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
Fundamental News Trading Strategy
50 - 200 USD
I want to build a fundamental news trading bot that trade off economic news data, as we know every economic news data released always have effect on the asset associated with it, so this bot will take a trade instantly based on the news data released either to buy or sell, it will come with good money management and also SL and TP target based on price and pips value
TumiiFX
30 - 20000 USD
1. Use two EMAs: 20 and 50. If EMA 20 is above EMA 50 → uptrend (look for buys) If EMA 20 is below EMA 50 → downtrend (look for sells) 2. Wait for a pullback into the area between the two EMAs. - For buys: price must touch or move between EMA 20 and EMA 50 during the last few candles. - For stils: same idea, but in a downtrend. 3. Entry signal: Buy: a bullish engulfing candle in an uptrend after the pullback
HFT / Latency Arbitrage pepperstone
30 - 5000 USD
I am looking for an experienced MQL5 developer to build a high-frequency (HFT) latency arbitrage Expert Advisor for Pepperstone MT5 , using LMAX as the leading price feed. The initial focus will be on US30 (Dow Jones) , and if the strategy proves successful, I want the EA to be easily expandable to additional symbols such as NAS100, GER40, XAUUSD, major forex pairs, and other supported instruments. The EA should
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
Мне нужен простой торговый бот, написанный исключительно на Python. Бот должен подключаться к терминалу MetaTrader 5 через официальную библиотеку Python "MetaTrader5". Объем кода невелик (около 250 строк). КРИТИЧЕСКИ ВАЖНЫЕ ТРЕБОВАНИЯ: 1. НЕТ КОДА MQL5: Весь проект должен быть написан только на Python. 2. ВНЕШНЯЯ КОНФИГУРАЦИЯ: У бота должен быть внешний конфигурационный файл (config.ini или settings.json). Я должен
Crypto Latency Arbitrage EA
30 - 5000 USD
I am looking for an experienced MQL4 or MQL5 developer to build a high-frequency (HFT) latency arbitrage Expert Advisor for cryptocurrency trading between LMAX and IC Markets. I need someone who understands low-latency execution, price feeds, slippage, spreads, and fast order execution. The basic idea is that LMAX acts as the leading price feed while IC Markets is the execution broker. The EA should constantly
Title: Need 5 Years Historical Trading Central Analyst Views Data for Backtesting Hello, I need historical Trading Central Analyst Views data for at least the past 5 years. This is not only a tool to record new signals from today. I need past historical data already available from previous years. The data I need should include, if available: Symbol : XAUUSD / GOLD Date and time of the Analyst View Timeframe: 30 MIN
Advanced Forex Expert Advisor-fully automated system
200 - 300 USD
I require a custom EA and an accompanying custom indicator built in MQL5 for Meta Trader 4/5. The EA must be fully automated (Algo Trading); Telegram-Signal-Linked and named 'AMK Fx'
Informazioni sul progetto
Budget
30+ USD
Scadenze
da 1 a 2 giorno(i)