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MONEY MANAGEMENT ALGORITHMS CONCEPT

MMA Diagram (1).webp
  • Equity curve trading for individual trading systems.

  • Apply technical analysis to the equity curve.

  • Better entry algorithm for entry techniques.

Portfolio Money Management Algorithms for automated trading
  • Equity curve trading for combined trading systems.

  • Portfolio level stop loss and profit targets.

  • Manage risk intra-day.

PORTFOLIO MONEY MANAGEMENT ALGORITHMS (PMMA)

The Portfolio Money Management Algorithms (PMMA) were developed and released in 2023. The Portfolio Money Management Algorithms were developed to manage the combined trading systems equity curve intra-day. The ability to answer intra-day risk management questions for combinations of trading systems has become a focus in our algorithmic trading systems portfolios research. Typically, combined trading system portfolios end of day performance and risk analysis is measured with the Portfolio Calculator or the Multicharts Portfolio Trader.

Building a tool similar to the Money Management Algorithms, we found a way to measure the combined trading systems portfolio equity curve intra-day and in real time, down to one-minute intervals.

 

The Portfolio Money Management Algorithms are a series of indicator and strategy templates that allow us to measure and manage risk more incrementally so that portfolio level stop losses can be used intra-day.  

combining trading systems into one setup

PORTFOLIO MONEY MANAGEMENT ALGORITHM EXAMPLES

SIGNAL STRENGTH TRADER

signal strength trader strategies for the E-mini

The Signal Strength Trader is one application of the Portfolio Money Management Algorithms and is a customizable template that can be applied to a portfolio of trading systems trading the same market.  The Signal Strength Trader will sum the trading system signal inputs and return a net position based on a signal threshold based on the inputs NetPosLong or NetPosShort.

For example, the inputs for the Signal Strength Trader could include a list of 20 E-mini Nasdaq strategies. The Signal Strength Trader sums up the net position and then takes a net long or net short position. If NetPosLong is set to +3 then the number of long signals minus short signals must be greater than 3 before the Singal Strength Trader takes a single long position.

If the NetPosShort is set to -3 then the number of short signals minus long signals must be greater than 3, which would be a -3 net short signal, then the Signal Strength Trader would take a single short position.

The real benefit of the Signal Strength Trader for the multi-strategy algorithmic trader is the ability to combine multiple trading system signals into one strategy and then scale risk based on the number of contracts traded. Max Daily Entries as well as stop losses, profit targets, and other risk parameters are also available inputs that can be added to manage risk in each market on an intra-day basis.

MARKET POSITION TRADER

marketposition trader for futures trading systems

The Market Position Trader is another application of the Portfolio Money Management Algorithms and is a customizable template that can be applied to a portfolio of trading systems trading the same market. The Market Position Trader takes a list of trading system signals on the same market and then takes the same trades as the original trading system signals in one combined trading system. The advantage of the Market Position Trader is its ability to connect all strategies and trade them as one trading system.

Connecting all strategies into one trading systems provides the framework for intra-day risk management. Typically, portfolios of trading systems are managed individually and could potentially have a much worse case losing day or drawdown in the future than the portfolio of strategies had in the past. Connecting the list of strategies into a portfolio of trading systems also provides a framework for additional trade management and the ability to test trading ideas to manage risk and seek risk adjusted returns.

For example, if the Marketposition Trader is using a list of 20 E-mini S&P trading systems, the Marketposition Trader can add a portfolio level stop loss for both backtesting and automation. Backtesting and automating portfolio level stop losses and other risk management intra-day for a group of trading systems is very challenging with the available trading software tools that are currently available. The Portfolio Money Management Algorithms make this setup possible with the Marketposition Trader. Additionally, the Marketposition Trader can limit max number of contracts, limit max daily entries, and add additional exit strategies such as profit targets. The open code template and training provide the resources to customize an unlimited number of possibilities and scenarios.

This coding framework in the templates and covered in the training, sets up the ability to combine strategies and then filter entries based on numerous sets of criteria and additional data sets. The Marketposition Trader is just one of many potential applications. 

SEVEN STEP PMMA SETUP PREVIEW

This is a preview on how to start with four basic strategies including RSI, Bollinger Bands, Momentum, and Consecutive Closes and build out a live setup so that each strategy measures the combination of all strategies in its trade decisions intra-day. The complete training and open code is in the Members Area Course section.

BEFORE Basic Strategy Setup

trading system equity curves on e-mini nasdaq

AFTER Basic Strategy Setup

equity curve improvements using money management algorithms

MONEY MANAGEMENT ALGORITHMS (MMA)

using money management algorithms to reduce drawdowns in trading

The Money Management Algorithm Tool was originally developed starting in 2005. It was developed as an individual trading system that provides an overlay of different technical analysis techniques and market indicators to be applied to the equity curve of the original strategy to decide when to trade the strategy. It is a “trading system for a trading system” and also known as also known as the Equity Curve Algorithms so the money allocated to trading systems can be managed.

​The Money Management Algorithms were developed out of necessity to answer “What If” questions based on timing the equity curve which became essential in our own market research and strategy development. There was not a tool that would efficiently allow us to answer these types of questions for both backtesting and automating strategies in live trading.

THE ORIGINAL VERSION - FIRST REVISION

The original setup for the Money Management Algorithms required that the trade information would be exported using an indicator to a text file. The data was then imported into a new chart window along with the original strategy. The originally strategy would be modified and backtested based on the new equity curve information.

The original approach was a static environment for backtesting  but it was impossible to implement during live trading. Updating multiple strategies during the trading day by exporting and importing data was too slow to implement in live trading. Concepts could be tested to determine if they were valid trading ideas but implementing them on intra-day charts was not realistic.

HERE IS THE PROBLEM

The problem with writing an equity curve algorithm rule within the original strategy is simply the fact that once the strategy is turned off, based on the equity curve rule, it will not continue to generate an equity curve that can be tracked. Tracking the original equity curve when the strategy is not being traded is essential to develop an equity curve algorithm.

The equity curve algorithms track the original trades and then select which LIVE trades to take based on the criteria developed during research. This video highlights a simple code set that will stop trade generation on a drawdown. Once a strategy stops generating trades, tracking future performance is not possible. Starting the strategy based on a new cycle or time, such as a new month, is possible, but writing the algorithm to start based on its latest performance is preferred.

HOW DOES IT WORK? STRATEGY EXAMPLES

TICK PULSE V7

Tick Pulse is one of our long-term trading systems that we have had for a long time. We apply a couple of rules from the Money Management Algorithm rules to improve the equity curve. The basic rule set that we apply is based on the concept of starting on a drawdown and exiting on a runup. This average trade profit as well as the net profit as a percentage of drawdown increase to more attractive trading levels.

Tick Pulse v7 E-mini Nasdaq can also be used to send signals to the E-mini S&P through the Money Management Algorithm. This is an example of using a market that has
sharper moves can be used to trade a correlated but more liquid market with more stealth price movement. 

ADD DIVERGENCE

ADD Divergence is a trading system that trades the E-mini Nasdaq using the broader market internal $ADD or the Advance Decline Line. This strategy has performed well the last 18 months. It has also performed well the last 3 years but with more volatility in the equity curve. Is there a way to reduce the volatility in the equity curve?

Simply applying one basic rule, Rule 12, we are able to reduce drawdown, increase net profit, and boost average trade profit. This open code example and training is in the Members Area.

VIX SWING

The VIX Swing setup is an example of how a long-term swing trade strategy such as VIX Swing, with an amazing closed trade equity curve, but a very steep intra-trade drawdown, could be used to take shorter-term swing trades or day trades.

The Money Management Algorithm is applied to the chart on the right using Rule 6 and Rule 12. The strategy uses a $1000 stop loss and $1000 profit target per contract. Setting up the Entry Times and Exit Times would enable a day trade approach to trade this strategy that essentially uses the marketposition of the VIX Swing strategy to determine when long trades should be taken. The open code setup and training is in the Members Area.

RSI BREAKOUT FADER

One of the best ways to trade the Nasdaq futures in 2023 is to simply fade the RSI Breakouts. The equity curve for this strategy is favorable for traders in 2023. How do we manage risk during time periods when this strategy doesn't work as well?

The RSI Breakout Fader E-mini Nasdaq managed with the Money Management Algorithm's Rule 1, Closed Trade Moving Average, manages its historical drawdown and in a seven year historical backtest. The open code strategy files are in the Members Area.

MOMENTUM FADER

This is an example of how we use a basic Momentum indicator to develop a momentum fader strategy for the E-mini Nasdaq and manage the risk in the equity curve with the Money Management Algorithm.

The Momentum Fader NQ uses a few basic rules for entry with a stop loss, profit target, and end of day exit to short rallies and buy dips. This pattern is very relevant in 2023. Applying the closed trade moving average with the default inputs of 20 and 40 for the L1 and L2 in the moving average, reduce the historical drawdowns. The open code strategy files are in the Members Area.

THE RULES

  1. ​Close Trade Moving Average – Trade the strategy only if the two period moving average of the original strategies closed trade equity curve is up.

  2. Close Trade ADX – Trade the strategy only if the ADX of the closed trade equity curve is above the ADX Threshold.

  3. Closed Trade Stochastic – Trade the strategy only if the Stochastic of the closed trade equity curve is above the Stochastic Threshold.

  4. ​Closed Trade RSI – Trade the strategy only if the RSI of the closed trade equity curve is above the RSI Threshold.

  5. ​Average Trade Profit – Trade the strategy only if the Average Trade Profit of the last 10 trades is with the specified average trade profit range, typically above 0.

  6. ​Better Entry Price – Improve entry efficiency by entering at better prices by looking at the base strategies marketposition and entry price and placing limit orders to improve on that price by a specified dollar amount.

  7. ​Drawdown Stop – Stop trading the equity curve if it goes into a pre-defined drawdown (set as an input) and then start trading if it goes into a run up of a pre-defined amount from equity valley lows.

  8. ​Open Trade Drawdown Start I – Require the Drawdown to be greater than the Average Drawdown of the last 100 bars.

  9. ​Open Trade Drawdown Start II – Require the Drawdown to be less than the Average Drawdown of the last 100 bars.

  10. ​Open Trade Moving Average – Require the open equity curve two period moving average be up.

  11. ​Open Trade RSI – RSI of the open equity curve of the last L1 bars be greater than the RSI Threshold.

  12. ​Dip Buying Entry – Enter the strategy on a drawdown by “buying dips” of a specified amount/input.

  13. ​Consecutive Losers Algorithm – lets us start after a specified number of losers.

The type of questions that we wanted to ask and answer were:

  1. What happens if you stop trading on a drawdown and then start again on a runup?

  2. What happens if we wait for a drawdown to start trading and then stop on a runup?

  3. Can we apply indicators such as Moving Averages, RSI, and Stochastics to the equity curve to time our trading periods.?

  4. Can we look at the cycles in the average trade profit statistic to start and stop trading a strategy?

  5. Can we look at the original entry and then wait for a pullback to get in on a better entry?

These are just a few example questions that can be answered with the Money Management Algorithms. In order to answer these questions, the original strategy has to continue to generate trades while the algorithm decides which one of those trades to take.

In theory these ideas could be tested in spreadsheets and manually implemented (the way I tried to originally set it up in 2005). In the fast-paced world of trading and markets, precision is lost and any technical errors make it impossible to recover.

A basic solution that includes programming stop losses directly into a trading system based on time periods such as stopping on a drawdown and then starting on the new month can be implemented. If “stop trading” based on a daily, weekly, or monthly drawdown is programmed directly into a trading system, then the base equity curve is no longer being generated. The idea of starting once the equity curve turns higher is not possible since the equity curve is no longer being generated in the original strategy.

The Money Management Algorithms are the only tool I have found (and developed) that can accurately backtest ideas based on tracking a continuous equity curve and then selecting when to start and stop trading. 

strategy inputs for money management
equity curve of a money management algorithm
equity curve examples for trading systems and strategy development

The same setup that is used to backtest these ideas can then be automated in live trading and used to manage your trading system.

DLL’s are used to pass trade information between charts. The base trading system signal is generated in one chart window. The information from the based strategy is then passed to a second window that has a strategy that uses the original rules in addition to the equity curve management rules.

LIFETIME LICENSE

MONEY MANAGEMENT ALGORITHMS
  • Complete Training Course with 22+ Lessons

  • ​Open Code Software for complete customization

  • ​Optimization Algorithm

  • ​Strategy Specific Algorithm

  • ​Fully disclosed trading system examples

  • ​ADD Divergence

  • ​VIX Swing

  • ​Momentum Fader

  • ​RSI Breakout Fader

  • ​MM Indicators - released in 2023

  • ​Easylanguage Training Sessions

  • ​Lifetime access to Membership Area, Upgrades, Support

concepts in algorithmic trading using money management algorithms
PORTFOLIO MONEY MANAGEMENT ALGORITHMS
  • Complete Training Course with 20 Lessons

  • ​Open Code Software for complete customization

  • ​PMMA Indicators

  • ​Signal Strength Trader

  • ​Market Position Trader

  • ​PMMA 7 Step Setup

  • ​Fully disclosed trading system examples

  • ​Consecutive Closes Fader​

  • ​Bollinger Band Fader

  • ​HVDP Trading Systems

  • ​Easylanguage Training Sessions

  • ​Lifetime access to Membership Area, Upgrades, Support

portfolio money management algorithm design

The Lifetime License includes the open-source code for the Money Management Algorithms, Portfolio Money Management Algorithms, example trading systems as well as access to both Members Areas with software downloads and training.

Requires a signed Non-Disclosure Agreement
for complete access.
Download NDA

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PORTFOLIO MONEY MANAGEMENT ALGORITHMS

Intra-Day Risk Management Tool to Manage Portfolios of Trading Systems

  • Includes the Money Management Algorithms

  • Backtest and Live Automation

  • Open Source Code with Examples

  • Lifetime Access and Updates

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