InvestiQuant - What's New #algo trading

Matt's Minute: Is experience needed?

Mon, 22 Sep 2025 07:10:03 -0400

Experience needed?

A common concern amongst those first learning about autotrading is, how much investing experience is needed to participate. With our solution, it’s less than you think. Check out this quick video to learn more.

Six Benefits Of Cloud-Based Automated Trading Systems

Mon, 01 Sep 2025 07:00:00 -0400

Are you a trader or an active, self-directed investor who has tried different strategies with little success? Are you tired of the emotional rollercoaster that comes with making trading decisions? Or do you find it difficult to monitor the markets and execute trades effectively due to a busy schedule? If you answered yes to any of these questions, it may be time to consider a new approach. Cloud-based automated trading systems use sophisticated algorithms to analyze market data in real time, identify trading opportunities, and execute trades without human intervention. This can eliminate the emotional bias that often plagues manual trading and help you achieve more consistent results. Additionally, cloud-based automated trading programs can operate 24/7, 365 days a year, allowing you to take advantage of more trading opportunities if you have a busy schedule, without having to install or manage complicated software. This innovative trading approach can also leverage advances in technology, like AI, with statistical backing.

Let’s take a deeper dive into automated, cloud-based trading systems and how you can incorporate them into your investment approach.

What Are Cloud-Based Trading Systems?

Cloud-based trading systems are software applications that are hosted in the “cloud” (via an Internet server farm like those offered by AWS (Amazon) or Microsoft) and managed over the Internet by the system developer. They are not installed on an individual’s PC or computer. The trading systems use sophisticated algorithms to analyze market data and identify trading opportunities. When a market condition meets pre-determined rules and parameters, the algorithms send trade signals to the broker’s signal execution software and execute trades without human intervention.

Cloud-based, automated trading systems can offer several benefits. However, it is not without risks, and self-directed investors should carefully consider their goals, risk tolerance, and investment capital before using an automated trading program.

How Are Fully Automated Trading Systems Different From Other Investment Strategies?

Fully automated trading systems are different from other investment strategies in that they rely on pre-programmed rules and algorithms to execute trades, rather than relying on human decision-making or analysis. These rules can be adaptive based on recent market performance and changing conditions and even driven by advanced machine-learning and AI technology to find opportunities that would not be found otherwise. Automation also improves execution speed (minimizing missed opportunities) and removes emotion from the trading process, helping to improve consistency and trading results.

Traditional investment strategies, such as those based on fundamental or technical analysis, involve a trader making subjective decisions based on their analysis of market conditions, news events, and other factors. This can be influenced by cognitive biases (such as anchoring, loss aversion, and gamblers’ fallacy) and other emotions and ultimately lead to poor trading results. Fully automated trading programs can also operate 24/7, without the need for human intervention, which can allow for more opportunities and even take advantage of movements in multiple markets simultaneously.

Fully automated trading programs are complex to build and maintain, but when built properly by trading professionals, can offer many benefits to investors.

What Are The Benefits Of Cloud-based, Fully Automated Trading Systems?

Cloud-based, fully automated trading systems offer several benefits to new and experienced traders, and investors alike, including:

Consistency: One of the key benefits of using fully automated trading systems is consistency. Unlike human traders who can be influenced by emotions and cognitive biases, an automated trading program follows pre-programmed rules and parameters, leading to more disciplined and profitable trades.
Efficiency: Automated trading programs can save traders time by analyzing market data and executing trades much faster than a human trader. This can allow traders to take advantage of more trading opportunities, particularly in fast-moving markets. Moreover, automated trading programs can operate 24/7, meaning traders can benefit from trades that occur outside of regular trading hours.
Backtesting: Automated trading systems can be backtested and optimized on historical data, allowing traders to identify strategies with the best odds of success. This can lead to better trading results and help traders avoid costly mistakes that may have been made through trial and error.
Risk Management: Automated trading software can help traders manage risk by setting stop-loss orders and other risk-management parameters. This can help prevent large losses and preserve capital, particularly in volatile markets
Scalability: Automated trading programs can be scaled up to handle larger trading volumes, which can be difficult for human traders to manage. This can allow traders to take advantage of more trading opportunities without sacrificing performance.
Diversification: Automated trading solutions can allow traders to diversify their trading portfolios by using multiple strategies simultaneously. This can help to spread risk and potentially increase returns.

How Do I Get Started With A Cloud-Based Automated Trading System?

Getting started withInvestiQuant's fully automated trading programs is a great way to take advantage of volatility and put advanced technology to work for you. iQ Autotrading offers traders a range of robust, statistically-based, systematic trading strategies that are designed to generate attractive returns, regardless of the direction of the stock market. The best part is that these turnkey solutions can be enabled to autotrade your account at an approved execution broker in a matter of days, without having to install or maintain any software whatsoever.

With the intraday design of the strategies, there is no overnight risk, and all strategies seek to profit using one of two primary trading methodologies: mean-reversion or momentum. They trade either long or short with adaptive, volatility-based stops and targets to take advantage of bullish and bearish opportunities.

By utilizing a wide range of strategy inputs, including alternative data, technical patterns, volatility, market profile, seasonal events, and multiple advanced machine-learning techniques,iQ Autotrading offers a diversified approach to algorithmic trading. InvestiQuant's autotrading is a powerful, hands-free solution for traders and investors of all types, offering strong, verified returns, potential for outsized growth, emotion-free, statistically-based trading, low correlation with stock trading strategies, a turnkey, hands-free solution, and aligned interests.

Ready to see how cloud-based, automated trading can transform your investment portfolio? Request info to learn about the performance of InvestiQuant’s automated trading programs and how your account could be enabled for automated trading in a matter of days.

How Do Algorithmic Autotrading Strategies Work?

Fri, 25 Jul 2025 14:37:15 -0400

I have explained what Autotrading is at a high level in a past article. Now, let’s take a peak underneath the hood of the iQ (InvestiQuant) Autotrading engine and learn how autotrading strategies work.

Data Capture
The first step in the algorithmic trading strategies process is historical data capture. Algorithmic autotrading strategies require large amounts of clean, robust market data for optimal evaluation and signal generation. Sophisticated software and 3rd party data feeds are utilized to capture and cleanse historical market and price data for a diverse array of contextual market inputs:
Market Events (e.g. FOMC Fed Announcement, Options Expiration, Futures Rollover, etc.),
Economic Reports (Employment, CPI, PPI, earnings, etc.),
Price Patterns (new market highs/lows, overbought/oversold levels, unfilled price gaps, etc.),
Volatility (VIX, ATR, etc.),
Calendar Considerations (day of week, day of month, month, quarter, etc.),
As the saying goes, “garbage in = garbage out” and InvestiQuant expends considerable resources to capture, cleanse, organize, store, update, and monitor nearly a thousand different data points (most of which update daily) that help its algorithms evaluate the real-time state of the market. When determining the optimal signals and risk parameters for a given trading day, context is king.

Strategy Tracking
But market data alone is not enough to generate a trading signal. Every signal is created by a well-constructed strategy with very strict trading parameters (long or short, target, stop) for a given trading instrument (futures contract, option, or share), market (S&P 500, AAPL, etc.) and environment (trend, volatility, etc.). Every strategy should be thoroughly researched, analyzed, tested, traded, and refined before entering production.

Once a strategy is trading live in production, both its historical and ongoing live trading results should be stored and tracked in databases for real-time evaluation each day. Maintaining a comprehensive database of strategies and their daily performance across all market environments is critical for enabling them to adapt to changing conditions, as well as their own performance. With the help of advanced financial machine learning techniques, the most sophisticated trading algorithms (like many of those utilized by InvestiQuant’s autotrading programs) will automatically deactivate until their performance improves, or a more conducive environment is present.

Note: One of the most common reasons algorithmic trading strategies fail is because they were over-optimized originally (typically by a novice developer), or their algorithmic rules were not adaptive. Meaning, the rules are too simple and rigid, and not engineered to evolve with the ever-changing markets. The adaptive nature of InvestiQuant’s strategies and machine learning techniques is what has allowed them to stand the test of time and perform across a wide range of market environments (e.g. raging bull markets, volatile bear markets, etc.)

Signal Identification
Algorithms come in many shapes and sizes and utilize a series of processes, calculations, and other problem-solving operations to achieve a desired outcome. Even tying your shoelaces is a form of an algorithm! Of course, trading algorithms are a bit more involved, to say the least.

The 3rd step of the algorithmic trading strategies process is algorithmic signal identification. The goal of this step is to identify which trading strategies’ requirements (market conditions, price patterns, volatility levels, events, calendar considerations, etc.) are met and should be considered for execution. In other words, for which strategies are market conditions favorable for trading today?

Risk Management and Signal Selection
Once all of the potential strategies for a given day have been identified, they are then segmented into the autotrading programs that each supports. (InvestiQuant offers different size autotrading programs powered by algorithmic trading strategies to accommodate the wide range of desired investment levels of traders and investors. The larger the program the more strategies it includes.)

Each iQ Autotrading program operates with a predetermined maximum daily risk budget (varies by program, typically 3-5%) and a suggested funding level ($30k - $1M). For example, the iQ Trader Program consists of about 15 different algorithmic strategies, and its max daily risk budget for a given day is $2.5k. If the trade criteria for four of its strategies are met and their collective required risk budget (i.e. the amount of risk needed to allow them to trade optimally) is $3k total, then the signal selection algo will only have enough risk budget to select three of the strategies’ signals for execution that day.

Signal Execution
After the filtering process of risk management and signal selection, the signals are then ready for electronic distribution. That day’s trading signals are electronically sent within milliseconds of selection by our signal server (co-located with the CME exchange in Chicago, Illinois) to our executing brokers’ order management software which processes and converts the signals into electronic orders for execution at the exchanges on behalf of InvestiQuant’s clients.

The brokers utilize sophisticated APS (average price sold) algorithms to ensure that all clients earn the same results (to the penny) that I get in my account for each signal executed.

Algorithmic Autotrading Strategies - Powered by IQ
Since 2008, InvestiQuant has armed traders and self-directed investors with institutional quality algorithmic trading strategies based on statistical edges. InvestiQuant’s AI-driven automated solutions help clients better protect and grow their wealth, hands-free—regardless of the direction of the broader stock market. Request autotrading information today to learn more!

Request Autotrading Info

Click below to share with your friends or colleagues

Matt's Minute: Experience needed?

Fri, 01 Nov 2024 17:20:47 -0400

Experience needed?

A common concern amongst those learning about autotrading for the first time is, how much investing experience is needed to participate. With our solution, it’s less than you think. Check out this quick video to find out.

Matt's Minute: Important Tips for Autotrading Success

Sat, 19 Oct 2024 13:45:26 -0400

How Algorithmic Autotrading Strategies Work

Wed, 05 Jul 2023 11:45:58 -0400

In the prior article, I explained what Autotrading is at a high level. Now, let’s take a peak underneath the hood of the iQ (InvestiQuant) Autotrading engine and learn how autotrading strategies work.

Data Capture
The first step in the algorithmic trading strategies process is historical data capture. Algorithmic autotrading strategies require large amounts of clean, robust market data for optimal evaluation and signal generation. Sophisticated software and 3rd party data feeds are utilized to capture and cleanse historical market and price data for a diverse array of contextual market inputs:
Market Events (e.g. FOMC Fed Announcement, Options Expiration, Futures Rollover, etc.),
Economic Reports (Employment, CPI, PPI, earnings, etc.),
Price Patterns (new market highs/lows, overbought/oversold levels, unfilled price gaps, etc.),
Volatility (VIX, ATR, etc.),
Calendar Considerations (day of week, day of month, month, quarter, etc.),
As the saying goes, “garbage in = garbage out” and InvestiQuant expends considerable resources to capture, cleanse, organize, store, update, and monitor nearly a thousand different data points (most of which update daily) that help its algorithms evaluate the real-time state of the market. When determining the optimal signals and risk parameters for a given trading day, context is king.

Strategy Tracking
But market data alone is not enough to generate a trading signal. Every signal is created by a well-constructed strategy with very strict trading parameters (long or short, target, stop) for a given trading instrument (futures contract, option, or share), market (S&P 500, AAPL, etc.) and environment (trend, volatility, etc.). Every strategy should be thoroughly researched, analyzed, tested, traded, and refined before entering production.

Once a strategy is trading live in production, both its historical and ongoing live trading results should be stored and tracked in databases for real-time evaluation each day. Maintaining a comprehensive database of strategies and their daily performance across all market environments is critical for enabling them to adapt to changing conditions, as well as their own performance. With the help of advanced financial machine learning techniques, the most sophisticated trading algorithms (like many of those utilized by InvestiQuant’s autotrading programs) will automatically deactivate until their performance improves, or a more conducive environment is present.

Note: One of the most common reasons algorithmic trading strategies fail is because they were over-optimized originally (typically by a novice developer), or their algorithmic rules were not adaptive. Meaning, the rules are too simple and rigid, and not engineered to evolve with the ever-changing markets. The adaptive nature of InvestiQuant’s strategies and machine learning techniques is what has allowed them to stand the test of time and perform across a wide range of market environments (e.g. raging bull markets, volatile bear markets, etc.)

Signal Identification
Algorithms come in many shapes and sizes and utilize a series of processes, calculations, and other problem-solving operations to achieve a desired outcome. Even tying your shoelaces is a form of an algorithm! Of course, trading algorithms are a bit more involved, to say the least.

The 3rd step of the algorithmic trading strategies process is algorithmic signal identification. The goal of this step is to identify which trading strategies’ requirements (market conditions, price patterns, volatility levels, events, calendar considerations, etc.) are met and should be considered for execution. In other words, for which strategies are market conditions favorable for trading today?

Risk Management and Signal Selection
Once all of the potential strategies for a given day have been identified, they are then segmented into the autotrading programs that each supports. (InvestiQuant offers different size autotrading programs powered by algorithmic trading strategies to accommodate the wide range of desired investment levels of traders and investors. The larger the program the more strategies it includes.)

Each iQ Autotrading program operates with a predetermined maximum daily risk budget (varies by program, typically 3-5%) and a suggested funding level ($30k - $1M). For example, the iQ Trader Program consists of about 15 different algorithmic strategies, and its max daily risk budget for a given day is $2.5k. If the trade criteria for four of its strategies are met and their collective required risk budget (i.e. the amount of risk needed to allow them to trade optimally) is $3k total, then the signal selection algo will only have enough risk budget to select three of the strategies’ signals for execution that day.

Signal Execution
After the filtering process of risk management and signal selection, the signals are then ready for electronic distribution. That day’s trading signals are electronically sent within milliseconds of selection by our signal server (co-located with the CME exchange in Chicago, Illinois) to our executing brokers’ order management software which processes and converts the signals into electronic orders for execution at the exchanges on behalf of InvestiQuant’s clients.

The brokers utilize sophisticated APS (average price sold) algorithms to ensure that all clients earn the same results (to the penny) that I get in my account for each signal executed.

Algorithmic Autotrading Strategies - Powered by IQ
Since 2008, InvestiQuant has armed traders and self-directed investors with institutional quality algorithmic trading strategies based on statistical edges. InvestiQuant’s AI-driven automated solutions help clients better protect and grow their wealth, hands-free—regardless of the direction of the broader stock market. Request autotrading information today to learn more!

Request Autotrading Info

Click below to share with your friends or colleagues