Investor/RT includes all the required tools for a professional approach to trading system design, including an easy to use Trading System Builder Interface providing advanced backtesting features, together with advanced optimization and walk forward testing features. Trading systems can then be deployed under real time live data feed for actual live (or simulated) trading thanks to Trading Systems indicator which offer unique ways to visualize the live execution of a trading strategy
Key concepts for building, testing and deploying Trading Systems with Investor/RT
Investor/RT backtesting features are built around the Trading System intuitive Builder Interface and setup windows. A trading system is composed of one or more trading rules. Each trading rule is, in turn, composed of a trading signal and an associated action, such as buy, sell, sell short, etc. Trading signals are logical expressions (true or false statements) written in the Investor/RT Language (RTL). RTL is the language used to compose custom indicators and signals, which are 2 of the key Investor/RT objects, and which can use the output (through RTL or RTX tokens) of any Investor/RT technical indicators.
Dedicated backtesting tokens provides an instantaneous feedback, at every bar of the backtest computation, about the system execution and performance: the most recent entry and exit price, the stop or target price, the number of bars or minutes or days the position has been open, the position size and/or cost and the profitability of the position at any stage, etc They can therefore be included in the syntax of trading rules, allowing the user to manage complex situations such as adding size to an existing position, taking partial profits, simulating complex position sizing strategies, etc
Once a trading system has been defined in Investor/RT and given a name, users can backtest their set of trading rules using historical data for one or more instruments. Backtesting refers to the simulation, on past data, of the actual execution of the trading system rules on a security, or set of securities over a given period of time. Upon completion of the backtest, Investor/RT displays a summary and a detailed report of the performance of the trading system over the specified period. The user can make revisions to the trading rules, add or remove rules, break down rules execution with dedicated diagnostics tools (based on SHOW statements or NOTE tokens), or adjust the trading signals used in the rules, then Backtest again to see if the revisions improved the performance of the trading system.
Optimization and "Walk Forward Testing" (aka the "Realization" feature)
Both features play a key role in the Trading system development loop :
- Optimization pertains to the ability to determine the combination of trading system parameters which results in the most favorable performance for that trading system. These optimisation parameters include a variety of technical indicator periods, periodicity, stops and target levels, or money management criteria involved in the search for the optimum position sizing strategies.
- Realization feature performs "out of sample" walk forward testing to make sure to avoid the main pitfall of any trading system development: overfitting, ie designing a system that adapts so closely to a single set of historical data that it becomes fully ineffective in the future.
Here is a logical path to discover our trading system features and indicators:
The Trading System Indicator (TSYSI)
Trading System", as well as "Optimization" and "Realization" are 3 separate Investor/RT objects: they are falling in the same "object category" as charts, custom indicators or Signals, meaning they can be managed (ie imported, exported, copied, renamed etc) through the Object Manager. On the other hand, the Trading System Indicator (TSYSI), like any other technical indicator, needs to be attached to a chart and is used in 2 scenarios :
- in the first scenario, the same exact Trading System "object" is used inside the TSYS indicators (with no rule syntax modifications). In that case, the TSYS will use the chart periodicity and viewing period as inputs for the trading system, with the order being executed as per the "Price Rule" associated with each Buying or Selling Trading Rule action.
- In a second configuration, "Signal actions" are associated with the individual buy and sell "trading rules", with these signal actions triggering the opening and closure of actual trading positions by sending orders towards a Rithmic, CQG or IBKR accounts.
Of course, it is always recommended, in a first stage, to execute such automated orders towards a paper server environment, before switching to real money accounts. The critical issue is of course of keeping the theoretical system position synchronized with the live position, as trading orders might not execute as simulated in the trading system. This is discussed in the 3 part video series on the TSYSI indicator page.
Inside a given chart, a Trading Rule indicator has to be considered as an alternative of the TSYSI : it will trigger the execution of a trading order not based upon the application of a complex set of rules embedded inside a Trading System, but according to simplified applicability settings : A trading rule will become applicable when a certain trading state is in effect (FLAT, LONG, SHORT, LONG or FLAT, SHORT or FLAT, etc.). This indicator allows for the implementation of semi-automated systems based on simple rules such as a price level being touched or crossed.
Reminder: please review the order execution features support page when it comes to implementing live trade features.
Finally, it is worth mentioning 2 separate RTX indicators that also provide trading systems like type of analysis, but not based on the trading system tool itself :
The Pivot Strategy Analyzer (PSA RTX) and the associated Pivot Strategy Optimizer (PSO RTX) which were both specifically developed to evaluate the performance of simple trading strategies with entries and exits based on the crossing of key market levels. This tool allows traders to specify daily or weekly price levels (like previous high, close, Profile Value Area, overnight VPOC, levels based on the pivot point indicators, etc) and analyze how price reacts to them during the following session or week.