in df.columns or df['atr'].isnull().all():
        df['hl'] = df['high'] - df['low']
        df['hc'] = (df['high'] - df['close'].shift()).abs()
        df['lc'] = (df['low'] - df['close'].shift()).abs()
        df['tr'] = df[['hl','hc','lc']].max(axis=1)
        df['atr'] = df['tr'].rolling(14, min_periods=1).mean()
    atr = float(df['atr'].iloc[-1])

    if atr < atr_min:
        return {"signal": 0, "reason": "ATR too low → sideways", "atr": atr}

    slope_long = linreg_slope(df['close'].iloc[-50:])
    slope_short = linreg_slope(df['close'].iloc[-15:])

    slope_threshold = slope_factor * atr / float(df['close'].iloc[-1])

    if abs(slope_long) < slope_threshold and abs(slope_short) < slope_threshold:
        return {"signal": 0, "reason": "Trend flat → sideways", "atr": atr,
                "slope_long": slope_long, "slope_short": slope_short}

    trend_standard = 'uptrend' if slope_long > 0 else 'downtrend'
    trend_fast = 'uptrend' if slope_short > 0 else 'downtrend'

    current_price = float(df['close'].iloc[-1])

    # adaptive RRR
    atr_norm = atr / current_price
    slope_strength = abs(slope_short)
    rrr_base = 1.2
    rrr_from_vol = atr_norm * 1500
    rrr_from_slope = slope_strength / (slope_threshold if slope_threshold!=0 else 1e-9)
    rrr = rrr_base + rrr_from_vol + rrr_from_slope
    rrr = max(1.2, min(rrr, 3.0))

    stop_loss = None
    take_profit = None
    signal = 0

    if trend_standard == 'uptrend' and trend_fast == 'uptrend':
        signal = 1
        stop_loss = current_price - atr_mult * atr
        take_profit = current_price + atr_mult * atr * rrr
        if place_orders:
            market_order(symbol, volume_dict.get(symbol, 0.0), 'buy', stoploss=stop_loss, take_profit=take_profit)

    elif trend_standard == 'downtrend' and trend_fast == 'downtrend':
        signal = -1
        stop_loss = current_price + atr_mult * atr
        take_profit = current_price - atr_mult * atr * rrr
        if place_orders:
            market_order(symbol, volume_dict.get(symbol, 0.0), 'sell', stoploss=stop_loss, take_profit=take_profit)

    return {
        'signal': signal,
        'price': current_price,
        'stop_loss': stop_loss,
        'take_profit': take_profit,
        'Risk Reward': rrr,
        'atr': atr,
        'slope_long': slope_long,
        'slope_short': slope_short,
        'trend_standard': trend_standard,
        'trend_fast': trend_fast
    }


def execute_m5_trade(symbol=symbol, atr_mult=1.5, base_rrr=2.0):
    """Execute trade only if Top-Down approves. Uses get_top_down_signal and get_m5_trade_signals (analysis).
    """
    global volume_dict, pause_trading
    if pause_trading == 1:
        print('Trading paused, skipping execution')
        return None

    top = get_top_down_signal(symbol)
    if not top.get('setup_ready') or top.get('entry_signal',0) == 0:
        print('Top-Down not ready or no entry signal, aborting execution')
        return None

    # get m5 analysis (no auto-order)
    m5 = get_m5_trade_signals(symbol=symbol, atr_mult=atr_mult, base_rrr=base_rrr, place_orders=False)
    if m5.get('signal',0) == 0:
        print('M5 analysis returned no entry, abort')
        return None

    # execute using the M5-calculated SL/TP
    direction = 'buy' if m5['signal'] == 1 else 'sell'
    print(f'Executing {direction} {symbol} @ {m5["price"]} SL={m5["stop_loss"]} TP={m5["take_profit"]}')
    market_order(symbol, volume_dict.get(symbol,0.0), direction, stoploss=m5['stop_loss'], take_profit=m5['take_profit'])

    # after entry, run dynamic trailing update once
    dynamic_update_sl_tp(symbol, atr_mult=atr_mult, rrr_factor=m5['Risk Reward'], check_tf='m5')

    return {'executed': True, 'direction': direction, 'm5': m5, 'topdown': top}


execute_m5_trade()

from apscheduler.schedulers.background import BackgroundScheduler
import time


scheduler = BackgroundScheduler()
#scheduler.add_job(main, 'date', run_date='2025-03-07 14:29:50')

#scheduler.add_job(decide_order, 'interval', minutes=1) #intervall
#scheduler.add_job(set_symbol, 'interval', minutes=30)
#scheduler.add_job(set_trend, 'interval', minutes=1)

#scheduler.add_job(decide_order, 'interval', minutes=1)
#scheduler.add_job(decide_order, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*') #cron
#scheduler.add_job(get_buy_sell_signal, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
#scheduler.add_job(get_m5_trade_signals, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
scheduler.add_job(execute_m5_trade, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
scheduler.add_job(manual_update_trailing_sl_tp, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*') #cron

#scheduler.add_job(export_marketview, 'cron', year="*", month='*', day_of_week='mon, tue, wed; thu, fri', hour='8-22', minute=00)

#scheduler.add_job(pause_trading, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour=22, minute=00) #cron
scheduler.start()


scheduler.get_jobs()

scheduler.remove_all_jobs()

scheduler.shutdown()

In [2]:

#%pip install talib

In [3]:

#!pip install ta
from ta.trend import ADXIndicator, EMAIndicator
from ta.momentum import RSIIndicator
from talib import CDLHAMMER, CDLSHOOTINGSTAR

In [4]:

import pandas as pd
import matplotlib.pyplot as plt
import mplfinance as mpf
import keyring as kr
import MetaTrader5 as mt
import requests
import re
from time import sleep
import sqlite3 as db
#import matplotlib.pyplot as plt
import pandas_ta as ta
import numpy as np

from sklearn.linear_model import LinearRegression
from scipy.signal import savgol_filter
from scipy.signal import find_peaks

In [5]:

# login to your Trading Account - sign up in the description
mt.initialize()
    
login = 10800246
server = 'VantageInternational-Demo'
password = kr.get_password(server, str(login))


mt.login(login, password, server)

Out [5]:

True

In [6]:

project = "trading-" + server[-4::1]
project = project.lower()
project

Out [6]:

'trading-demo'

Set symbol and volume

In [7]:

pause_trading = 0

In [8]:

symbols = ['XAUUSD']
#symbols = ['BTCUSD']

#'BTCUSD', 'ETHUSD', 
            #'XRPUSD', , 'EURNZD', 'EURUSD'

In [9]:

volume_dict = {
    'BTCUSD' : 0.1,
    'BTCUSD_short' : 0.1,

    'ETHUSD' : 1.0,
    'ETHUSD_short' : 1.0,
    
    'XRPUSD': 0.1,
    'XRPUSD_short': 0.1,

    'XAUUSD': 0.1,
    'XAUUSD_short': 0.1,

    'EURUSD': 0.1,
    'EURUSD_short': 0.1,

    'EURNZD': 0.1,
    'EURNZD_short': 0.1,

}

In [10]:

periods_dict = {
    'BTCUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],
    
    'ETHUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],

    
    'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
    
    'XAUUSD':  [ 'm15'],

    
    'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],


    'EURNZD': ['m5', 'm2', 'm1'],

}

In [11]:

def get_symbol():
    global symbols, pause_trading, periods_dict
    pricemovement = {}

    for s in symbols:
        items = mt.symbol_info(s)
        pricemovement[s] = round(items.price_change,2)
        #print(s, round(items.price_change,2))

    #percentage = pricemovement[max(pricemovement, key=pricemovement.get)]
    #symbol = max(pricemovement, key=pricemovement.get)
    #volume = volume_dict[symbol]

    sorted_pricemovement = sorted(pricemovement.items(), key=lambda x:x[1], reverse=True)
    converted_dict = dict(sorted_pricemovement)

    print(converted_dict)

    percentage = converted_dict[max(converted_dict, key=converted_dict.get)]
    symbol = max(converted_dict, key=converted_dict.get)
    volume = volume_dict[symbol]


    print(symbol, percentage, volume)
    # if percentage > 0: # and percentage < 0.8:
    #    periods_dict[symbol] =  ['m15', 'm5', 'm2', 'm1']
    # elif percentage > 0.8:
    #     periods_dict[symbol] = ['h1', 'm30', 'm15', 'm5', 'm1']

    #

    #print(periods_dict)

    # if percentage > 0 and mt.positions_total() == 0:
    #     pause_trading = 0 #0 no puase
    #     return symbol, percentage, volume, periods_dict
    # elif percentage < 0.1 and mt.positions_total() == 0:
    #     print("aktuell kein neues Symbol, pause Trading")
    #     pause_trading = 1 #1 pause
    #     return None

    return symbol, percentage, volume, periods_dict

In [12]:

get_symbol()

Out [12]:

{'XAUUSD': 1.08}
XAUUSD 1.08 0.1

('XAUUSD',
 1.08,
 0.1,
 {'BTCUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'ETHUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XAUUSD': ['m15'],
  'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'EURNZD': ['m5', 'm2', 'm1']})

In [13]:

def set_symbol():
    global symbol, volume, volume_dict
    symb = get_symbol()
    if symb != None:
        symbol = symb[0]
        volume = volume_dict[symb[0]]

set volume manuell

In [14]:

get_symbol()

Out [14]:

{'XAUUSD': 1.08}
XAUUSD 1.08 0.1

('XAUUSD',
 1.08,
 0.1,
 {'BTCUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'ETHUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XAUUSD': ['m15'],
  'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'EURNZD': ['m5', 'm2', 'm1']})

In [15]:

set_symbol()

{'XAUUSD': 1.08}
XAUUSD 1.08 0.1

In [16]:

symbol, volume, pause_trading

Out [16]:

('XAUUSD', 0.1, 0)

Functions to place Orders on Market

In [17]:

pos = mt.positions_get()
for i in pos:
    #if i.comment == 'Retracement Bot':
    #    print(i.ticket)

    if bool(re.search('^BuyStop[0-9]{2}', i.comment)):
        print(i.ticket)

mt.positions_total()

Out [17]:

In [18]:

strategy_name = 'Retracement Bot'
pos = mt.positions_get()
for p in pos:
    if p.comment == strategy_name:
        print(p.comment)
print(pos)

Retracement Bot
(TradePosition(ticket=382221755, time=1756930501, time_msc=1756930501001, time_update=1756930501, time_update_msc=1756930501001, type=0, magic=30, identifier=382221755, reason=3, volume=0.1, price_open=3572.38, sl=3569.69, tp=3580.45, price_current=3572.1, swap=0.0, profit=-2.8, symbol='XAUUSD', comment='Retracement Bot', external_id=''),)

Market Order Function

In [19]:

def market_order(symbol, volume, order_type, deviation=20, magic=30, stoploss=0.0, take_profit=0.0,
                 strategy_name='Retracement Bot'):

    global project, pause_trading

    project_id_dict = {
        'trading-demo': 'a3f3ae',
        'trading-live': '747543'
    }

    order_type_dict = {
        'buy': mt.ORDER_TYPE_BUY,
        'sell': mt.ORDER_TYPE_SELL
    }

    price_dict = {
        'buy': mt.symbol_info_tick(symbol).ask,
        'sell': mt.symbol_info_tick(symbol).bid
    }

    buypos = []

    pos = mt.positions_get()
    for p in pos:
        if p.comment == strategy_name:
            buypos.append('true')
    
    activepos = buypos.count('true')

    if order_type == 'buy' and activepos == 0 and pause_trading == 0: # and mt.positions_total() == 0:
        
        request = {
            "action": mt.TRADE_ACTION_DEAL,
            "symbol": symbol,
            "volume": volume,  # FLOAT
            "type": order_type_dict[order_type],
            "price": price_dict[order_type],
            "sl":  stoploss,  # FLOAT
            "tp":  take_profit,  # FLOAT
            "deviation": deviation,  # INTERGER
            "magic": magic,  # INTERGER
            "comment": strategy_name,
            "type_time": mt.ORDER_TIME_GTC,
            "type_filling": mt.ORDER_FILLING_IOC,  # mt.ORDER_FILLING_FOK if IOC does not work
        }

        requests.post('https://api.mynotifier.app', {
            "apiKey": 'beafb52e-3cb6-477a-92ef-2f10bff50e20',
            "message": "Es wrude ein Handel eröffnet!",
            "description": "Bitte kontrolliere die Position",
            "type": "info",#"info", # info, error, warning or success
            "project": project_id_dict[project]
        })

        order_result = mt.order_send(request)
        #return (order_result)
    
    elif order_type == 'sell' and mt.positions_total() > 0:
        pos = mt.positions_get()
        for p in pos:
            if p.comment == strategy_name:
                # while schleife ?
                positions = mt.positions_get()
                ticket = p.ticket
                request = {
                    "action": mt.TRADE_ACTION_DEAL,
                    "symbol": symbol,
                    "volume": volume,  # FLOAT
                    "type": order_type_dict[order_type],
                    "price": price_dict[order_type],
                    "position": ticket,
                    "sl": stoploss,  # FLOAT
                    "tp": take_profit,  # FLOAT
                    "deviation": deviation,  # INTERGER
                    "magic": magic,  # INTERGER
                    "comment": strategy_name,
                    "type_time": mt.ORDER_TIME_GTC,
                    "type_filling": mt.ORDER_FILLING_IOC,  # mt.ORDER_FILLING_FOK if IOC does not work
                }

                requests.post('https://api.mynotifier.app', {
                    "apiKey": 'beafb52e-3cb6-477a-92ef-2f10bff50e20',
                    "message": "Es wrude ein Handel geschlossen!",
                    "description": "Bitte prüfe die Position",
                    "type": "info",#"info", # info, error, warning or success
                    "project": project_id_dict[project]
                })

                order_result = mt.order_send(request)
                #return (order_result)

Trend Detection

timefame & trend dictionary

In [20]:

debug = False

In [21]:

trend_dict = {
    #'m5': '',
    #'m10': '',
    'm15': '',
    #'m30': '',
    #'h4': '',
}

In [22]:

timeframes_dict = {
    'm1': mt.TIMEFRAME_M1,
    'm2': mt.TIMEFRAME_M2,
    'm3': mt.TIMEFRAME_M3,
    'm5': mt.TIMEFRAME_M5,
    'm15': mt.TIMEFRAME_M15,
    'm20': mt.TIMEFRAME_M20,
    'm30': mt.TIMEFRAME_M30,
    'h1': mt.TIMEFRAME_H1,
    'h4': mt.TIMEFRAME_H4,
    'd1': mt.TIMEFRAME_D1,
}


# timeframes = {
#         'm1': mt.TIMEFRAME_M1,
#         'm2': mt.TIMEFRAME_M2,
#         'm3': mt.TIMEFRAME_M3,
#         'm5': mt.TIMEFRAME_M5,
#         'm15': mt.TIMEFRAME_M15,
#         'm20': mt.TIMEFRAME_M20,
#         'm30': mt.TIMEFRAME_M30,
#         'h1': mt.TIMEFRAME_H1,
#     }

In [23]:

print(trend_dict)

{'m15': ''}

In [24]:

def get_rates(periode, bars=300):
    #global symbol

    # OHLC abrufen
    ohlc = mt.copy_rates_from_pos(symbol, timeframes_dict[periode], 0, bars)
    df = pd.DataFrame(ohlc)
    df['time'] = pd.to_datetime(df['time'], unit='s')

    # Umwandeln in float
    df["open"] = df["open"].astype(float)
    df["high"] = df["high"].astype(float)
    df["low"] = df["low"].astype(float)
    df["close"] = df["close"].astype(float)

    # ATR berechnen (klassisch 14)
    df["atr"] = ta.atr(high=df["high"], low=df["low"], close=df["close"], length=14)

    # leichte Glättung (optional, um Rauschen zu reduzieren)
    df["atr"] = df["atr"].rolling(window=5).mean()

    # Index setzen
    df.set_index("time", inplace=True)

    # NaNs entfernen (anfangs durch ATR-Berechnung)
    df = df.dropna()

    return df

In [25]:

get_rates('h4', 200)

Out [25]:

	open	high	low	close	tick_volume	spread	real_volume	atr
time
2025-07-23 16:00:00	3419.98	3420.52	3381.47	3387.38	89692	19	0	13.861980
2025-07-23 20:00:00	3387.33	3395.94	3385.74	3386.78	49534	19	0	14.128410
2025-07-24 00:00:00	3388.01	3393.16	3386.48	3391.44	18496	19	0	14.305667
2025-07-24 04:00:00	3391.44	3393.36	3374.71	3382.57	56315	19	0	14.547405
2025-07-24 08:00:00	3382.56	3382.92	3365.82	3369.68	54663	19	0	14.818019
...	...	...	...	...	...	...	...	...
2025-09-03 04:00:00	3536.07	3545.89	3529.36	3536.81	59697	20	0	18.731740
2025-09-03 08:00:00	3536.82	3541.19	3526.93	3539.99	58476	20	0	19.107330
2025-09-03 12:00:00	3540.04	3551.43	3532.29	3550.56	63000	20	0	19.007235
2025-09-03 16:00:00	3550.60	3572.57	3549.38	3572.43	87375	20	0	18.985718
2025-09-03 20:00:00	3572.45	3572.98	3570.24	3572.12	4428	20	0	18.713310

182 rows × 8 columns

Parameter	Wirkung
`distance`	Wie nah beieinander Peaks liegen dürfen
`width`	Wie breit/flach ein Peak sein muss
`prominence`	Wie stark auffällig ein Peak sein muss

In [26]:

def get_trend(timeframe="h4", lookback=150):
    """
    Bestimme Trendrichtung per Linear Regression.
    Liefert:
      - trend: "uptrend" / "downtrend" / "sideways"
      - slope: numerischer Wert der Regression
      - atr: ATR des Zeitraums
      - slope_threshold: dynamische Schwelle für Seitwärtsbewegungen
    """
    df = get_rates(timeframe, lookback)
    if df.empty:
        return {"trend": "sideways", "slope": 0, "atr": 0, "slope_threshold": 0}

    # Linear Regression
    X = np.arange(len(df)).reshape(-1, 1)
    y = df["close"].values.reshape(-1, 1)
    slope = LinearRegression().fit(X, y).coef_[0][0]

    # ATR
    df["hl"] = df["high"] - df["low"]
    df["hc"] = (df["high"] - df["close"].shift()).abs()
    df["lc"] = (df["low"] - df["close"].shift()).abs()
    df["tr"] = df[["hl","hc","lc"]].max(axis=1)
    atr = df["tr"].rolling(14).mean().iloc[-1]

    slope_threshold = (atr / df["close"].iloc[-1]) * 1.2

    if abs(slope) < slope_threshold:
        trend = "sideways"
    else:
        trend = "uptrend" if slope > 0 else "downtrend"

    return {
        "trend": trend,
        "slope": slope,
        "atr": atr,
        "slope_threshold": slope_threshold
    }

In [27]:

def get_top_down_signal(symbol=symbol):
    """
    Top-Down Ansatz:
      - D1 / H4: Trend bestimmen
      - H1 / M30: Setup identifizieren
      - M15 / M5: Einstiege optimieren (Signale ohne Tradeausführung)
    """
    global trend_dict

    # --- Höhere Timeframes ---
    d1_trend_info = get_trend("d1", lookback=200)
    h4_trend_info = get_trend("h4", lookback=150)

    # --- Mittlere Timeframes für Setups ---
    h1_trend_info = get_trend("h1", lookback=100)
    m30_trend_info = get_trend("m30", lookback=60)

    # --- Niedrigere Timeframes für Einstiege (nur Signal, kein Trade) ---
    m15_trend_info = get_trend("m15", lookback=50)
    m5_trend_info = get_trend("m5", lookback=20)

    # --- Konsolidierte Trendanalyse ---
    top_down_trend = "sideways"
    if d1_trend_info["trend"] == h4_trend_info["trend"]:
        top_down_trend = d1_trend_info["trend"]

    # --- Setup-Bedingungen ---
    setup_ready = False
    if top_down_trend != "sideways":
        if h1_trend_info["trend"] == top_down_trend and m30_trend_info["trend"] == top_down_trend:
            setup_ready = True

    # --- Einstiegsberechnung ---
    entry_signal = 0
    if setup_ready:
        if m15_trend_info["trend"] == top_down_trend and m5_trend_info["trend"] == top_down_trend:
            entry_signal = 1 if top_down_trend == "uptrend" else -1

    # --- Speichern ---
    trend_dict["top_down"] = {
        "D1": d1_trend_info,
        "H4": h4_trend_info,
        "H1": h1_trend_info,
        "M30": m30_trend_info,
        "M15": m15_trend_info,
        "M5": m5_trend_info,
        "top_down_trend": top_down_trend,
        "setup_ready": setup_ready,
        "entry_signal": entry_signal
    }

    return trend_dict["top_down"]

In [28]:

get_trend("h1")

Out [28]:

{'trend': 'uptrend',
 'slope': 1.4477744934856227,
 'atr': 9.130714285714314,
 'slope_threshold': 0.0030673261656543383}

In [56]:

get_top_down_signal(symbol)

Out [56]:

{'D1': {'trend': 'uptrend',
  'slope': 4.621913228515892,
  'atr': 39.36499999999988,
  'slope_threshold': 0.013270331238569827},
 'H4': {'trend': 'uptrend',
  'slope': 0.8813408347377812,
  'atr': 20.499285714285698,
  'slope_threshold': 0.006910512170269389},
 'H1': {'trend': 'uptrend',
  'slope': 1.8054590176423853,
  'atr': 9.649999999999993,
  'slope_threshold': 0.0032531105411456656},
 'M30': {'trend': 'uptrend',
  'slope': 1.0648326715825298,
  'atr': 6.73500000000003,
  'slope_threshold': 0.0022704351807892403},
 'M15': {'trend': 'uptrend',
  'slope': 0.3136986803519024,
  'atr': 4.114285714285676,
  'slope_threshold': 0.0013869664483344836},
 'M5': {'trend': 'downtrend',
  'slope': -0.909999999999854,
  'atr': nan,
  'slope_threshold': nan},
 'top_down_trend': 'uptrend',
 'setup_ready': True,
 'entry_signal': 0}

In [30]:

get_trend()

Out [30]:

{'trend': 'uptrend',
 'slope': 0.8855957903085263,
 'atr': 19.30285714285713,
 'slope_threshold': 0.006484504599909453}

In [31]:

def get_trend_fast(period):
    global trend_dict, periods_dict, symbol

    df2 = get_rates(period).iloc[-200:]
    df2["close_smooth"] = savgol_filter(df2.close, 15, 5)  # kleinere Glättung

    atr = df2.atr.iloc[-1]

    # Weniger strenge Peak-Erkennung
    peaks_idx, _ = find_peaks(df2.close_smooth, distance=1, width=2, prominence=atr*0.5) #evtl kleiner 0.5
    troughs_idx, _ = find_peaks(-1*df2.close_smooth, distance=1, width=2, prominence=atr*0.5)

    # Trend über Peaks/Troughs
    if len(peaks_idx) > 0 and len(troughs_idx) > 0:
        if peaks_idx[-1] > troughs_idx[-1]:
            trend = "downtrend"
        else:
            trend = "uptrend"
    else:
        # Falls keine klaren Peaks gefunden wurden → Slope nutzen
        slope = df2.close_smooth.diff().iloc[-5:].mean()
        trend = "downtrend" if slope < 0 else "uptrend"

    trend_dict[period] = trend
    return trend

In [32]:

get_trend_fast('m15')

Out [32]:

'uptrend'

In [33]:

def set_trend():

    global pause_trading, periods_dict, trend_dict

    for k,v in trend_dict.items():
        get_trend_fast(k) 
        print(k)
    
    for k,v in reversed(trend_dict.items()):

        if v == 'uptrend':
            print(k,v)
            periods_dict[symbol] = [k]
            pause_trading = 0
            print(f"Pause Trading: {pause_trading}")
            break
        elif v == 'downtrend':
            periods_dict[symbol] = [k] #['m15']
            pause_trading = 1
            print(f"Pause Trading: {pause_trading}")

In [34]:

set_trend()

m15

[1;31m---------------------------------------------------------------------------[0m
[1;31mKeyError[0m                                  Traceback (most recent call last)
Cell [1;32mIn[34], line 1[0m
[1;32m----> 1[0m set_trend()

Cell [1;32mIn[33], line 6[0m, in [0;36mset_trend[1;34m()[0m
[0;32m      3[0m [38;5;28;01mglobal[39;00m pause_trading, periods_dict, trend_dict
[0;32m      5[0m [38;5;28;01mfor[39;00m k,v [38;5;129;01min[39;00m trend_dict[38;5;241m.[39mitems():
[1;32m----> 6[0m     get_trend_fast(k) 
[0;32m      7[0m     [38;5;28mprint[39m(k)
[0;32m      9[0m [38;5;28;01mfor[39;00m k,v [38;5;129;01min[39;00m [38;5;28mreversed[39m(trend_dict[38;5;241m.[39mitems()):

Cell [1;32mIn[31], line 4[0m, in [0;36mget_trend_fast[1;34m(period)[0m
[0;32m      1[0m [38;5;28;01mdef[39;00m [38;5;21mget_trend_fast[39m(period):
[0;32m      2[0m     [38;5;28;01mglobal[39;00m trend_dict, periods_dict, symbol
[1;32m----> 4[0m     df2 [38;5;241m=[39m get_rates(period)[38;5;241m.[39miloc[[38;5;241m-[39m[38;5;241m200[39m:]
[0;32m      5[0m     df2[[38;5;124m"[39m[38;5;124mclose_smooth[39m[38;5;124m"[39m] [38;5;241m=[39m savgol_filter(df2[38;5;241m.[39mclose, [38;5;241m15[39m, [38;5;241m5[39m)  [38;5;66;03m# kleinere Glättung[39;00m
[0;32m      7[0m     atr [38;5;241m=[39m df2[38;5;241m.[39matr[38;5;241m.[39miloc[[38;5;241m-[39m[38;5;241m1[39m]

Cell [1;32mIn[24], line 5[0m, in [0;36mget_rates[1;34m(periode, bars)[0m
[0;32m      1[0m [38;5;28;01mdef[39;00m [38;5;21mget_rates[39m(periode, bars[38;5;241m=[39m[38;5;241m300[39m):
[0;32m      2[0m     [38;5;66;03m#global symbol[39;00m
[0;32m      3[0m 
[0;32m      4[0m     [38;5;66;03m# OHLC abrufen[39;00m
[1;32m----> 5[0m     ohlc [38;5;241m=[39m mt[38;5;241m.[39mcopy_rates_from_pos(symbol, timeframes_dict[periode], [38;5;241m0[39m, bars)
[0;32m      6[0m     df [38;5;241m=[39m pd[38;5;241m.[39mDataFrame(ohlc)
[0;32m      7[0m     df[[38;5;124m'[39m[38;5;124mtime[39m[38;5;124m'[39m] [38;5;241m=[39m pd[38;5;241m.[39mto_datetime(df[[38;5;124m'[39m[38;5;124mtime[39m[38;5;124m'[39m], unit[38;5;241m=[39m[38;5;124m'[39m[38;5;124ms[39m[38;5;124m'[39m)

[1;31mKeyError[0m: 'top_down'

Set Trend manually

In [ ]:

pause_trading, trend_dict, periods_dict, symbols[0]

(0,
 {'m15': 'uptrend',
  'top_down': {'D1': {'trend': 'uptrend',
    'slope': 4.621913228515892,
    'atr': 39.36499999999988,
    'slope_threshold': 0.013270331238569827},
   'H4': {'trend': 'uptrend',
    'slope': 0.8813408347377812,
    'atr': 20.499285714285698,
    'slope_threshold': 0.006910512170269389},
   'H1': {'trend': 'uptrend',
    'slope': 1.8054590176423853,
    'atr': 9.649999999999993,
    'slope_threshold': 0.0032531105411456656},
   'M30': {'trend': 'uptrend',
    'slope': 1.0648326715825298,
    'atr': 6.73500000000003,
    'slope_threshold': 0.0022704351807892403},
   'M15': {'trend': 'uptrend',
    'slope': 0.3136986803519024,
    'atr': 4.114285714285676,
    'slope_threshold': 0.0013869664483344836},
   'M5': {'trend': 'downtrend',
    'slope': -0.909999999999854,
    'atr': nan,
    'slope_threshold': nan},
   'top_down_trend': 'uptrend',
   'setup_ready': True,
   'entry_signal': 0},
  'm5': {'standard': 'downtrend', 'fast': 'downtrend', 'signal': -1}},
 {'BTCUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'ETHUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'XAUUSD': ['m15'],
  'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],
  'EURNZD': ['m5', 'm2', 'm1']},
 'XAUUSD')

Kein Top-Down-Setup vorhanden. Kein Trade.

In [36]:

set_trend()

m15

[1;31m---------------------------------------------------------------------------[0m
[1;31mKeyError[0m                                  Traceback (most recent call last)
Cell [1;32mIn[36], line 1[0m
[1;32m----> 1[0m set_trend()

Cell [1;32mIn[33], line 6[0m, in [0;36mset_trend[1;34m()[0m
[0;32m      3[0m [38;5;28;01mglobal[39;00m pause_trading, periods_dict, trend_dict
[0;32m      5[0m [38;5;28;01mfor[39;00m k,v [38;5;129;01min[39;00m trend_dict[38;5;241m.[39mitems():
[1;32m----> 6[0m     get_trend_fast(k) 
[0;32m      7[0m     [38;5;28mprint[39m(k)
[0;32m      9[0m [38;5;28;01mfor[39;00m k,v [38;5;129;01min[39;00m [38;5;28mreversed[39m(trend_dict[38;5;241m.[39mitems()):

Cell [1;32mIn[31], line 4[0m, in [0;36mget_trend_fast[1;34m(period)[0m
[0;32m      1[0m [38;5;28;01mdef[39;00m [38;5;21mget_trend_fast[39m(period):
[0;32m      2[0m     [38;5;28;01mglobal[39;00m trend_dict, periods_dict, symbol
[1;32m----> 4[0m     df2 [38;5;241m=[39m get_rates(period)[38;5;241m.[39miloc[[38;5;241m-[39m[38;5;241m200[39m:]
[0;32m      5[0m     df2[[38;5;124m"[39m[38;5;124mclose_smooth[39m[38;5;124m"[39m] [38;5;241m=[39m savgol_filter(df2[38;5;241m.[39mclose, [38;5;241m15[39m, [38;5;241m5[39m)  [38;5;66;03m# kleinere Glättung[39;00m
[0;32m      7[0m     atr [38;5;241m=[39m df2[38;5;241m.[39matr[38;5;241m.[39miloc[[38;5;241m-[39m[38;5;241m1[39m]

Cell [1;32mIn[24], line 5[0m, in [0;36mget_rates[1;34m(periode, bars)[0m
[0;32m      1[0m [38;5;28;01mdef[39;00m [38;5;21mget_rates[39m(periode, bars[38;5;241m=[39m[38;5;241m300[39m):
[0;32m      2[0m     [38;5;66;03m#global symbol[39;00m
[0;32m      3[0m 
[0;32m      4[0m     [38;5;66;03m# OHLC abrufen[39;00m
[1;32m----> 5[0m     ohlc [38;5;241m=[39m mt[38;5;241m.[39mcopy_rates_from_pos(symbol, timeframes_dict[periode], [38;5;241m0[39m, bars)
[0;32m      6[0m     df [38;5;241m=[39m pd[38;5;241m.[39mDataFrame(ohlc)
[0;32m      7[0m     df[[38;5;124m'[39m[38;5;124mtime[39m[38;5;124m'[39m] [38;5;241m=[39m pd[38;5;241m.[39mto_datetime(df[[38;5;124m'[39m[38;5;124mtime[39m[38;5;124m'[39m], unit[38;5;241m=[39m[38;5;124m'[39m[38;5;124ms[39m[38;5;124m'[39m)

[1;31mKeyError[0m: 'top_down'

In [37]:

trend_dict[periods_dict[symbol][0]]

Out [37]:

'uptrend'

In [38]:

periods_dict[symbol], pause_trading

Out [38]:

(['m15'], 0)

Generate signals

In [39]:

def get_mt5_data(symbol=symbol, timeframe=mt.TIMEFRAME_M15, n_bars=500):
    rates = mt.copy_rates_from_pos(symbol, timeframe, 0, n_bars)
    df = pd.DataFrame(rates)
    df["time"] = pd.to_datetime(df["time"], unit="s")
    return df

In [40]:

def get_m5_trade_signal(symbol, atr_mult=1.5):
    global trend_dict, periods_dict

    # Hole die letzten M5-Daten
    df = get_rates('m5').iloc[-200:]

    # Smoothen
    df["close_smooth_std"] = savgol_filter(df.close, 25, 5)
    df["close_smooth_fast"] = savgol_filter(df.close, 15, 5)

    atr = df.atr.iloc[-1]

    # --- Standard-Trend ---
    peaks_std, _ = find_peaks(df.close_smooth_std, distance=1, width=2, prominence=atr)
    troughs_std, _ = find_peaks(-df.close_smooth_std, distance=1, width=2, prominence=atr)

    if len(peaks_std) > 0 and len(troughs_std) > 0:
        if peaks_std[-1] > troughs_std[-1]:
            trend_standard = "downtrend"
        else:
            trend_standard = "uptrend"
    else:
        trend_standard = "neutral"

    # --- Fast-Trend ---
    peaks_fast, _ = find_peaks(df.close_smooth_fast, distance=1, width=2, prominence=atr*0.5)
    troughs_fast, _ = find_peaks(-df.close_smooth_fast, distance=1, width=2, prominence=atr*0.5)

    if len(peaks_fast) > 0 and len(troughs_fast) > 0:
        if peaks_fast[-1] > troughs_fast[-1]:
            trend_fast = "downtrend"
        else:
            trend_fast = "uptrend"
    else:
        slope = df.close_smooth_fast.diff().iloc[-5:].mean()
        trend_fast = "downtrend" if slope < 0 else "uptrend"

    # --- Kombiniertes Signal ---
    signal = 0  # 0 = neutral, 1 = long, -1 = short
    stop_loss = None

    if trend_standard == "uptrend" and trend_fast == "uptrend":
        signal = 1
        stop_loss = df.close.iloc[-1] - atr_mult * atr
    elif trend_standard == "downtrend" and trend_fast == "downtrend":
        signal = -1
        stop_loss = df.close.iloc[-1] + atr_mult * atr

    # Speichern
    trend_dict['m5'] = {"standard": trend_standard, "fast": trend_fast, "signal": signal}

    return {
        "signal": signal,
        "price": df.close.iloc[-1],
        "stop_loss": stop_loss,
        "trends": trend_dict['m15']
    }

In [41]:

def generate_signal(df = get_mt5_data(symbol=symbol, timeframe=timeframes_dict['m15']), confirm_window=3):
    """
    Berechnet drei Signalarten:
    - fast_signal: schnelle, aggressive Variante
    - standard_signal: konservative Basisstrategie
    - optimized_signal: zusätzliche Filter (ATR, ADX, strengerer RSI)
    """

    # Indikatoren
    df["ema21"] = df["close"].ewm(span=3).mean()
    df["ema50"] = df["close"].ewm(span=9).mean()
    df["rsi9"] = ta.rsi(df["close"], length=9)
    df["rsi14"] = ta.rsi(df["close"], length=14)
    df["trend"] = savgol_filter(df["close"], 25, 3)

    # Zusätzliche Filterindikatoren
    df["atr"] = ta.atr(df["high"], df["low"], df["close"], length=14)
    df["adx"] = ta.adx(df["high"], df["low"], df["close"], length=14)["ADX_14"]

    # Spalten für Signale
    df["fast_signal"] = 0
    df["standard_signal"] = 0
    df["optimized_signal"] = 0

    for i in range(1, len(df)):
        # -----------------------
        # FAST SIGNAL (früh/aggressiv)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["rsi9"].iloc[i] > 30
        ):
            df.at[i, "fast_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["rsi9"].iloc[i] < 60
        ):
            df.at[i, "fast_signal"] = -1

        # -----------------------
        # STANDARD SIGNAL (konservativ, ursprüngliche Logik)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] <= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] < 70
            and df["rsi9"].iloc[i] > 40
            and df["trend"].iloc[i] > df["trend"].iloc[i - 1]
        ):
            df.at[i, "standard_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] >= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] > 40
            and df["rsi9"].iloc[i] < 70
            and df["trend"].iloc[i] < df["trend"].iloc[i - 1]
        ):
            df.at[i, "standard_signal"] = -1

        # -----------------------
        # OPTIMIZED SIGNAL (mit ADX + ATR + strengerem RSI)
        # -----------------------
        if (
            df["ema21"].iloc[i] > df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] <= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] < 65                          # strengerer Filter
            and df["rsi9"].iloc[i] > 45                           # Momentum klarer
            and df["adx"].iloc[i] > 20                            # Trendstärke vorhanden
            and df["atr"].iloc[i] > df["atr"].rolling(50).mean().iloc[i]  # Volatilität über Durchschnitt
        ):
            df.at[i, "optimized_signal"] = 1
        elif (
            df["ema21"].iloc[i] < df["ema50"].iloc[i]
            and df["ema21"].iloc[i - 1] >= df["ema50"].iloc[i - 1]
            and df["rsi14"].iloc[i] > 35                          # strengerer Filter unten
            and df["rsi9"].iloc[i] < 55
            and df["adx"].iloc[i] > 20
            and df["atr"].iloc[i] > df["atr"].rolling(50).mean().iloc[i]
        ):
            df.at[i, "optimized_signal"] = -1

    return df

In [42]:

generate_signal()

Out [42]:

	time	open	high	low	close	tick_volume	spread	real_volume	ema21	ema50	rsi9	rsi14	trend	atr	adx	fast_signal	standard_signal	optimized_signal
0	2025-08-27 07:45:00	3374.93	3375.85	3373.94	3374.93	2467	20	0	3374.930000	3374.930000	NaN	NaN	3376.087631	NaN	NaN	0	0	0
1	2025-08-27 08:00:00	3374.91	3376.21	3374.11	3374.74	2904	20	0	3374.803333	3374.824444	NaN	NaN	3375.914101	NaN	NaN	0	0	0
2	2025-08-27 08:15:00	3374.73	3377.46	3374.21	3375.77	2313	20	0	3375.355714	3375.211967	NaN	NaN	3375.933879	NaN	NaN	0	0	0
3	2025-08-27 08:30:00	3375.78	3378.81	3375.69	3377.69	2745	20	0	3376.600667	3376.051409	NaN	NaN	3376.121647	NaN	NaN	0	0	0
4	2025-08-27 08:45:00	3377.68	3378.88	3375.85	3377.23	2374	20	0	3376.925484	3376.402013	NaN	NaN	3376.452085	NaN	NaN	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
495	2025-09-03 19:15:00	3565.40	3568.01	3564.18	3567.83	3848	20	0	3566.195399	3562.948376	71.921940	69.332849	3568.219300	5.677877	31.169524	1	0	0
496	2025-09-03 19:30:00	3567.83	3569.42	3566.97	3569.01	3442	20	0	3567.602699	3564.160701	73.431485	70.341690	3569.360376	5.447314	32.165951	1	0	0
497	2025-09-03 19:45:00	3569.01	3572.57	3568.59	3572.43	3585	20	0	3570.016350	3565.814561	77.394221	73.103386	3570.468641	5.342506	33.517576	1	0	0
498	2025-09-03 20:00:00	3572.45	3572.98	3570.24	3572.22	3806	20	0	3571.118175	3567.095649	76.604942	72.656004	3571.539564	5.156613	34.825121	1	0	0
499	2025-09-03 20:15:00	3572.26	3572.68	3571.43	3572.06	1092	20	0	3571.589087	3568.088519	75.941120	72.292990	3572.568615	4.877569	36.039270	1	0	0

500 rows × 18 columns

In [43]:

pos = mt.positions_total()

if pos > 0:
    open_positions = mt.positions_get() 
    trail_factor = 0.5
    entry_price = open_positions[0].price_open
    tp_current = open_positions[0].tp
    current_price = open_positions[0].price_current
    profit = open_positions[0].profit

    profit = current_price - entry_price
    if profit > 0:
        new_tp = current_price - entry_price * trail_factor
    
    if profit < 0:
        new_tp = current_price - profit * trail_factor        

    if new_tp > current_price:
        #update tp from open pos
        new_tp
        print(f"Trailing +TP for BUY {symbol}: {new_tp} CP: {current_price}")
    if new_tp < current_price:
        #update tp from open pos
        new_tp
        print(f"Trailing -TP for BUY {symbol}: {new_tp} CP: {current_price}")

#open_positions

Trailing +TP for BUY XAUUSD: 3572.2200000000003 CP: 3572.06

Update TP-SL on open positions

In [44]:

def update_trailing_sl_tp(pos, atr, rrr=2.0, atr_mult=1.5, max_retries=2):
    """
    Aktualisiert SL und TP für offene Positionen:
    - ATR-basiertes Trailing
    - Gewinn-stufenweises Nachziehen
    - Dynamische Anpassung mit Validierung
    - Fallback-Mechanismus bei RETCODE 1016
    """

    symbol = pos.symbol
    info = mt.symbol_info(symbol)
    digits = info.digits
    point = info.point
    stops_level = info.trade_stops_level * point  # Mindestabstand vom Broker

    entry_price = pos.price_open
    current_tick = mt.symbol_info_tick(symbol)
    bid, ask = current_tick.bid, current_tick.ask
    current_price = bid if pos.type == 0 else ask
    pos_type = pos.type  # 0 = BUY, 1 = SELL

    # Gewinn in ATR berechnen
    if pos_type == 0:  # LONG
        profit_atr = (current_price - entry_price) / atr
        base_sl = current_price - atr_mult * atr
        new_sl = max(pos.sl or 0, base_sl)

        if profit_atr > 2:
            new_sl = max(new_sl, entry_price + 1.5 * atr)
        elif profit_atr > 1:
            new_sl = max(new_sl, entry_price + 1.0 * atr)
        elif profit_atr > 0.5:
            new_sl = max(new_sl, entry_price + 0.5 * atr)

        new_tp = entry_price + (entry_price - new_sl) * rrr

        # Validierung BUY
        if new_sl >= bid - stops_level:
            new_sl = bid - stops_level
        if new_tp <= ask + stops_level:
            new_tp = ask + stops_level

    else:  # SHORT
        profit_atr = (entry_price - current_price) / atr
        base_sl = current_price + atr_mult * atr
        new_sl = min(pos.sl or 999999, base_sl)

        if profit_atr > 2:
            new_sl = min(new_sl, entry_price - 1.5 * atr)
        elif profit_atr > 1:
            new_sl = min(new_sl, entry_price - 1.0 * atr)
        elif profit_atr > 0.5:
            new_sl = min(new_sl, entry_price - 0.5 * atr)

        new_tp = entry_price - (new_sl - entry_price) * rrr

        # Validierung SELL
        if new_sl <= ask + stops_level:
            new_sl = ask + stops_level
        if new_tp >= bid - stops_level:
            new_tp = bid - stops_level

    # Runden auf gültige Stellen
    new_sl = round(new_sl, digits)
    new_tp = round(new_tp, digits)

    # --- Nur updaten, wenn sich Werte geändert haben ---
    if (pos.sl is None or abs(new_sl - pos.sl) > point) or \
       (pos.tp is None or abs(new_tp - pos.tp) > point):

        for attempt in range(max_retries):
            request = {
                "action": mt.TRADE_ACTION_SLTP,
                "symbol": symbol,
                "sl": new_sl,
                "tp": new_tp,
                "position": pos.ticket
            }
            result = mt.order_send(request)

            if result.retcode == mt.TRADE_RETCODE_DONE:
                print(f"🔄 Updated {symbol} | SL: {new_sl:.5f} | TP: {new_tp:.5f}")
                break
            elif result.retcode == mt.TRADE_RETCODE_INVALID_STOPS:
                # Fallback: Stops korrigieren
                print(f"⚠️ RETCODE 1016 (Invalid stops) – Versuch {attempt+1}/{max_retries}")
                adjust = 2 * stops_level  # mehr Abstand
                if pos_type == 0:  # BUY
                    new_sl = bid - adjust
                    new_tp = ask + adjust
                else:  # SELL
                    new_sl = ask + adjust
                    new_tp = bid - adjust

                new_sl = round(new_sl, digits)
                new_tp = round(new_tp, digits)
                continue  # retry
            else:
                print(f"❌ SL/TP Update Fehler: {result.retcode} ({result.comment})")
                break

    return {"new_sl": new_sl, "new_tp": new_tp, "profit_atr": profit_atr}

In [45]:

def manual_update_trailing_sl_tp(atr_mult=1.5, slope_factor=1.5):
    # --- Hole die letzten M5-Daten ---
    df = get_rates('m5').iloc[-200:]

    # --- ATR Berechnung ---
    df["hl"] = df["high"] - df["low"]
    df["hc"] = (df["high"] - df["close"].shift()).abs()
    df["lc"] = (df["low"] - df["close"].shift()).abs()
    df["tr"] = df[["hl","hc","lc"]].max(axis=1)
    df["atr"] = df["tr"].rolling(14).mean()
    atr = df["atr"].iloc[-1]

    current_price = df["close"].iloc[-1]

    # --- Trendrichtung per Linear Regression ---
    def linreg_slope(series):
        X = np.arange(len(series)).reshape(-1, 1)
        y = series.values.reshape(-1, 1)
        model = LinearRegression().fit(X, y)
        return model.coef_[0][0]

    slope_long = linreg_slope(df["close"].iloc[-50:])  # 50 Balken (~4h)
    slope_short = linreg_slope(df["close"].iloc[-15:]) # 15 Balken (~1h)

    # --- Dynamischer Seitwärtsfilter ---
    slope_threshold = slope_factor * atr / df["close"].iloc[-1]

    # --- Dynamische RRR-Berechnung ---
    atr_norm = atr / current_price          # relative Volatilität
    slope_strength = abs(slope_short)       # Trendstärke aus Regression

    rrr_base = 1.2
    rrr_from_vol = atr_norm * 1500          # skaliert ATR-Einfluss
    rrr_from_slope = slope_strength / slope_threshold  # Slope-Einfluss

    rrr = rrr_base + rrr_from_vol + rrr_from_slope
    rrr = max(1.2, min(rrr, 3.0))           # Begrenzung


    open_positions = mt.positions_get(symbol=symbol)
    for pos in open_positions:
        atr_value = df["atr"].iloc[-1]
        update_trailing_sl_tp(pos, atr=atr_value, rrr=rrr, atr_mult=atr_mult)

In [46]:

manual_update_trailing_sl_tp()

🔄 Updated XAUUSD | SL: 3569.71000 | TP: 3580.40000

Get M5 Trade Signals

In [54]:

def get_m5_trade_signals(symbol=symbol, atr_mult=1.5, base_rrr=2.0, atr_min=0.0005, slope_factor=1.5, execute=False):
    """
    Analyse von M5-Signalen mit ATR/Trend/Seitwärtsfilter
    - execute=False -> nur Analyse
    - execute=True  -> führt Orders aus
    """

    global trend_dict, volume_dict

    df = get_rates('m5').iloc[-200:]

    # --- ATR ---
    df["hl"] = df["high"] - df["low"]
    df["hc"] = (df["high"] - df["close"].shift()).abs()
    df["lc"] = (df["low"] - df["close"].shift()).abs()
    df["tr"] = df[["hl","hc","lc"]].max(axis=1)
    df["atr"] = df["tr"].rolling(14).mean()
    atr = df["atr"].iloc[-1]

    if atr < atr_min:
        return {"signal": 0, "reason": "ATR too low → sideways"}

    # --- Trend ---
    def linreg_slope(series):
        X = np.arange(len(series)).reshape(-1, 1)
        y = series.values.reshape(-1, 1)
        model = LinearRegression().fit(X, y)
        return model.coef_[0][0]

    slope_long = linreg_slope(df["close"].iloc[-50:])
    slope_short = linreg_slope(df["close"].iloc[-15:])
    slope_threshold = slope_factor * atr / df["close"].iloc[-1]

    if abs(slope_long) < slope_threshold and abs(slope_short) < slope_threshold:
        return {"signal": 0, "reason": "Trend flat → sideways"}

    trend_standard = "uptrend" if slope_long > 0 else "downtrend"
    trend_fast = "uptrend" if slope_short > 0 else "downtrend"

    current_price = df["close"].iloc[-1]

    # --- RRR ---
    atr_norm = atr / current_price
    slope_strength = abs(slope_short)
    rrr_base = 1.2
    rrr_from_vol = atr_norm * 1500
    rrr_from_slope = slope_strength / slope_threshold
    rrr = max(1.2, min(rrr_base + rrr_from_vol + rrr_from_slope, 3.0))

    # --- Signal ---
    signal, stop_loss, takeprofit = 0, None, None
    if trend_standard == "uptrend" and trend_fast == "uptrend":
        signal = 1
        stop_loss = current_price - atr_mult * atr
        takeprofit = current_price + atr_mult * atr * rrr

        if execute:
            print(f"BUY {symbol} @ {current_price} | TP: {takeprofit} | SL: {stop_loss}")
            market_order(symbol, volume_dict[symbol], "buy", stoploss=stop_loss, take_profit=takeprofit)

    elif trend_standard == "downtrend" and trend_fast == "downtrend":
        signal = -1
        stop_loss = current_price + atr_mult * atr
        takeprofit = current_price - atr_mult * atr * rrr

        if execute:
            print(f"SELL {symbol} @ {current_price} | TP: {takeprofit} | SL: {stop_loss}")
            market_order(symbol, volume_dict[symbol], "sell", stoploss=stop_loss, take_profit=takeprofit)

    # --- speichern ---
    trend_dict['m5'] = {"standard": trend_standard, "fast": trend_fast, "signal": signal}

    return {
        "signal": signal,
        "price": current_price,
        "stop_loss": stop_loss,
        "take_profit": takeprofit,
        "Risk Reward": rrr,
        "trends": trend_dict['m5'],
        "atr": atr,
        "slope_long": slope_long,
        "slope_short": slope_short
    }

In [47]:

def execute_m5_trade(symbol=symbol, atr_mult=1.5, base_rrr=2.0):
    """
    Führt einen Trade auf M5 nur aus, wenn das Top-Down-Setup ein positives Signal liefert.
    Nutzt adaptive ATR, dynamisches RRR und SL/TP.
    """

    global trend_dict, volume_dict, pause_trading

    # --- Top-Down-Signal prüfen ---
    top_down = get_top_down_signal(symbol)

    if pause_trading == 1:
        print("⚠️ Trading pausiert. Keine Trades ausgeführt.")
        return None

    if not top_down["setup_ready"] or top_down["entry_signal"] == 0:
        print("Kein Top-Down-Setup vorhanden. Kein Trade.")
        return None

    # --- M5-Signal berechnen (nur Signal, keine automatische Order) ---
    m5_signal_info = get_m5_trade_signals(symbol=symbol, atr_mult=atr_mult, base_rrr=base_rrr)

    if m5_signal_info["signal"] == 0:
        print("M5 Signal neutral. Kein Trade.")
        return None

    # --- Trade ausführen ---
    current_price = m5_signal_info["price"]
    stop_loss = m5_signal_info["stop_loss"]
    take_profit = m5_signal_info["take_profit"]

    if m5_signal_info["signal"] == 1:
        # Long
        print(f"✅ BUY {symbol} @ {current_price} | TP: {take_profit} | SL: {stop_loss}")
        market_order(symbol, volume_dict[symbol], "buy", stoploss=stop_loss, take_profit=take_profit)
    elif m5_signal_info["signal"] == -1:
        # Short
        print(f"✅ SELL {symbol} @ {current_price} | TP: {take_profit} | SL: {stop_loss}")
        market_order(symbol, volume_dict[symbol], "sell", stoploss=stop_loss, take_profit=take_profit)

    # --- Offene Trades aktualisieren (Trailing SL/TP) ---
    open_positions = mt.positions_get(symbol=symbol)
    for pos in open_positions:
        update_trailing_sl_tp(pos, atr=m5_signal_info["atr"], rrr=m5_signal_info["Risk Reward"], atr_mult=atr_mult)

    return {
        "top_down": top_down,
        "m5_signal": m5_signal_info
    }

In [55]:

execute_m5_trade()

Kein Top-Down-Setup vorhanden. Kein Trade.

✅ BUY XAUUSD @ 3571.06 | TP: 3578.2085714285713 | SL: 3568.677142857143
✅ BUY XAUUSD @ 3571.81 | TP: 3579.604642857143 | SL: 3569.2117857142857
🔄 Updated XAUUSD | SL: 3570.03000 | TP: 3579.44000
🔄 Updated XAUUSD | SL: 3570.58000 | TP: 3577.77000
🔄 Updated XAUUSD | SL: 3573.16000 | TP: 3573.77000
✅ BUY XAUUSD @ 3573.65 | TP: 3581.7596428571433 | SL: 3570.946785714286
🔄 Updated XAUUSD | SL: 3570.95000 | TP: 3582.31000
🔄 Updated XAUUSD | SL: 3571.07000 | TP: 3581.95000
✅ BUY XAUUSD @ 3573.67 | TP: 3581.6832142857143 | SL: 3570.9989285714287
🔄 Updated XAUUSD | SL: 3571.77000 | TP: 3579.86000
🔄 Updated XAUUSD | SL: 3571.77000 | TP: 3579.85000
✅ BUY XAUUSD @ 3574.21 | TP: 3582.0914285714284 | SL: 3571.5828571428574
🔄 Updated XAUUSD | SL: 3574.66000 | TP: 3575.40000
🔄 Updated XAUUSD | SL: 3574.67000 | TP: 3575.60000
🔄 Updated XAUUSD | SL: 3574.67000 | TP: 3575.39000
🔄 Updated XAUUSD | SL: 3574.67000 | TP: 3575.63000
🔄 Updated XAUUSD | SL: 3574.67000 | TP: 3575.68000
✅ BUY XAUUSD @ 3575.28 | TP: 3582.5957142857146 | SL: 3572.841428571429
✅ BUY XAUUSD @ 3575.47 | TP: 3583.2614285714285 | SL: 3572.872857142857
🔄 Updated XAUUSD | SL: 3572.88000 | TP: 3584.11000
Kein Top-Down-Setup vorhanden. Kein Trade.
✅ BUY XAUUSD @ 3575.29 | TP: 3583.4285714285716 | SL: 3572.577142857143
🔄 Updated XAUUSD | SL: 3573.59000 | TP: 3581.99000
🔄 Updated XAUUSD | SL: 3573.90000 | TP: 3581.07000
🔄 Updated XAUUSD | SL: 3573.90000 | TP: 3581.06000
🔄 Updated XAUUSD | SL: 3576.56000 | TP: 3577.79000
✅ BUY XAUUSD @ 3577.4 | TP: 3584.628928571429 | SL: 3574.990357142857
⚠️ RETCODE 1016 (Invalid stops) – Versuch 1/2
🔄 Updated XAUUSD | SL: 3577.00000 | TP: 3578.00000
✅ BUY XAUUSD @ 3578.23 | TP: 3586.0310714285715 | SL: 3575.6296428571427
🔄 Updated XAUUSD | SL: 3575.63000 | TP: 3586.63000
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
M5 Signal neutral. Kein Trade.
M5 Signal neutral. Kein Trade.
M5 Signal neutral. Kein Trade.
M5 Signal neutral. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
M5 Signal neutral. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
M5 Signal neutral. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
M5 Signal neutral. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
M5 Signal neutral. Kein Trade.
✅ SELL XAUUSD @ 3563.72 | TP: 3551.165 | SL: 3567.9049999999997
Kein Top-Down-Setup vorhanden. Kein Trade.
✅ SELL XAUUSD @ 3563.47 | TP: 3551.0532142857146 | SL: 3567.6089285714284
✅ SELL XAUUSD @ 3563.92 | TP: 3552.6603571428577 | SL: 3567.673214285714
✅ SELL XAUUSD @ 3565.45 | TP: 3553.589285714286 | SL: 3569.403571428571
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
✅ SELL XAUUSD @ 3562.22 | TP: 3553.2489285714287 | SL: 3565.210357142857
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.
Kein Top-Down-Setup vorhanden. Kein Trade.

Job-Scheduler

Doku Appscheduler Cronjob

In [51]:

from apscheduler.schedulers.background import BackgroundScheduler
import time


scheduler = BackgroundScheduler()
#scheduler.add_job(main, 'date', run_date='2025-03-07 14:29:50')

#scheduler.add_job(decide_order, 'interval', minutes=1) #intervall
#scheduler.add_job(set_symbol, 'interval', minutes=30)
#scheduler.add_job(set_trend, 'interval', minutes=1)

#scheduler.add_job(decide_order, 'interval', minutes=1)
#scheduler.add_job(decide_order, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*') #cron
#scheduler.add_job(get_buy_sell_signal, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
#scheduler.add_job(get_m5_trade_signals, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
scheduler.add_job(execute_m5_trade, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*/5') #cron
scheduler.add_job(manual_update_trailing_sl_tp, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour='0-23', minute='*') #cron

#scheduler.add_job(export_marketview, 'cron', year="*", month='*', day_of_week='mon, tue, wed; thu, fri', hour='8-22', minute=00)

#scheduler.add_job(pause_trading, 'cron', year="*", month="*", day_of_week="mon, tue, wed, thu, fri", hour=22, minute=00) #cron
scheduler.start()

Get Jobs

In [52]:

scheduler.get_jobs()

Out [52]:

[<Job (id=6d0f4ea790404395bdf8de6e4b08d249 name=manual_update_trailing_sl_tp)>,
 <Job (id=aaf1ad647f4d4187acd88c3bfd1a24be name=execute_m5_trade)>]

Remove all Jobs

In [ ]:

scheduler.remove_all_jobs()

Shutdown AppScheduler

In [ ]:

scheduler.shutdown()

132 KiB Raw Blame History Unescape Escape

Import Libaries

Login

Set symbol and volume

set volume manuell

Functions to place Orders on Market

Market Order Function

Trend Detection

timefame & trend dictionary

Set Trend manually

Generate signals

Update TP-SL on open positions

Get M5 Trade Signals

Job-Scheduler

Get Jobs

Remove all Jobs

Shutdown AppScheduler

132 KiB

Raw Blame History