The product is an automated trading bot that analyzes stock market patterns to manage trades. It offers a performance-based licensing model, allowing users to pay based on profits gained. The bot provides high-speed automated trades and optimized strategies for users.
Features
Automated Trading Bots
Flumen Capital provides fully automated strategies that analyze patterns in the stock market. The bots use data analytics to help traders gain an edge.
Performance-Based Licensing
One of Flumen's licenses is based on the bot's trading performance. Users pay according to their earnings, making for a results-oriented approach.
Risk Management Tools
The platform offers fully automated risk management, ensuring consistent performance and loss control over thousands of trades.
Zoom Consultation
Potential users can book a Zoom call with Flumen Capital's team to discuss the tools and strategies in detail.
Free Access to NinjaTrader
Download NinjaTrader and receive immediate free access to the platform, allowing for exploration of its features without initial cost.
Trading Platform
NinjaTrader is an award-winning trading platform featuring thousands of apps and add-ons for customization. It provides advanced market analysis, professional charting, and fast order execution.
Trading Simulator
New traders can start with a free trading simulator that features real-time market data to practice before entering live markets.
Market Data Feed by Kinetick
Kinetick offers a reliable, fast, and cost-effective market data feed for active traders, providing real-time quotes for stocks, futures, and forex. It offers free end-of-day historical market data through NinjaTrader.
Hypothetical Performance Tracking
Helps track and visualize hypothetical trading performance over time with advanced charts and graphs.
Predictive Modeling
Forecast future price movements based on historical and real-time data to enhance decision-making by identifying potential market trends and patterns.
Risk Management
Improve risk assessment and mitigation strategies by identifying and responding to potential risks in real-time, protecting capital more effectively.
Market Sentiment Analysis
Gauge market sentiment to anticipate potential market shifts. Adapt trading strategies based on prevailing sentiments for a competitive edge.
Data-Driven Decision Making
Utilizes real-time market data and statistical models to inform trading decisions. This empirical approach offers a more objective basis for decision-making.
Algorithm Development
Insights derived from quantitative analysis are used to create algorithms, allowing traders to maximize returns by employing data-driven strategies.
Risk Management
Quantitative analysis is crucial for identifying potential risks and devising strategies to balance potential returns and risks.
Backtesting
Involves testing trading strategies using historical data to validate their effectiveness and identify areas for improvement.
Execution Optimization and Adaptability
Helps in optimizing trade executions, minimizing costs, and enhancing the efficiency of the trading process.
Formulating Hypotheses
Establishing a competitive advantage by thoroughly grasping market dynamics and understanding the assumptions and predictions underlying the strategy.
Tool Selection
Choosing the right programming languages and brokers for building and executing the trading strategy, including considerations of speed, efficiency, and reliability.
Hypothesis Testing
Programming the strategy to test hypotheses based on historical market data to evaluate its potential performance.
Backtesting
Validating the strategy against historical data to assess its effectiveness and reliability before deploying it in real market conditions.
Result Analysis
Evaluating key metrics such as returns, risks, and volatility, to measure the success of the trading strategy.
Strategy Enhancement
Refining the trading code based on test results to enhance the strategy's market adaptability and performance.
Additional Backtesting and Optimization
Fine-tuning parameters through iterative testing to optimize strategy performance.
Iterative Refinement
Continuously enhancing the strategy by leveraging ongoing data analysis and feedback loops.
Cross-Market Evaluation
Assessing the strategy's performance across various financial markets to ensure versatility and robustness.
Live Testing and Ongoing Optimization
Implementing the strategy in real-world conditions and making adjustments based on live performance data for continual improvement.
Trend-Following Strategies
Utilizes indicators like moving averages to identify and act on persistent market trends. Example: MACD strategy, which signals buying or selling based on trend directions.
Mean Reversion Strategies
Based on the theory that prices revert to a mean over time. Includes pair trading by exploiting price differentials between correlated securities.
Arbitrage Opportunities
Exploits price inefficiencies across markets using statistical arbitrage. Involves identifying price discrepancies across securities for profit.
Market Making Strategies
Focuses on earning profits by providing market liquidity and capitalizing on bid-ask spread differences.
Sentiment Analysis Strategies
Uses big data and machine learning to analyze market sentiment and social media to predict market trends, like selling on negative news impact.