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
Valutazioni
Progetti
2
0%
Arbitraggio
1
0%
/
0%
In ritardo
2
100%
Gratuito
2
Valutazioni
Progetti
66
12%
Arbitraggio
12
58%
/
42%
In ritardo
1
2%
Gratuito
3
Valutazioni
Progetti
50
42%
Arbitraggio
3
33%
/
33%
In ritardo
4
8%
Gratuito
4
Valutazioni
Progetti
10
50%
Arbitraggio
6
17%
/
50%
In ritardo
3
30%
In elaborazione
5
Valutazioni
Progetti
4
50%
Arbitraggio
4
0%
/
75%
In ritardo
0
Gratuito
Ordini simili
Hello Traders and Investors, I am a professional algorithmic trading developer specialized in building high-quality Expert Advisors (EAs), Indicators, Scripts, and Trade Management Tools for MetaTrader 4 and MetaTrader 5. With extensive experience in financial markets and trading automation, I can transform your trading ideas into reliable and efficient solutions with clean, optimized, and well-structured code. My
8 cap prop firm passing
30 - 3000 USD
I am looking for an experienced MQL4/MQL5 HFT developer to build or optimize a High-Frequency Trading (HFT) Expert Advisor that can successfully pass proprietary trading firm challenges and perform consistently under live trading conditions with brokers such as 8cap or BlackBull Markets . The developer should have proven experience with HFT execution, ultra-low-latency trading, broker execution, slippage, spreads
MT4/MT5 HFT EA Live Trading
40 - 10000 USD
I have a High-Frequency Trading (HFT) Expert Advisor for both MT4 and MT5 designed primarily for US30 (Dow Jones Index) . The EA performs consistently and profitably on demo accounts, but when I run it on an IC Markets Raw or Standard live account, it starts generating losses under what appear to be the same trading conditions. At this time, I cannot provide the source code (.mq4/.mq5). I can only provide the
Version document : 1.0 Plateforme : TradingView Langage : Pine Script v6 Type : Indicateur d'analyse et d'aide à la décision (non-exécutant) 1. Présentation du projet Nom du produit ONYX SR V2 — Intelligent Support & Resistance Scalping System Objectif Créer un indicateur TradingView capable d'identifier automatiquement des opportunités de scalping basées sur : supports et résistances dynamiques ; action du prix ;
EA Crafter
500+ USD
Act as a professional Quantitative Developer and Risk Manager. I want to build a systematic trading strategy rulebook that prioritizes capital preservation and statistical edge over raw performance. Please generate a structured trading strategy using the following framework: 1. ASSET CLASS & TIMEFRAME: - Asset: [e.g., Apple (AAPL), Bitcoin (BTC), or EUR/USD] - Timeframe: [e.g., 5-minute, 1-hour, Daily] 2. CORE
Driven Multiple Choice
30+ USD
Part 1: Project setup Input settings (risk, stop loss, take profit, EMA periods) Indicator initialization Trade management framework Part 2: Trading logic EMA crossover detection Buy/Sell entry rules One-trade-per-symbol check Part 3: Risk management Automatic lot size calculation Stop-loss and take-profit placement Trade execution and error handling Part 4: Final touches On-screen information Optimization
Informazioni sul progetto
Budget
30+ USD
Scadenze
da 1 a 2 giorno(i)