Wisdomise

Automatically scans multiple cryptocurrency trading pairs simultaneously to identify technical patterns (support/resistance levels, moving average crossovers, candlestick formations) using machine learning models trained on historical OHLCV data. The system processes real-time market feeds from connected exchanges, extracts feature vectors from price action, and classifies patterns against a learned model to surface actionable signals without manual chart analysis.

Synthesizes multiple technical and market microstructure signals (pattern matches, momentum indicators, volatility regimes, order book imbalances) into unified buy/sell recommendations with attached confidence scores. The system uses an ensemble approach or weighted scoring model to combine heterogeneous signal sources, then ranks opportunities by expected risk-adjusted return or Sharpe ratio to prioritize execution.

Connects to multiple cryptocurrency exchange accounts (Binance, Coinbase, Kraken, etc.) via API keys, aggregates account balances and positions, and maintains synchronized state across all exchanges. The system handles API authentication, manages rate limits, reconciles positions with trade history, and detects discrepancies (e.g., trades executed outside Wisdomise). Traders can manage all accounts from a single interface without logging into each exchange separately.

Executes buy/sell orders directly on connected cryptocurrency exchanges (Binance, Coinbase, Kraken) based on AI-generated signals, handling order placement, partial fills, slippage management, and position sizing without manual intervention. The system maintains authenticated connections to exchange APIs, implements order routing logic (market vs limit orders, order splitting for large positions), and tracks execution metrics (fill price, fees, slippage) for post-trade analysis.

Simulates trading strategy performance against historical OHLCV data to estimate expected returns, drawdowns, win rates, and Sharpe ratios before deploying to live markets. The system replays historical price action, applies signal generation logic to each candle, executes trades at simulated prices, and accounts for slippage, fees, and position sizing to produce realistic performance metrics. Results are aggregated into equity curves, trade-by-trade P&L, and statistical summaries.

Continuously monitors open positions across all connected exchange accounts, calculates unrealized P&L, tracks realized gains/losses from closed trades, and displays portfolio metrics (total balance, allocation by pair, leverage ratio) with real-time updates. The system aggregates account state from multiple exchanges, reconciles positions with trade history, and computes performance attribution to identify which trades and pairs are driving overall returns.

Allows users to define custom entry/exit rules, position sizing logic, and risk management parameters through a configuration interface (likely UI-based rule builder or JSON/YAML config files). The system interprets these rules during signal generation and execution, enabling traders to encode domain knowledge and risk preferences without modifying code. Rules can reference technical indicators, account state, and market conditions to create conditional trading logic.

Automatically places stop-loss and take-profit orders based on user-defined risk parameters (max loss percentage, profit target, risk-reward ratio) when trades are executed. The system calculates stop-loss and take-profit prices from entry price and position size, submits orders to the exchange, and monitors for fills. If a stop-loss is hit, the position is closed to limit losses; if take-profit is hit, the position is closed to lock in gains.

Analyzes market conditions (volatility, trend direction, momentum) to classify the current regime (bull trend, bear trend, sideways consolidation, high volatility, low liquidity) and automatically adjusts trading strategy parameters or switches between predefined strategies. The system uses statistical measures (Bollinger Bands width, ADX, VIX-equivalent metrics) or ML classifiers to detect regime changes in real-time and apply regime-specific rules (e.g., reduce position size in high volatility, use tighter stops in sideways markets).

Sends real-time alerts to traders when trading signals are generated, positions are opened/closed, or risk thresholds are breached. Alerts are delivered via multiple channels (in-app notifications, email, SMS, webhooks) and can be customized by signal type, pair, or severity level. Webhook integration allows downstream automation — traders can trigger external systems (Discord bots, Slack notifications, custom scripts) when specific events occur.

Aggregates historical trade data and generates detailed performance reports showing returns by pair, strategy, timeframe, and trade characteristics. The system calculates performance metrics (Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor), identifies which trades and strategies contributed most to overall returns, and provides visualizations (equity curves, drawdown charts, monthly returns heatmaps) for analysis. Attribution analysis breaks down returns by pair, entry signal type, and market regime to identify strengths and weaknesses.