Discover algorithmic trading using automated forex systems, covering core strategies, must-have indicators, and risk management tactics to boost your overall trading performance.
Algorithmic trading in Forex uses computer software to generate, manage, and execute trading decisions according to predefined rules. An algorithm can monitor currency pairs, calculate indicators, identify market conditions, determine position size, place orders, manage stops and targets, and close positions without requiring a trader to perform every step manually.
Automated execution can improve consistency and process large amounts of market data quickly, while it does not create a trading edge by itself. An unprofitable strategy remains unprofitable when automated, and poorly designed software can magnify errors much faster than manual trading.
A robust Forex algorithm therefore requires more than entry signals. Data quality, trading costs, position sizing, execution logic, testing methodology, system monitoring, market-regime behavior, infrastructure, and failure controls all determine how the system performs in live conditions.
What Is Algorithmic Trading in Forex?
Algorithmic Trading Explained
Algorithmic trading uses programmed instructions to perform part or all of the trading process.
The rules can be simple, such as buying when a moving-average condition occurs, or considerably more complex, combining price, volatility, economic data, execution conditions, portfolio exposure, and multiple timeframes.
Automated Trading and Algorithmic Trading
The terms algorithmic trading and automated trading are often used interchangeably, although automation can cover different levels of the process.
One system might generate an alert and leave execution to the trader. Another can automatically enter, manage, and exit every position.
Algorithms Do Not Need to Trade Quickly
Algorithmic trading is often associated with speed, although an algorithm does not need to operate in milliseconds.
A systematic swing strategy can evaluate a daily chart once per candle and still qualify as algorithmic trading. The defining feature is the use of programmed decision rules rather than the speed or number of trades.
Algorithmic Trading Is Not the Same as High-Frequency Trading
High-frequency trading (HFT) is a specialized form of algorithmic trading that operates at extremely short time horizons and can depend heavily on low-latency infrastructure, rapid market-data processing, and sophisticated execution technology.
Most retail automated Forex strategies are not HFT systems. Traders interested in conventional very short-term trading can compare the approach with the Forex Scalping Strategies guide.
How Algorithmic Forex Trading Works
Step 1: Define the Trading Idea
Every algorithm starts with a trading hypothesis.
The strategy might attempt to follow trends, trade pullbacks, capture breakouts, exploit mean reversion, respond to economic events, or manage execution according to predefined rules.
Step 2: Convert the Idea Into Objective Rules
The strategy must be translated into conditions that software can evaluate.
A statement such as "buy when the market looks strong" is too subjective. An algorithm requires measurable definitions of strength, entry, invalidation, position size, and exit.
Step 3: Obtain Market Data
The system receives data such as:
- Bid and ask prices.
- Open, high, low, and close.
- Spread.
- Tick activity.
- Broker or venue-specific volume where available.
- Economic data where the strategy requires it.
Step 4: Generate the Trading Signal
The algorithm evaluates current information against its predefined conditions.
No position is opened until the required rules are satisfied.
Step 5: Apply Risk Controls
Before sending the order, the system can check position size, current exposure, correlated trades, maximum daily loss, permitted leverage, spread conditions, and other risk limits.
Step 6: Execute the Order
The system sends the required market, limit, stop, or other supported order to the broker or execution venue.
The requested price and actual execution price can differ because of spread changes, latency, liquidity, and slippage.
Step 7: Manage the Position
The algorithm can monitor stops, targets, trailing rules, time-based exits, volatility changes, and other trade-management conditions.
Step 8: Record and Monitor the Result
A production trading system should record orders, fills, rejected orders, spread, slippage, errors, exposure, and strategy decisions.
Automation removes the need to perform each action manually. It does not remove the need for supervision.
Core Components of an Algorithmic Trading System
Strategy Logic
Strategy logic defines when the system can trade.
This includes the market condition, entry trigger, filters, invalidation rules, and exit conditions.
Market Data
The trading logic is only as reliable as the information supplied to it.
Incorrect prices, missing candles, inconsistent timestamps, duplicate data, or a different broker feed can materially affect both backtests and live signals.
Risk Engine
The risk engine determines whether a valid trading signal is permitted to become an actual position.
It can apply:
- Maximum position size.
- Maximum account exposure.
- Currency exposure limits.
- Maximum daily or session loss.
- Maximum number of open positions.
- Spread filters.
- Volatility limits.
Execution Engine
The execution engine converts a trading decision into an order and manages interaction with the broker or trading venue.
Execution rules can determine order type, acceptable slippage, timing, retry behavior, and what happens when an order is rejected or only partly executed where partial fills are possible.
Monitoring and Logging
Every important system action should be recorded.
Logs help distinguish a losing strategy from a software bug, connectivity issue, incorrect data feed, or execution problem.
Main Algorithmic Trading Strategies
Trend-Following Algorithms
Trend-following systems attempt to participate in sustained directional price movement.
Rules can use price structure, moving averages, breakouts, momentum measurements, or combinations of these methods.
Trend-following strategies generally accept that several small losing or flat trades can occur before a larger directional move develops.
Mean-Reversion Algorithms
Mean-reversion strategies are built around the idea that certain price deviations can eventually move back toward a reference level or statistical average.
The main risk is treating every large deviation as temporary. Markets can move into persistent trends where the historical mean itself changes.
Breakout Algorithms
Breakout systems monitor support, resistance, previous highs and lows, consolidations, volatility ranges, or other defined boundaries.
The system can enter after price crosses the boundary, closes beyond it, or successfully retests the broken area.
False-breakout logic should be incorporated separately. The site's Breakout Trading guide covers the underlying price structure in more detail.
Swing Trading Algorithms
Algorithmic trading can also operate over several days or weeks.
A swing system can evaluate higher-timeframe trends, pullbacks, support and resistance, volatility, and event risk before entering a position.
The longer holding-period framework is covered separately in the Swing Trading Strategies guide.
Statistical Arbitrage
Statistical arbitrage uses quantitative relationships between instruments to identify deviations from historically observed behavior.
A strategy can monitor spreads, correlations, cointegration relationships, or other statistical variables.
Historical relationships can break down, so a statistical deviation is not proof that the relationship will revert.
Cross-Market or Venue Arbitrage
Arbitrage strategies attempt to exploit temporary price differences between related markets or venues.
Apparent price differences are not automatically risk-free profit. Execution latency, trading costs, quote changes, liquidity, and the possibility that only one side of the trade fills can remove the expected advantage.
Market-Making Algorithms
Market-making systems quote both sides of a market and manage inventory while attempting to earn part of the bid-ask spread.
Institutional market making is fundamentally different from a retail trader simply placing frequent buy and sell orders. It requires appropriate market access, execution infrastructure, inventory controls, and sophisticated risk management.
Event-Driven Algorithms
Event-driven strategies react to scheduled or unscheduled information.
Rules can incorporate economic releases, central bank decisions, or other machine-readable events.
Event trading carries substantial execution risk because spreads, liquidity, and price can change extremely quickly around important announcements.
Execution Algorithms and Trading Algorithms Are Different
Strategy Algorithms
A strategy algorithm decides whether market exposure should be opened or closed.
Execution Algorithms
An execution algorithm focuses on how an existing order should be completed.
Large orders can be divided into smaller pieces or distributed across time to control market impact and execution cost.
TWAP
A Time-Weighted Average Price approach distributes execution across defined time intervals.
Its purpose is order execution rather than predicting market direction.
VWAP Considerations in Forex
Volume-Weighted Average Price requires volume information.
Spot Forex is decentralized and does not have one centralized total-market volume figure. A Forex implementation therefore depends on the particular venue or data feed supplying its volume information.
Technical Indicators in Algorithmic Forex Trading
Moving Averages
Moving averages can be converted easily into objective rules.
Algorithms can evaluate slope, price position, crossovers, or the relationship between several averages.
Moving averages remain lagging calculations derived from historical price.
Relative Strength Index
RSI measures momentum by comparing recent gains with recent losses.
An algorithm can use levels such as 70 and 30, while those thresholds should not automatically be coded as sell and buy commands. Strong trends can remain overbought or oversold for extended periods.
MACD
MACD evaluates the relationship between shorter-term and longer-term exponential moving averages.
Algorithms can use Signal-Line crosses, zero-line position, histogram behavior, or divergence as part of a broader ruleset.
Bollinger Bands
Bollinger Bands provide information about price location relative to a moving average and recent standard deviation.
Touching an outer band is not inherently a reversal signal. A system should define whether the strategy is trading mean reversion, volatility expansion, or another specific behavior.
Average True Range
ATR measures recent price range and can help normalize stops, targets, filters, and position-sizing rules according to volatility.
Price Structure
Algorithms do not need to rely entirely on conventional indicators.
Swing highs, swing lows, breakouts, ranges, previous-session levels, candle ranges, and other price-action features can all be converted into objective numerical rules.
Forex Data Matters More Than It Looks
Spot Forex Is Decentralized
Spot Forex does not trade through one centralized exchange containing every quote and transaction.
Different brokers and liquidity sources can therefore show small differences in bid, ask, spreads, highs, lows, and tick activity.
Tick Volume Is Not Global Transaction Volume
Many retail Forex platforms provide tick volume, which measures the number of price changes during a period.
It can provide useful relative activity information while it is not a complete measure of every transaction occurring throughout the global spot Forex market.
Bid and Ask Data Matter
A strategy tested only on midpoint or candle-close prices can ignore the spread that exists during actual execution.
This becomes particularly important for short-term systems with small targets.
Time Zones and Session Boundaries Matter
Daily candles and session-based indicators can differ depending on the broker's server time and data construction.
Strategy development should use consistent timestamps between research and live deployment.
Backtesting an Algorithmic Trading Strategy
What Backtesting Does
Backtesting applies the strategy's rules to historical data to estimate how the system would have behaved in previous market conditions.
A backtest is an analytical tool rather than proof of future profitability.
Use the Same Rules Throughout the Test
Entry, exit, position-size, and risk rules should be defined before the results are evaluated.
Changing the rules each time an unattractive historical trade appears creates curve fitting rather than objective testing.
Include Spread
Realistic spread assumptions should be incorporated into historical testing.
Using the minimum advertised broker spread across every period can materially overstate results.
Include Commission
Commission-based accounts need round-trip trading costs reflected in the backtest.
Model Slippage
Execution at the exact historical signal price cannot always be assumed.
Slippage becomes especially important for short-term, breakout, and event-driven algorithms.
Include Overnight Financing Where Relevant
Strategies holding Forex positions across rollover can incur or receive swap adjustments.
These can become meaningful for systems with longer holding periods.
The Main Backtesting Errors to Avoid
Look-Ahead Bias
Look-ahead bias occurs when a backtest uses information that would not have been available at the time of the simulated trade.
One example is using the final value of a candle before that candle had actually closed.
Overfitting
Overfitting occurs when strategy parameters are adjusted too closely to historical noise.
The resulting system can look exceptionally strong on past data and fail when exposed to new market conditions.
Excessive Parameter Optimization
Testing hundreds or thousands of parameter combinations increases the probability of finding a historically attractive result by chance.
Robust systems should not depend on one unusually precise parameter combination.
Ignoring Transaction Costs
A system with a small theoretical edge can lose that edge once realistic spread, commission, financing, and slippage are included.
Testing Only One Market Condition
A trend-following system can perform well during a strongly directional historical period and poorly during extended ranges.
Testing should cover several volatility and market-regime environments.
Out-of-Sample and Walk-Forward Testing
Separate Development From Validation
Part of the historical data can be used to develop the system, while another untouched section is reserved for evaluation.
This helps determine whether the strategy can operate on data it was not optimized to fit.
Walk-Forward Analysis
Walk-forward testing repeatedly develops or adjusts a strategy on one historical window and evaluates it on a later unseen period.
This provides a more realistic robustness test than optimizing one dataset repeatedly.
Forward Testing
A strategy can also be run in a simulated or demo environment using live market data before real capital is committed.
Forward testing can reveal data, execution, timing, and infrastructure problems that are difficult to reproduce in a historical test.
Execution Costs in Algorithmic Forex Trading
Spread
The spread creates an immediate trading cost when a position is opened.
Strategies with high turnover are particularly sensitive to changes in spread.
Commission
Raw-spread accounts can charge a separate commission on each transaction.
Slippage
Slippage occurs when the actual fill differs from the expected execution price.
Both positive and negative slippage are possible.
Latency
Latency is the delay between receiving information, generating an order, transmitting it, and receiving an execution response.
It matters most for strategies whose expected edge exists for very short periods.
Liquidity
Market depth changes throughout the trading day and around major events.
A backtest based only on displayed historical prices can underestimate the difficulty of executing certain positions during fast conditions.
Risk Management in Algorithmic Trading
Position Size Should Follow the Stop
The system should first establish the price level that invalidates the setup.
Position size is then calculated so that the distance from entry to stop corresponds with the intended monetary risk.
Account risk and stop distance are separate variables.
Maximum Position Size
A system can place an absolute limit on the size of any single position regardless of the generated signal.
Maximum Portfolio Exposure
Several algorithms can produce positions at the same time.
Total exposure should therefore be controlled at account or portfolio level rather than only trade by trade.
Currency Correlation
Long EUR/USD and long GBP/USD can both create substantial exposure to US dollar weakness.
An algorithm that treats these positions as completely independent can underestimate portfolio risk.
Maximum Daily Loss
A predefined daily or session loss limit can stop new trading after losses exceed an acceptable amount.
This can protect the account when unusual market conditions, data problems, or software errors cause the strategy to behave differently from expectation.
Maximum Order Frequency
Limits on message or order frequency can reduce the damage caused by a programming error that repeatedly submits unintended orders.
Leverage Limits
An automated system should define the maximum exposure it is permitted to create.
Maximum leverage offered by the broker is a platform limit rather than an appropriate position-sizing target.
Kill Switches and Emergency Controls
Why a Kill Switch Matters
An automated system can continue submitting orders until it is explicitly stopped.
A rapid shutdown mechanism allows trading to be disabled when the algorithm, data feed, broker connection, or market behaves unexpectedly.
Conditions That Can Trigger Shutdown
Emergency rules can respond to:
- Maximum account loss.
- Excessive order frequency.
- Abnormal spread.
- Missing or corrupted data.
- Repeated rejected orders.
- Unexpected position size.
- Connectivity problems.
- Strategy behavior outside predefined limits.
Automation Still Requires Human Oversight
A well-designed algorithm reduces the need for manual decision-making during individual trades.
Someone still needs responsibility for monitoring system health, reviewing abnormal behavior, deploying code updates, and deciding when the strategy should be disabled.
Algorithmic Trading and Psychology
Automation Does Not Eliminate Emotion Completely
Algorithms follow their programmed rules without fear or greed.
The human operator can still react emotionally by disabling a system after normal losses, increasing risk after gains, changing parameters impulsively, or overriding trades.
Strategy Drawdowns Need to Be Expected
Even a historically profitable strategy can experience losing periods.
The trader should understand the expected drawdown characteristics before live deployment rather than assuming automation will produce smooth results.
Avoid Constant Parameter Changes
Changing the algorithm every time recent performance deteriorates can convert a systematic strategy into discretionary decision-making.
Strategy changes should come from structured research and validation.
Fundamental Data in Algorithmic Forex Trading
Economic Indicators Can Be Programmed
Algorithmic Forex systems do not have to rely exclusively on technical indicators.
Economic data such as inflation, employment, GDP, and interest-rate decisions can be incorporated when a reliable structured data source is available.
Actual Results Versus Expectations
Currency markets often react to the difference between the published result and market expectations.
An economic number cannot be classified as bullish or bearish without considering what traders had already priced in.
Central Bank Events
Algorithms can restrict trading before monetary-policy announcements or implement separate rules specifically designed for event volatility.
News conditions should not be assumed to have the same spread, slippage, and liquidity characteristics as normal trading.
Unscheduled Events Remain a Risk
Geopolitical developments and unexpected policy announcements cannot always be anticipated by an economic calendar.
Risk controls therefore need to remain active even when no scheduled event is expected.
Infrastructure for Algorithmic Forex Trading
Trading Platform or API
The system needs a method for receiving market information and sending orders.
This can be provided through a trading platform, programming interface, or broker API depending on the implementation.
Programming Environment
Languages commonly used in quantitative and automated trading include Python, C++, platform-specific languages, and other general-purpose programming environments.
The appropriate language depends on execution requirements, platform support, data processing, and development needs.
Reliable Internet and Connectivity
Connectivity failures can prevent the algorithm from receiving prices or managing open positions.
Systems should define what happens when connectivity is lost rather than assuming the connection will remain available continuously.
VPS and Remote Hosting
Remote servers can keep automated systems running without depending on a trader's personal computer.
Hosting closer to execution infrastructure can also reduce latency for strategies that are sensitive to communication delays.
Lower latency improves execution infrastructure. It does not create a profitable strategy by itself.
Redundancy and Recovery
More advanced systems can use backup data, alternative connectivity, monitoring services, and recovery procedures to reduce operational risk.
Common Algorithmic Trading Mistakes
Automating an Untested Strategy
Writing code does not transform an unverified trading idea into a robust system.
Strategy logic should be tested independently from the quality of the programming.
Assuming Backtested Profit Equals Future Profit
Historical results describe how the rules behaved on previous data.
Future market conditions can differ materially.
Ignoring Spread and Slippage
Small execution costs can completely change a high-turnover strategy.
Excessive Optimization
Optimizing every parameter until historical drawdown disappears usually produces a fragile strategy rather than a perfect one.
Using Too Many Indicators
Adding more indicators does not automatically add independent information.
Several indicators can be derived from the same historical prices and effectively measure similar behavior.
Ignoring Data Quality
Missing or inconsistent historical data can create signals that would not have existed in live markets.
Assuming Automation Removes Risk
Automation can execute errors consistently and rapidly.
Software, connectivity, data, execution, market, model, and leverage risks remain.
Deploying Full Size Immediately
Historical and simulated performance cannot reproduce every live execution condition.
Initial live deployment can therefore use limited exposure while execution and monitoring behavior are evaluated.
Moving From Backtest to Live Trading
Stage 1: Historical Research
Develop the initial rules and test them across suitable historical periods.
Stage 2: Out-of-Sample Testing
Evaluate the strategy on data that was not used to select its parameters.
Stage 3: Forward Testing
Run the system on live market data without meaningful capital exposure.
Verify signal timing, data handling, broker communication, and trade management.
Stage 4: Limited Live Deployment
Begin with controlled position sizes and monitor differences between expected and actual execution.
Stage 5: Ongoing Monitoring
Continue comparing live results with the strategy's expected behavior.
A production algorithm should not be treated as finished software that can be ignored indefinitely.
Artificial Intelligence and Machine Learning
Machine Learning Can Expand the Model
Machine-learning systems can process larger sets of variables than many traditional rule-based strategies.
Inputs can include price, volatility, spreads, economic variables, market activity, and other structured information.
Prediction Accuracy Is Not Guaranteed
Using AI does not mean future prices can be predicted with high accuracy.
A model can perform well on historical data because of overfitting, data leakage, changing relationships, or random statistical patterns.
Explainability and Monitoring Matter
More complex models can become harder to diagnose when performance changes.
Traders still need controls for position size, abnormal outputs, changing data, and system failure.
Simple Models Can Remain Useful
A complicated machine-learning model is not automatically superior to a transparent rules-based strategy.
The relevant question is whether the system produces sufficiently robust results after realistic costs and risk.
High-Frequency Trading and Retail Algorithms
HFT Is Infrastructure Intensive
Professional high-frequency strategies can depend on specialized hardware, direct market connectivity, co-location, highly optimized software, and detailed market microstructure data.
Retail Automation Has Different Constraints
Retail Forex algorithms generally operate through broker platforms or APIs and face different latency, liquidity, spread, and data conditions.
A conventional Expert Advisor or automated retail strategy should not be described as institutional HFT simply because it trades frequently.
Speed Cannot Replace an Edge
Faster execution can help preserve an existing edge.
It cannot transform random entry logic into a profitable strategy.
Building a Complete Algorithmic Trading Plan
Define the Market
Specify which currency pairs and trading sessions the strategy can trade.
Define the Data
Identify the exact price feed, timeframe, spread data, economic data, and other inputs required.
Define the Market Condition
State whether the algorithm operates during trends, ranges, breakouts, volatility expansions, mean reversion, or another measurable regime.
Define the Entry
Program the exact conditions that permit a position to be opened.
Define Invalidation
Identify the market condition or price structure that makes the original trade thesis invalid.
Define Position Size
Calculate exposure according to stop distance and the system's monetary risk limits.
Define the Exit
Set profit-taking, stop, trailing, and time-based exit rules before deployment.
Define Portfolio Limits
Control aggregate leverage, currency correlation, open positions, and maximum account exposure.
Define Execution Rules
Specify acceptable spread, slippage assumptions, order type, and handling of rejected or incomplete orders.
Define Emergency Controls
Establish the conditions that stop new trading or shut the algorithm down.
Define the Testing Process
Document historical testing, out-of-sample validation, forward testing, and live-deployment procedures.
Define the Review Process
Establish how live performance, system errors, execution quality, drawdown, and strategy behavior will be reviewed.
Future of Algorithmic Forex Trading
Greater Automation of Risk Controls
Trading systems can increasingly combine entry logic with portfolio-level exposure limits, real-time monitoring, automated shutdown conditions, and execution analytics.
Better Data Processing
Improvements in computing infrastructure make it easier to process larger datasets and test strategies across broader historical samples.
More Emphasis on Robustness
The most useful development is not simply producing more complicated models.
Greater attention to out-of-sample testing, realistic execution, data integrity, and model stability can improve how algorithmic strategies are evaluated.
Continued Importance of Human Oversight
More automation increases the importance of monitoring how systems behave when market conditions, data, infrastructure, or software fail to operate as expected.
Conclusion
Algorithmic Forex trading uses programmed rules to analyze markets, generate signals, execute orders, manage positions, and control risk. The technology can improve consistency, process information quickly, and automate repetitive trading decisions.
Automation does not guarantee profitability. The performance of an algorithm depends on the quality of the trading strategy, market data, testing methodology, execution, transaction costs, position sizing, risk limits, and system monitoring.
Historical backtests need realistic spreads, commissions, slippage, financing costs, and strict controls against look-ahead bias and overfitting. Out-of-sample and forward testing provide additional evidence before meaningful live capital is exposed.
Risk controls should extend beyond individual stop-loss orders. Position limits, portfolio exposure, currency correlation, leverage controls, loss limits, order-frequency limits, monitoring, and emergency shutdown procedures are all important parts of a complete automated system.
Used this way, algorithmic trading is not a shortcut around trading uncertainty. It is a structured method for converting a defined trading process into software that can be tested, monitored, and executed consistently.
FAQs About Algorithmic Forex Trading
What is algorithmic trading in Forex?
Algorithmic Forex trading uses computer programs to analyze market information and perform trading actions according to predefined rules.
Is algorithmic trading the same as automated trading?
The terms are often used interchangeably. Algorithmic trading can automate signal generation, order execution, position management, or the entire trading process.
Is algorithmic trading the same as HFT?
No. High-frequency trading is a specialized type of algorithmic trading focused on extremely short time horizons and low-latency execution. Many algorithmic strategies trade much less frequently.
Can a Forex algorithm trade automatically?
Yes. A fully automated system can generate signals, calculate position size, place orders, manage stops and targets, and close positions according to its programmed rules.
Does algorithmic trading remove emotion?
Algorithms execute their programmed rules without emotion, while the trader can still make emotional decisions when designing, modifying, overriding, or disabling the system.
Does a profitable backtest mean an algorithm will make money?
No. Historical results can be affected by overfitting, unrealistic costs, poor data, look-ahead bias, or market conditions that do not repeat.
What is overfitting in algorithmic trading?
Overfitting occurs when a strategy is adjusted too closely to historical data and captures random past behavior rather than a relationship that remains useful on new data.
Why is out-of-sample testing important?
Out-of-sample testing evaluates the strategy on historical data that was not used to develop or optimize its rules, providing a stronger test of robustness.
What trading costs should an algorithm include?
Relevant costs can include spread, commission, slippage, overnight financing, and other execution-related charges.
Can algorithms use technical indicators?
Yes. Moving averages, RSI, MACD, Bollinger Bands, ATR, price structure, and many other calculations can be converted into objective algorithmic rules.
Can algorithms use fundamental data?
Yes. Economic releases, interest-rate decisions, and other structured fundamental information can be incorporated when the strategy has a reliable data source and clearly defined rules.
What is a kill switch in algorithmic trading?
A kill switch is a mechanism that rapidly stops automated trading when predefined risk, technical, or operational conditions are breached.
Is AI required for algorithmic trading?
No. Many algorithmic strategies use straightforward mathematical rules. Machine learning is one possible approach rather than a requirement.
Can AI predict Forex prices accurately?
AI can analyze large datasets and identify statistical relationships, while it does not guarantee accurate future price predictions. Results depend on data, validation, costs, market changes, and model design.
Does faster execution make an algorithm profitable?
No. Faster execution can help preserve an existing trading edge, while it cannot create an edge when the underlying strategy has no positive expectancy.
Published by:
Daniel Carter