Algorithmic Forex Trading with MT5 Python API

Explore the MT5 terminal, create a demo account, and learn to place trades with one-click and new order, while understanding pip basics and order types.

Learn to automate Mt5 trading with Python by launching the Mt5 terminal, reading credentials from key.txt, and initializing the Mt5 API to connect to the demo server.

Connect your Mt5 terminal to Darwinex zero's live trading server and use your Python trading application for live trading and backtesting.

Learn how forex symbols work by identifying the base currency (what you buy or sell) and the counter currency (what you pay with), with lot sizes of 100,000 and a minimum 0.01 lot.

Learn how to define a pip, how pip moves translate to PnL, and why major pairs use 0.0001 (0.01 for yen), with examples on euro/usd and usd/jpy.

Understand balance, equity, and margin in the MT5 terminal, with 100:1 leverage shaping margin and unrealized PNL affecting equity for open positions.

demonstrates fetching account information with the mt5 python api via account_info, showing balance, equity, margin, and free margin, and introduces account_details for bundling data before placing orders.

Align MT5 historical data with UTC by localizing times and converting between Eastern European time and UTC using pytz, ensuring data consistency across MT5 charts and Python data.

learn to fetch time zone agnostic historical data in mt5 using copy rates from position. use start position to get data till current time for backtesting without time zone complications.

Learn what technical indicators are, how to calculate them from price data in Python, and how they confirm trends with lagging signals using indicators like MACD, RSI, and Bollinger Band.

Explore how to access, insert, and download MT5 technical indicators, understand indicator families, and plan to replicate Mql5 calculations in Python using TradingView guidance.

Explore moving averages, including simple and exponential types, to smooth forex price noise and reveal trends with SMA and EMA calculations.

Implement moving averages in Python using historical close prices, building SMA and EMA functions, handling NaN values, and validating results against historical data in MT5 Python API.

Implement MacD by building fast and slow EMAs, compute MacD, derive a signal EMA, and an optional histogram, then append MacD, signal, and histogram to the input dataframe.

Implement the stochastic indicator by calculating highest highs and lowest lows over a 14-period lookback and deriving percent k and percent d with SMA, using pandas rolling.

Explore support and resistance as key price levels used by technical analysts, and learn to identify hills and valleys with Python code to determine these levels from historical forex data.

Explore how to compute support and resistance from valleys in historical data, using current price, neighborhood ranges, and a nested loop approach to identify valid levels.

Place market and limit forex orders, including pending orders, using the MT5 Python API by building a request dictionary for order_send, including action, symbol, volume, and price.

Learn to close trades in MT5 with the close function, including offsetting naked positions or targeting a specific ticket, using symbol and ticket.

Explore how to modify existing MT5 orders with the MT5 Python API, updating stop loss and take profit for positions and adjusting pending orders or bracket orders through trade actions.

Explore backtesting with historical data to mimic real trading conditions, account for slippage and costs, and conservatively evaluate strategies before deployment.

Backtest a forex trading strategy with MT5 Python, using utility functions and MacD to generate long and short signals, then compute pips, slippage, stop loss, take profit, and trade stats.

Extend backtesting for existing long or short signals with stop loss, take profit, slippage, and pip-based returns; add MACD-based false-signal exits and end-of-data close handling.

Integrates support and resistance into a single MT5 backtesting function, using hills and valleys, configurable bars and time frame, with robust defaults.

Explore key intraday backtesting metrics for algorithmic forex trading with MT5 Python API, including absolute return in pips, win rate, mean return per winning and losing trades, and maximum drawdown.

Learn to compute backtest KPIs from trade stats and candle data, including absolute return from cumulative returns and a win rate for long and short trades.

Evaluate a forex strategy using KPIs like mean return and win rate in backtesting. Analyze pips, drawdown, and cumulative return within MT5 Python API workflows.

Backtest your forex strategy with KPI functions to compute the equity curve, max drawdown, win rate, and average pip returns, then analyze USD JPY results.

Review backtesting flow and structure, tweak strategies with time frame changes and additional indicators (MACD, RSI), and adjust support/resistance logic to balance take-profit and stop-loss.

Explore the macd–stochastic double cross forex strategy, using a 200-period sma trend filter, with clear long and short entry rules and simple exit signals, plus practical backtesting insights.

Deploy your backtested forex strategy with mt5 python api by optimizing position size, asset selection, asset-specific parameters, and establishing notifications and contingency plans.

Develop a Python function to compute position size from a per-pip pnl goal using MT5 symbol data, current price, and USD conversion factor, base currency, and volume step compliance.

Learn to perform correlation analysis on forex pairs using historical data and Pearson correlation to select loosely correlated assets, improving diversification and risk-reward in MT5 Python API trading.

Learn to implement email notifications for algorithmic forex trading with Python using smtplib, Gmail SMTP, TLS, and app passwords. Send customizable messages for each trading signal.

Deploy a backtested forex strategy to live MT5 trading by converting the backtesting code to deployment logic, using a symbol-specific params csv and get position size for orders.

Analyze historical data and macd on ohlc data to generate trade signals, compute position size, and place bracket order using dynamic support and resistance levels.

Deploy your MT5 Python algos to the cloud for uninterrupted, scalable trading, avoiding desktop disruptions, and paying only for resources, with AWS as the course's preferred platform.

Connect to a cloud virtual machine using remote desktop, obtain the computer name and default password, sign in as administrator, and install only essential software while expecting initial lag.

Transfer files between your local machine and the vm while optimization runs, install the mt5 terminal, and log in; install miniconda to run python with pandas, numpy, matplotlib, and pytz.