Specifiche

# Import necessary libraries
import pandas as pd

# Define parameters
stop_loss_percentage = 0.02  # Set stop loss percentage (2% in this example)
take_profit_percentage = 0.05  # Set take profit percentage (5% in this example)

# Read historical price data
df = pd.read_csv("historical_data.csv")  # Replace with your historical data file or API integration

# Calculate moving averages
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['SMA_200'] = df['Close'].rolling(window=200).mean()

# Initialize variables
position = None
entry_price = 0.0

# Start trading loop
for i in range(200, len(df)):
    current_price = df['Close'].iloc[i]

    # Check for entry conditions
    if position is None and df['SMA_50'].iloc[i] > df['SMA_200'].iloc[i]:
        position = 'long'
        entry_price = current_price
        print(f"Enter long position at {entry_price}")

    elif position is None and df['SMA_50'].iloc[i] < df['SMA_200'].iloc[i]:
        position = 'short'
        entry_price = current_price
        print(f"Enter short position at {entry_price}")

    # Check for exit conditions
    if position == 'long' and current_price >= (1 + take_profit_percentage) * entry_price:
        position = None
        exit_price = current_price
        print(f"Exit long position at {exit_price}")
        profit = exit_price - entry_price
        print(f"Profit: {profit}")

    elif position == 'long' and current_price <= (1 - stop_loss_percentage) * entry_price:
        position = None
        exit_price = current_price
        print(f"Exit long position at {exit_price}")
        loss = exit_price - entry_price
        print(f"Loss: {loss}")

    elif position == 'short' and current_price <= (1 - take_profit_percentage) * entry_price:
        position = None
        exit_price = current_price
        print(f"Exit short position at {exit_price}")
        profit = entry_price - exit_price
        print(f"Profit: {profit}")

    elif position == 'short' and current_price >= (1 + stop_loss_percentage) * entry_price:
        position = None
        exit_price = current_price
        print(f"Exit short position at {exit_price}")
        loss = entry_price - exit_price
        print(f"Loss: {loss}")

Con risposta

1
Sviluppatore 1
Valutazioni
(2)
Progetti
2
0%
Arbitraggio
1
0% / 0%
In ritardo
2
100%
Gratuito
2
Sviluppatore 2
Valutazioni
(43)
Progetti
66
12%
Arbitraggio
12
58% / 42%
In ritardo
1
2%
Gratuito
3
Sviluppatore 3
Valutazioni
(35)
Progetti
50
42%
Arbitraggio
3
33% / 33%
In ritardo
4
8%
Gratuito
4
Sviluppatore 4
Valutazioni
(6)
Progetti
10
50%
Arbitraggio
6
17% / 50%
In ritardo
3
30%
In elaborazione
5
Sviluppatore 5
Valutazioni
(5)
Progetti
4
50%
Arbitraggio
4
0% / 75%
In ritardo
0
Gratuito
Ordini simili
I need an experienced MQL5 developer to build a semi automated trading signal system for Gold (XAUUSD) on MT5. The system is NOT a martingale or grid EA. The goal is to build a clean rule based signal engine that detects high probability setups based on predefined strategy rules and sends trading alerts with optional pending order logic. Main Requirements: 1. Signal Generation - Buy and Sell signals - Buy Limit - Buy
Mambo 30+ USD
I need a bot that can trade weltrade synthetic indices that can be consistently making profits if you have one for deriv its also fine a bot that executes and closes trades automat Will be ideal
I am looking for an experienced MQL4/MQL5 developer to build a custom MT4 indicator from scratch or cracking my ex4 file that i provide to you. I already have an existing indicator (EX4) which produces highly accurate buy/sell signals. I want a similar indicator developed based on its observable behavior and signal structure. my existing indicator is pc id protected so you have to do PC ID security bypass and source
I need a very advanced and intelligent MT5 Expert Advisor coded in MQL5 for XAUUSD, based on ICT + CRT + Smart Money Concepts. The goal is not a simple robot, but a professional decision-making system with strong filters, risk control, and high-quality trade selection. The EA must include: 1. Multi-Timeframe Analysis - D1 / H4 / H1 bias - M15 / M5 entry confirmation - Bullish or bearish market structure - BOS, CHoCH
Intraday Trade Ninja EA — Complete Logic Structure This document maps the full architecture, execution logic, signal flow, trade management, and safety structure of the Intraday Trade Ninja MT4 Expert Advisor. 1. Core Indicators · ©Price Border (TMA bands) · MA-X Arrows · MA-Y Arrows · LeManSignal · EMA 49 & 89 - Per Candle Color Switching 2. EA Entry Architecture ·
I have a 90% completed project with the execution part left to complete, I have been struggling to complete this section and I need help from someone expert in MQL5 with knowledge on forex trading and ICT Concepts coding. Contact me for further details
Patricia Ukawilu 6:43 PM I need help creating an EA to optimize my trade. I already have a preliminary pine script which I will want optimized and create an EA from it to optimize my trade on MT4. I also subscribed to a signal app. I’m looking to automate the execution of the signal from the app so as not to miss out on good trades
I am looking for an experienced MQL5 developer with Python/data analysis skills. I have my own MT5 Strategy Tester reports, exported trade history, and market CSV data. I need help analyzing these files and developing a new independent Expert Advisor based on clearly defined, statistically tested, and validated trading rules. Tasks: Analyze my MT5 Strategy Tester reports and exported trade history. Compare historical
We are looking for a developer to finish and stabilize an existing Kalshi trading bot (~60% complete) built in TypeScript. This is not a MetaTrader EA. The system interacts with the Kalshi API and requires strong understanding of execution logic, order handling, and state management. Scope of Work: Review and understand existing TypeScript codebase Complete missing functionality Fix execution issues (order placement
I need a professional MT5 Expert Advisor (EA) built with clean, modular code. This is an advanced strategy combining liquidity concepts, controlled DCA, hedge protection, and strict risk management. Core Requirements: Entry Logic (ALL must align): Liquidity sweep (Previous Day High/Low breakout and return) EMA50 and EMA200 trend alignment Higher timeframe bias (H1 or H4) RSI confirmation Bollinger Band entry Filters

Informazioni sul progetto

Budget
30+ USD
Scadenze
da 1 a 2 giorno(i)