Technology ecosystem
Practice market concepts and strategy workflows through technology designed for financial education.
Product statusStatus: Coming Soon
Conceptual illustration, not a product screenshot. Simulated environments do not reproduce all the characteristics of live markets.
What AlphaSync is
AlphaSync is being built as the technology and practice environment that sits alongside Vianmax Academy courses. It is a place to apply concepts, not a trading service.
Every capability starts from a course concept and a learning purpose, and is described here with its own status.
Learners apply ideas in a simulated environment and review their decisions before relying on them anywhere else.
Rules, decisions and reviews are recorded, so learning focuses on the quality of the process rather than on single outcomes.
Capabilities are mapped to the courses whose concepts they are designed to practise.
Learn → Practice
AlphaSync is designed to support each stage of the Academy method. It does not replace instruction, and practice results are not trading results.
Practice environment
Planned practice capabilities for applying chart reading, order handling and trading-plan discipline without real money.
A simulated environment for placing practice orders against market data, so course concepts can be applied without real money.
Learning purposeTurn a written trading plan into practice decisions, then review each one against the plan before relying on it.
Guided chart exercises for reading trend, structure, volume and indicators, aligned with course modules.
Learning purposePractise identifying structure and setups consistently, and write down the reasoning behind each reading.
Step through historical sessions bar by bar to practise decisions without knowing what comes next.
Learning purposeBuild patience and process discipline by making decisions in sequence, then reviewing them with hindsight.
Strategy development
Planned tools for expressing strategies as explicit rules and understanding what back-tests can and cannot show.
Express entry, exit and risk rules as an explicit, testable specification before writing any code.
Learning purposeDevelop algorithmic thinking: turning an idea into unambiguous rules that can be tested and reviewed.
Explore how a rule set behaved on historical data, including costs, slippage, look-ahead bias and over-fitting.
Learning purposeLearn what a back-test can and cannot tell you, and how to evaluate one critically.
Visualise payoff diagrams and how Greeks, volatility and time change an option position, using illustrative inputs.
Learning purposeUnderstand defined-risk structures and option sensitivities before studying them in live markets.
Back-tests and simulations use historical or illustrative data. They do not reproduce live-market execution and do not predict future results.
Research and analysis workflow
Planned workflow tools for recording hypotheses and decisions, and for learning how trading technology connects to data.
Record hypotheses, decisions, outcomes and reviews in one place, with prompts for risk and process.
Learning purposeMake decision-making reviewable: separate process quality from outcomes and learn from both.
Learning workflows for authenticating to market-data and broker APIs in paper-trading mode. No broker partners are named or implied.
Learning purposeUnderstand how trading technology connects to market data and order APIs, safely and in simulation.
Projects
Course projects are learning exercises grouped by the skills they build. They describe what learners make, not results achieved.
Categories:Trading · 2Analysis · 4Python · 5Algorithmic · 5Platform · 2Research · 4
Fetch historical market data for an NSE stock and present it in an interactive chart with volume, moving averages and Bollinger Bands.
A vectorised back-test of a 20/50 EMA crossover rule across several years of data for a sub-portfolio of Nifty 50 stocks, with a full performance report.
A reusable Python module that turns any back-test trade log into a complete HTML performance report.
An automated paper-trading engine: live data feed, indicator calculation, signal generation and simulated order placement, with alerts on every simulated trade.
Your own strategy taken end to end: designed, coded, back-tested, optimised, paper-traded, deployed and monitored, with full documentation.
A trend-following Expert Advisor with an EMA crossover signal, a higher-timeframe trend filter, ATR-based stop-loss, percentage-of-equity sizing and a trailing stop.
An alert script that fires only when several conditions are true at once — momentum, proximity to a pivot level, trend strength and volume — with an alert for each condition.
Annotate the daily charts of ten Nifty stocks with support/resistance, trend lines, candlestick patterns and two indicators, and write a short interpretation of each.
Identify and paper-trade ten setups over two weeks and record each in a journal: rationale, entry, stop-loss, outcome and lessons learned.
Academy integration
Courses and projects come first; AlphaSync and the AI Trading Professor are planned to support them. Each part shows its own status.
Tier 1: Academy
Tier 2: Learning and building
Tier 3: Technology layer
Tier 4: Practice
Tier 5: Learning companion
Tier 6: Outcome
Status labels come from the product status registry. Coming Soon and Roadmap items are planned and are not available yet.
Product status
Every capability declares its own status. Nothing is shown as available until it has been confirmed.
AlphaSyncStatus: Coming Soon
AlphaSync is Coming Soon. The capabilities below are planned; their scope and timing may change before launch.
Nothing is available yet. Each capability will move here only once it has been confirmed.
There is no AlphaSync sign-up or login yet. Until it launches, Academy courses use paper trading and the tools named in each curriculum for practice.
AlphaSync is built around Academy courses. Explore the courses and learning paths to start building the knowledge it is designed to practise.