Complete algorithmic forex trading and back testing system

Set up the development environment by installing Python 3.6+, creating virtual environments, using Visual Studio Code with extensions, and using Postman for API requests.

Install Python 3.6 or newer on your system and learn how to run the interpreter across Windows, Linux, and macOS. Create a clean environment and avoid Anaconda, especially Windows 7.

Discover how Python virtual environments isolate project libraries, prevent version conflicts, and keep dependencies tidy. Learn to create and activate a virtual environment to manage dependencies within each project.

Master Postman to test API endpoints, organize requests in workspaces and collections, and simulate trading data for the complete algorithmic forex trading and back testing system course.

Learn how to call the accounts endpoint using Postman by configuring a workspace and a collection, setting bearer authorization and content-type json, and parsing a 200 response with account properties.

Explore the account instruments endpoint to fetch all tradable instruments for a given account via a get request, returning the instruments list and the last transaction id.

Learn how to retrieve candlestick data using the instrument candles endpoint, configuring granularity, count, and from-to ranges, and interpreting open, high, low, close, and volume with UTC timestamps.

Fetch pricing data for specified instruments via the pricing endpoint and learn how price objects present time, bids, asks, tradability, and home currency conversions for live trading.

Begin by using Python to test endpoints, collect thousands of candles across currency pairs, and outline the main code structure for the complete forex trading and backtesting system.

Set up a Python virtual environment and VS Code workflow to run notebooks for forex data analysis, installing pandas, Jupyter, plotly, and requests, then create and run notebooks.

Set up a Python 3 virtual environment, install pandas and Jupyter, configure a Jupyter theme, create an exploration folder, start a notebook server, and run a simple API test notebook.

In a python notebook, set up a requests session with bearer api key and JSON content-type, build a ten candle eurodollar query, and parse the JSON response.

Plot candle charts in notebooks to evaluate strategies, then build a simple back test across multiple currency pairs and parameters, and save and reopen notebooks to assess results.

Develop a reusable candle plotting utility for forex backtesting, enabling easy candlestick visualization in notebooks with a dedicated class, layout updates, and time-string handling.

Plot the ten-period moving average on the candle chart using a scatter trace, with a clear line width, creating the moving average line alongside the candlestick plots.

Plot multiple moving averages using pandas in Python to visualize 10, 20, 50, 100, and 200 period trends on forex data and build line traces for rapid backtesting prep.

Evaluate cross strategies across currency pairs to gauge consistency. The BAC back test shows no strong cross and about half the pairs are profitable.

Analyze a cross-based forex backtest by sorting total gains, tracing cumulative gains over time, and comparing currency pairs with pandas plots, while preparing Excel exports for deeper review.

Learn to build an algorithmic forex trading and back testing system that tests moving average cross strategies, saves results to Excel, and uses Python with pandas to extend candles data.

Collect candlestick data with Python scripting across many instruments and finer granularity, spanning six years. Rerun the moving average cross simulation to evaluate performance changes.

Implement a collection entry function that iterates euro and gbp pairs, builds instrument ids, and tests hourly data collection via collect_data with defined dates, paths, and an api.

Define date ranges and time steps to fetch candles across a span, handling end date and from date. Concatenate data frames and save the final file.

Run moving average cross simulations over six years of data, saving Excel results for the H1 and the H4 granularities, and analyze CAD/JPY, GBP/CAD, and NZD pairs.

Learn how to calculate popular forex indicators and how the RSI signals, and gain the skills to build your own indicators without libraries for reliable backtesting and live trading.

Implement Bollinger bands in Python with pandas by computing the typical price and rolling mean and std using n and s to produce upper and lower bands for forex trading.

learn to compute candle characteristics in Python: determine direction, body size, full range, and body percentage using pandas, and visualize with candle plots.

Compute candle characteristics by deriving body bottom and top percentages from candle ranges, normalize to the full range, and store results in a dataframe for later pattern identification.

Translate candle characteristics from notebook to a Python script, defining a patterns module that applies candle props and patterns to a dataframe using pandas, with VS Code workflow and testing.

Explore the inverted hammer candlestick pattern and its reversal signals in forex trading, including how to detect shooting star setups and implement pattern checks in a notebook.

Identify the spinning top candlestick, signaling indecision, by enforcing a 40–60% body range and a 15% body size constraint across candles.

Explore how engulfing candles signal trend reversals in the dual candlestick pattern, distinguishing bullish and bearish cases, comparing body sizes, and excluding gapping.

Explore the morning star and evening star multi-candlestick patterns and implement their detection with midpoints, body percentages, and pandas dataframes for automated backtesting in forex trading.

Convert notebook candle pattern logic into a script, extend the dataframe with pattern fields, implement set candle patterns, test detection (engulfing, morning/evening star, mariposa), and prepare for deeper simulations.