Modern trading is increasingly influenced by data, mathematics, statistics and technology.
Professional traders and quantitative analysts do not simply look at charts and guess where prices may move next. They develop hypotheses, collect market data, define trading rules, test those rules historically, measure risk and determine whether a strategy remains meaningful after transaction costs and changing market conditions.
This is the foundation of quantitative trading.
An advanced quant trading course should therefore teach much more than technical indicators or Python syntax.
It should show learners how to move from an idea to a properly researched systematic trading strategy.
That process may include:
Market data → Statistical analysis → Strategy logic → Python implementation → Backtesting → Risk management → Performance evaluation → Validation.
For students, finance professionals, quantitative-finance learners and traders who want a more analytical approach to markets, advanced quant trading brings together finance, mathematics, statistics, programming and risk management.
This guide explains what an advanced quantitative trading course should cover, how Python and machine learning can be used, why backtesting needs to be treated carefully and what skills matter when developing systematic trading strategies.
What Is Quantitative Trading?
Quantitative trading uses mathematical, statistical and computational methods to develop and evaluate trading strategies.
Instead of making decisions entirely through discretion, a quantitative trader defines rules that can be tested objectively.
For example, a strategy might ask:
Does momentum persist after a strong price move?
Do two historically related securities converge after temporarily moving apart?
Does volatility provide information about future risk?
Can market regimes be identified statistically?
Can a portfolio be constructed systematically based on risk and return?
These questions can then be converted into models and tested using historical market data.
Quantitative trading therefore combines several disciplines:
Financial markets
Probability
Statistics
Programming
Time-series analysis
Portfolio management
Risk management
Machine learning
Peaks2Tails' quantitative-finance learning material similarly combines mathematics, statistics, Python, financial modelling, portfolio optimisation and trading applications within a broader quant ecosystem.
Advanced Quant Trading Is Not the Same as Technical Analysis
Technical analysis can form part of a trading system.
Quantitative trading is broader.
Traditional technical analysis may involve indicators such as:
Moving averages
RSI
MACD
Support and resistance
Bollinger Bands
A quantitative approach asks additional questions.
For example:
How often did the signal work historically?
How was the entry rule defined?
What happens after transaction costs?
How does the strategy behave during different market regimes?
What is the maximum drawdown?
How sensitive is performance to parameter changes?
Does the strategy work on data that was not used during development?
These questions transform a trading idea into a research problem.
Peaks2Tails currently lists technical analysis, intraday and options trading alongside quantitative finance and model-based Excel/Python implementation in its broader course ecosystem.
Mathematics Required for Quant Trading
You do not need extremely advanced mathematics to begin quantitative trading.
But serious quantitative work eventually requires mathematical foundations.
Important topics include:
Algebra
Functions
Logarithms
Probability
Statistics
Linear algebra
Optimisation
More advanced areas may include:
Calculus
Stochastic processes
Numerical methods
The required depth depends on the strategy.
A simple moving-average system requires less mathematics than an options-market-making or stochastic-volatility model.
The mistake is assuming every quant strategy needs complicated mathematics.
Complexity should serve the problem.
Probability for Quant Traders
Trading operates under uncertainty.
No serious strategy predicts every market movement correctly.
Probability allows traders to think in distributions rather than certainty.
Important concepts include:
Probability distributions
Expected value
Variance
Conditional probability
Correlation
Covariance
Suppose a strategy wins only 45% of trades.
That does not automatically make it a bad strategy.
If average winning trades are significantly larger than average losing trades, the strategy can still have positive expected value.
Quantitative trading therefore focuses on the distribution of outcomes rather than whether individual predictions are correct.
Statistics for Quantitative Trading
Statistics is one of the most important foundations of quant trading.
Important areas include:
Mean
Variance
Standard deviation
Correlation
Regression
Hypothesis testing
Confidence intervals
Statistical significance
Time-series analysis
Statistics helps traders distinguish potential patterns from random noise.
Financial markets contain enormous amounts of noise.
Without statistical discipline, traders can easily discover patterns that never actually existed.
Python for Quant Trading
Python is one of the most useful languages for quantitative trading research.
It can be used for:
Market-data processing
Strategy development
Backtesting
Statistical analysis
Portfolio modelling
Risk analysis
Machine learning
Visualisation
Important libraries include:
Pandas
Useful for:
Time-series data
Data cleaning
Market-price datasets
Strategy signals
NumPy
Useful for:
Numerical calculations
Arrays
Matrix operations
Simulation
Matplotlib
Useful for:
Price charts
Strategy equity curves
Drawdown analysis
Risk visualisation
Statsmodels
Useful for:
Regression
Time-series modelling
Statistical testing
Scikit-learn
Useful for:
Machine learning
Classification
Regression
Clustering
Model validation
Python allows a trader to take a strategy idea and test it systematically across large historical datasets.
Peaks2Tails currently emphasises hands-on Excel and Python implementation across its quantitative-finance programmes.
Market Data for Quant Trading
Every quantitative strategy starts with data.
Data may include:
Open price
High price
Low price
Close price
Volume
Bid and ask information
Derivatives data
Fundamental information
Macroeconomic variables
More advanced strategies may use alternative data.
But more data does not automatically create better strategies.
The data must be:
Accurate
Consistent
Properly timestamped
Relevant to the strategy
Bad data produces misleading backtests.
Data Cleaning for Trading Strategies
Raw market data often contains problems.
Examples include:
Missing observations
Duplicate rows
Incorrect timestamps
Corporate actions
Outliers
Different trading calendars
Python can help automate these checks.
Data cleaning matters because a single incorrect price can create a false trading signal or distort calculated returns.
Quantitative trading begins with good data engineering.
Returns Instead of Prices
Quantitative analysts frequently work with returns rather than raw asset prices.
A simple return may be calculated as:
Return = (Current Price − Previous Price) / Previous Price
Returns make it easier to compare assets with different price levels.
They are also used in calculations involving:
Volatility
Portfolio returns
Correlation
Sharpe ratio
Risk models
Understanding return calculations is one of the first practical steps in quantitative trading.
What Is a Trading Signal?
A signal is a measurable condition used to inform a trading decision.
For example:
Buy when short-term momentum becomes sufficiently positive.
Sell when momentum turns negative.
Or:
Buy an asset when its price deviates significantly below a statistical relationship.
Signals may be based on:
Price
Volume
Volatility
Momentum
Fundamentals
Statistical relationships
Machine-learning outputs
A signal is not a complete strategy.
The strategy also needs:
Entry rules
Exit rules
Position sizing
Risk limits
Transaction-cost assumptions
Momentum Trading Strategies
Momentum strategies attempt to benefit from persistence in market movements.
A simplified momentum hypothesis is:
Assets that have recently performed strongly may continue to outperform for some period.
Possible indicators include:
Historical returns
Moving-average crossovers
Breakouts
Relative strength
But a quantitative trader should never assume that a familiar indicator creates a profitable system.
The strategy must be tested objectively.
Mean-Reversion Strategies
Mean-reversion strategies assume that some financial variables tend to move back toward a historical or statistical average after extreme deviations.
Possible applications include:
Asset prices
Spreads
Relative valuations
Volatility
Mean reversion forms the basis of many systematic trading ideas.
But the historical mean itself can change.
That creates model risk.
Pairs Trading
Pairs trading is a common quantitative strategy.
Suppose two securities have historically maintained a strong relationship.
If that relationship temporarily diverges, a strategy may:
Buy one security.
Sell the other.
Then close the position if the relationship converges.
A proper pairs-trading strategy should examine more than correlation.
Learners may need to understand:
Cointegration
Spread construction
Z-scores
Entry thresholds
Exit thresholds
Stop-loss conditions
Peaks2Tails' quantitative-finance material currently includes pair-trading and portfolio applications as part of its broader quant-learning pathway.
Statistical Arbitrage
Statistical arbitrage uses statistical relationships to identify potential trading opportunities.
Strategies can involve:
Relative-value relationships
Mean reversion
Factor models
Portfolio construction
The term "arbitrage" can be misleading.
Many statistical-arbitrage strategies still contain considerable market and model risk.
Historical relationships can break.
What Is Backtesting?
Backtesting evaluates how a trading strategy would have behaved using historical market data.
A basic workflow is:
Define the strategy.
Obtain historical data.
Generate signals.
Apply trading rules.
Calculate profit and loss.
Include trading costs.
Evaluate performance.
Backtesting is essential because intuition alone does not tell you whether a strategy has historically worked.
But backtesting is also one of the easiest areas to misuse.
Peaks2Tails' current quant-finance content explicitly warns that historical backtests can mislead when researchers ignore overfitting, transaction costs, liquidity or future information.
A Good Backtest Is Not Proof of Future Profit
This point needs to be stated clearly.
A beautiful historical equity curve proves only that a set of rules performed well on the historical data and assumptions used in the test.
It does not prove that the strategy will perform similarly in live markets.
Markets change.
Competition changes.
Liquidity changes.
Relationships disappear.
Backtesting should therefore be used for research and validation, not as evidence of guaranteed future profit.
Peaks2Tails' own recent quant-finance material makes the same distinction and emphasises realistic evaluation rather than profit promises.
Look-Ahead Bias
Look-ahead bias occurs when a backtest accidentally uses information that was not available when the historical trade supposedly occurred.
For example, suppose today's closing price is used to create a signal that is assumed to have executed before today's market close.
That strategy is using future information.
Its backtest is invalid.
This problem can be surprisingly easy to introduce.
Survivorship Bias
Suppose you test a strategy using only companies that exist today.
Companies that failed, delisted or disappeared may be missing.
That can make historical performance appear better than reality.
This is survivorship bias.
Good quantitative research should consider whether the historical dataset accurately represents the investable universe that actually existed.
Overfitting in Trading Strategies
Overfitting happens when a strategy is optimised too closely to historical data.
For example, you test hundreds of indicator combinations and eventually discover one that produced exceptional historical returns.
The result may simply reflect luck.
The strategy has effectively memorised historical noise.
It may fail immediately in live markets.
Overfitting is one of the largest dangers in quantitative trading.
In-Sample vs Out-of-Sample Testing
A better research process separates data.
In-Sample Data
Used to develop the strategy.
Out-of-Sample Data
Used to test how the strategy performs on unseen periods.
The out-of-sample period provides a more realistic test of whether the strategy generalises.
Peaks2Tails' machine-learning and finance material similarly stresses chronological testing and out-of-sample evaluation for time-series problems.
Walk-Forward Testing
Walk-forward testing provides a more realistic way of evaluating time-series strategies.
For example:
Train the model on one period.
Test on the next period.
Move forward.
Train again.
Test again.
This mimics how strategies might actually be updated through time.
It is particularly important for machine-learning trading models.
Transaction Costs
Transaction costs can destroy a strategy that looks profitable before costs.
Important costs can include:
Brokerage
Taxes
Exchange charges
Bid-ask spread
Slippage
A strategy generating hundreds of small trades may look excellent before costs and useless after realistic implementation assumptions.
A serious advanced quant trading course must include transaction costs.
Slippage
Slippage is the difference between the expected trade price and the price actually achieved.
It becomes particularly important when:
Markets move quickly
Liquidity is weak
Position sizes are large
Strategies trade frequently
Ignoring slippage creates unrealistic backtests.
Liquidity Risk
Not every theoretical trade can be executed efficiently.
A strategy may appear profitable using historical prices but fail because:
Trading volume is insufficient
Bid-ask spreads are wide
Large orders move the market
Advanced quant trading therefore requires understanding execution as well as prediction.
Measuring Trading Strategy Performance
Total return alone is not enough.
A strategy generating 30% return with enormous drawdowns may be less attractive than one generating lower returns with more stable risk.
Useful measures include:
CAGR
Measures annualised growth.
Volatility
Measures variability of returns.
Sharpe Ratio
Measures return relative to volatility.
Maximum Drawdown
Measures the largest decline from a previous peak.
Win Rate
Measures the proportion of profitable trades.
But win rate alone can be misleading.
A strategy can have a low win rate and still be profitable if winning trades are significantly larger than losses.
Peaks2Tails' recent quant-bootcamp content recommends evaluating trading strategies using return, volatility, drawdown, win rate, risk-adjusted performance and robustness rather than relying on one headline metric.
Position Sizing
A trading signal tells you when to trade.
Position sizing determines how much to trade.
This can have a major effect on strategy risk.
Approaches may include:
Fixed position sizing
Percentage of capital
Volatility-based sizing
Risk-parity sizing
Even a strategy with good signals can fail when position sizing is poorly controlled.
Stop-Loss Rules
Stop-loss rules attempt to control losses when a position moves unfavourably.
But stop losses need to be tested rather than chosen arbitrarily.
A stop that is too tight may close positions because of normal market noise.
A stop that is too wide may create excessive losses.
Quantitative strategy development involves testing the full system, not individual components in isolation.
Portfolio-Level Quant Trading
Professional quantitative trading frequently involves multiple positions rather than one strategy on one asset.
Portfolio construction then becomes important.
Analysts may examine:
Asset correlations
Risk contribution
Capital allocation
Diversification
Portfolio volatility
A portfolio of individually attractive strategies can still produce poor results if all strategies fail under the same conditions.
Portfolio Optimisation
Quant traders can use mathematical optimisation to determine portfolio allocations.
Possible approaches include:
Mean-variance optimisation
Risk parity
Minimum-variance portfolios
Maximum-Sharpe portfolios
But optimisation is sensitive to inputs.
Slight changes in expected returns or correlations can produce very different allocations.
Optimisation therefore requires careful interpretation.
Market Regimes
Markets do not behave the same way continuously.
Periods may be characterised by:
High volatility
Low volatility
Trending markets
Range-bound markets
Crisis conditions
A strategy that performs well during one regime may struggle during another.
Advanced quantitative research may therefore attempt to identify regime changes.
Volatility in Quant Trading
Volatility influences:
Position sizing
Stop levels
Portfolio risk
Option prices
Strategy behaviour
Common volatility tools include:
Historical volatility
Rolling volatility
EWMA
GARCH
Understanding volatility is essential because trading results cannot be evaluated only in terms of returns.
Risk changes through time.
Time-Series Analysis for Quant Trading
Financial observations occur sequentially.
This makes time-series analysis highly relevant.
Important concepts include:
Stationarity
Autocorrelation
Partial autocorrelation
AR models
MA models
ARIMA
Volatility models
These methods can be used for:
Return research
Volatility forecasting
Spread modelling
Risk analysis
Machine Learning for Quant Trading
Machine learning allows researchers to model complex relationships in financial data.
Possible applications include:
Market-regime classification
Return prediction research
Volatility forecasting
Signal development
Asset classification
Portfolio analysis
Common algorithms include:
Logistic regression
Decision trees
Random forests
Gradient boosting
XGBoost
Support Vector Machines
Neural networks
Peaks2Tails currently includes machine-learning applications within its Deep Quant Finance framework, including supervised models, PCA/clustering, neural networks, LSTM and introductory reinforcement-learning applications.
Feature Engineering
Machine-learning models depend on input features.
Trading features might include:
Returns
Momentum
Volatility
Volume
RSI
MACD
Moving averages
Spread measures
But generating hundreds of indicators does not guarantee better predictions.
Feature engineering should be guided by a financial hypothesis.
Otherwise the model may simply discover accidental historical relationships.
Random Forest for Quant Trading
Random Forest combines multiple decision trees.
It can potentially identify nonlinear relationships between market variables.
Possible applications include:
Direction classification
Regime detection
Signal classification
But a Random Forest model that performs strongly in-sample may still fail out of sample.
The same validation principles apply.
XGBoost for Trading
XGBoost is another popular machine-learning method.
It can work well with structured datasets.
Possible trading applications include:
Directional classification
Signal ranking
Volatility research
Again, predictive accuracy is not equivalent to trading profitability.
A model must still survive:
Transaction costs
Slippage
Market changes
Risk constraints
Deep Learning for Quant Trading
Deep learning uses multilayer neural networks to model complex relationships.
Possible architectures include:
Artificial Neural Networks
Recurrent Neural Networks
LSTM
Applications may include:
Time-series forecasting
Market classification
Volatility modelling
But deep learning introduces greater model complexity and greater overfitting risk.
It should generally come after strong foundations in statistics and traditional machine learning.
Reinforcement Learning in Trading
Reinforcement learning involves an agent learning by interacting with an environment and receiving rewards.
Potential applications include:
Trading decisions
Portfolio rebalancing
Execution strategies
This is an advanced field.
It should not be presented as a beginner shortcut to profitable automated trading.
Peaks2Tails' Deep Quant Finance material currently includes basic reinforcement-learning trading applications within a wider quantitative-finance curriculum rather than presenting reinforcement learning in isolation.
Quant Trading and Technical Indicators
Technical indicators can still be useful when approached quantitatively.
Examples include:
RSI
MACD
Moving averages
Bollinger Bands
The critical difference is methodology.
Instead of saying:
"RSI below 30 means buy."
A quantitative trader asks:
How was the threshold chosen?
Across which assets?
During which period?
What happens after costs?
Does it work out of sample?
This turns technical analysis into testable research.
Intraday Quant Trading
Intraday strategies operate within the trading day.
Possible variables include:
Price momentum
Volume
Breakouts
Volatility
Market structure
But intraday strategies face especially important implementation problems:
High transaction costs
Slippage
Execution speed
Market microstructure
Liquidity
Peaks2Tails' broader ecosystem currently includes technical-analysis and intraday-trading material, including screeners and systematic setup development.
Options and Quant Trading
Options create more complex quantitative opportunities because their values depend on several variables.
Important topics include:
Option pricing
Implied volatility
Delta
Gamma
Vega
Theta
Quantitative options strategies require understanding both:
market direction
and
option risk.
A trader can predict direction correctly and still lose because of volatility or time decay.
Monte Carlo Simulation
Monte Carlo simulation generates many possible financial scenarios.
Quant traders may use it for:
Risk analysis
Portfolio modelling
Option pricing
Scenario generation
Peaks2Tails' current quantitative and market-risk material includes Monte Carlo simulation as part of its applied modelling framework.
Risk Management in Quant Trading
Risk management is not separate from the strategy.
It is part of the strategy.
A complete trading system should define:
Maximum position size
Portfolio exposure
Stop rules
Loss limits
Volatility limits
Drawdown limits
A strategy producing attractive average returns can still be unusable if the downside risk is excessive.
Drawdown Analysis
Drawdown measures how much a strategy declines from a previous peak.
Suppose an account increases from ₹10 lakh to ₹15 lakh and later falls to ₹10.5 lakh.
That decline represents a substantial drawdown even though the account remains above its original value.
Drawdowns matter because traders must survive bad periods long enough for a strategy's potential edge to matter.
Strategy Robustness
A robust strategy should not collapse when a parameter changes slightly.
Suppose a moving-average strategy works spectacularly with 19 and 47 days but fails completely with 18 and 48.
That may indicate overfitting.
Robust strategies generally perform reasonably across a sensible range of parameters and market conditions.
Paper Trading
Before deploying a strategy with actual capital, traders may use paper trading.
Paper trading allows:
Signal verification
Execution testing
Workflow testing
Error detection
However, simulated trading does not perfectly replicate live execution.
Real markets introduce:
Slippage
Emotional pressure
Liquidity constraints
Operational issues
Paper trading is therefore useful but not equivalent to live performance.
Quant Trading Projects Learners Should Build
An advanced quant trading course should include practical projects.
Useful projects include:
Momentum Strategy
Develop and backtest a rule-based momentum model.
Mean-Reversion Strategy
Test whether extreme deviations historically reverted.
Pairs-Trading Model
Use statistical relationships to develop entry and exit rules.
Portfolio Optimisation
Construct and compare multiple allocation frameworks.
Volatility Model
Analyse and forecast market volatility.
Machine-Learning Strategy
Build and evaluate a classification or prediction model.
Walk-Forward Backtest
Test whether strategy performance remains stable through time.
The objective is not simply to produce a profitable-looking chart.
The learner should understand why each model behaves the way it does.
Step-by-Step Advanced Quant Trading Roadmap
A sensible progression begins with financial markets.
Understand:
Equities
Futures
Options
Market mechanics
Then build mathematics and statistics.
Study:
Probability
Distributions
Regression
Time series
Next, learn Python.
Focus on:
Pandas
NumPy
Matplotlib
Statsmodels
Then learn systematic-strategy research.
Build:
Signals
Entry rules
Exit rules
Position sizing
After that, learn backtesting properly.
Study:
Look-ahead bias
Survivorship bias
Transaction costs
Slippage
Out-of-sample testing
Then add portfolio and risk management.
Only after those foundations should learners move deeply into machine learning and AI-driven trading.
Advanced Quant Trading at Peaks2Tails
Peaks2Tails currently positions itself around quantitative finance and risk-modelling education with Excel and Python implementation. Its learning materials cover mathematical and statistical foundations, derivatives, portfolio optimisation, machine learning and quantitative trading applications.
Its current quant-finance content also discusses trading analytics and backtesting with explicit attention to:
Strategy development
Transaction costs
Liquidity
Look-ahead bias
Survivorship bias
Drawdowns
Risk-adjusted performance
Robustness
This is an important distinction because systematic trading education should teach learners how to challenge strategies rather than simply show profitable historical charts.
Peaks2Tails' machine-learning content also connects ML with feature engineering, strategy backtesting, portfolio optimisation, market-regime analysis and out-of-sample validation.
For someone searching for an advanced quant trading course, this broader combination of finance, statistics, Python, modelling and risk control is substantially more relevant than an indicator-only trading programme.
Who Should Learn Advanced Quant Trading?
This area can be relevant for:
Quantitative-finance students
Finance graduates
Economics graduates
Mathematics students
Statistics students
Engineering graduates
Traders
Portfolio analysts
Risk analysts
Python learners interested in finance
Data analysts moving into finance
Different backgrounds create different gaps.
An experienced trader may need statistics and Python.
An engineer may need financial-market knowledge.
A finance graduate may need programming and mathematics.
Career Areas Related to Quant Trading
Quantitative trading skills may contribute to work in:
Quantitative Research
Quantitative Analysis
Systematic Strategy Research
Portfolio Analytics
Market Risk
Financial Data Science
Trading Analytics
Actual role requirements vary considerably.
Some institutional quant roles require substantial mathematics, programming and formal academic preparation.
Completing one trading course does not automatically make someone professionally qualified for every quantitative role.
What to Look for in an Advanced Quant Trading Course
A serious programme should go substantially beyond indicators.
Look for coverage of:
Mathematics
Statistics
Python
Financial markets
Time-series analysis
Strategy development
Backtesting
Transaction costs
Portfolio risk
Model validation
More advanced programmes can also include:
Machine learning
Deep learning
Portfolio optimisation
Statistical arbitrage
Options analytics
Monte Carlo simulation
A course advertising only trading setups without discussing risk, biases and validation is not advanced quantitative trading.
Common Mistakes in Quant Trading
One of the biggest mistakes is optimising a strategy until the historical results look perfect.
Another is ignoring transaction costs.
A third is using future information accidentally.
Learners also often jump into machine learning before understanding basic statistics.
And many judge strategies purely on total profit while ignoring drawdown and risk.
These mistakes can make weak strategies look excellent.
Good quantitative research is designed to uncover those weaknesses, not hide them.
Does Machine Learning Automatically Improve Trading?
No.
Machine learning can identify complex relationships.
It can also identify meaningless historical patterns extremely efficiently.
Whether it improves a strategy depends on:
Data quality
Feature quality
Validation
Market stability
Transaction costs
Model design
The fact that a model uses AI tells you nothing about whether it will trade successfully.
Can Quant Trading Guarantee Profits?
No credible course can guarantee that a quantitative strategy will produce future profits.
Financial markets contain uncertainty.
Even historically strong models can fail when:
Market regimes change
Competition increases
Relationships disappear
Costs rise
Liquidity changes
A serious quant trading education should teach learners how to quantify uncertainty rather than pretend uncertainty can be removed.
Conclusion: Advanced Quant Trading Is Research, Not Gambling With Code
An advanced quant trading course should teach learners to approach markets as researchers.
That means starting with a hypothesis.
Collecting reliable data.
Defining precise rules.
Building the strategy in Python.
Testing it historically.
Including realistic transaction costs.
Checking for look-ahead and survivorship bias.
Testing the model on unseen data.
Measuring drawdowns and risk-adjusted returns.
And being willing to reject a strategy when the evidence is weak.
That final point matters.
Quantitative trading is not about forcing every idea to become profitable.
It is about developing a process capable of distinguishing potentially useful signals from random market noise.
Python helps automate that process.
Statistics provides the framework for testing it.
Machine learning expands the set of models available.
Risk management determines whether the strategy is survivable.
And financial-market understanding keeps the model connected to reality.
Peaks2Tails' current quantitative-finance ecosystem brings together these areas through statistics, Excel, Python, portfolio modelling, machine-learning applications and trading analytics, while its recent material explicitly stresses realistic backtesting and the limitations of historical performance.
For anyone searching for an advanced quant trading course, the goal should therefore not be to discover a secret indicator.
The goal should be to develop the ability to research, build, test, challenge and manage systematic trading strategies using data and quantitative methods.