Here is how you can pass FTMO Challenge if you're Python Coder

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Relation between FTMO Challenge and Python

The FTMO Challenge is a series of trading tests designed to assess a trader's ability to generate profits while managing risk. The tests involve trading a simulated account with real-time market conditions and are designed to be challenging.

To pass the FTMO Challenge using Python, you will need to develop and implement a trading strategy that is able to consistently generate profits under these conditions. This will likely involve the use of technical analysis, risk management techniques, and sound decision-making skills.

Here are some general steps that you can follow to pass the FTMO Challenge using Python:

  1. Familiarize yourself with the rules and requirements of the FTMO Challenge.
  2. Use Python to analyze market data and develop a trading strategy that meets the requirements of the challenge.
  3. Implement your trading strategy in Python using tools such as the pandas and numpy libraries for data analysis, and the pytrader library for trading.
  4. Test your trading strategy using historical market data to ensure that it is robust and consistent.
  5. Use the FTMO Simulator to practice trading under real-time market conditions and refine your strategy as needed.
  6. Once you feel confident in your trading strategy, apply for the FTMO Challenge and demonstrate your trading skills to pass the tests.

It is important to note that passing the FTMO Challenge is not easy, and it requires a high level of skill and dedication. However, by following these steps and consistently working to improve your trading skills, you can increase your chances of success.

Python Script to predict Volatility of any Forex pair using OHLC data

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# load the data
data = pd.read_csv('forex_data.csv')

# select features and target
X = data[['open', 'high', 'low', 'close']]
y = data['volatility']

# split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# train the model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# evaluate the model
score = model.score(X_test, y_test)
print('Model score:', score)

# make predictions
predictions = model.predict(X_test)

# calculate root mean squared error
rmse = np.sqrt(mean_squared_error(y_test, predictions))
print('RMSE:', rmse)

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