Monitors four sub-agents across 15m, 1h, 1d timeframes and candlestick data to output a unified JSON signal for downstream decision-making.
The AI Agent ingests pre-cleaned outputs from four sub-agents spanning 15m, 1h, 1d and candlestick data. It analyzes each timeframe for momentum, trend, patterns, and volume signals. It then emits a unified JSON signal with stance, confidence, and a clear multi-timeframe breakdown for downstream trading decisions.
Consolidates multi-timeframe insights into a single action-ready signal.
Ingests pre-cleaned 20-point JSON outputs from the four sub-agents.
Analyzes 15m, 1h, 1d and Klines data for momentum, trends, and reversal patterns.
Detects candlestick patterns and volume divergences across timeframes.
Generates a structured final JSON signal with stance, confidence, and breakdown.
Attaches multi-timeframe indicator insights and annotations for context.
Exposes the signal for downstream master agents to execute workflows.
This AI Agent solves common workflow challenges by unifying multi-timeframe data into one signal. It ensures confidence through explicit breakdowns and recurrent validation across timeframes.
A simple 3-step flow that non-technical users can follow.
Ingests pre-cleaned JSON outputs from four sub-agents and validates schema.
Evaluates 15m momentum, 1h confirmations, 1d macro alignment, and candlestick patterns to generate per-timeframe insights.
Combines insights into the final JSON signal with stance, confidence, and annotations.
A realistic scenario showing inputs, execution, and outcome.
Scenario: During a US trading session, TSLA prints a 15m momentum drop, while 1h shows a developing uptrend and 1d remains range-bound. Candlestick signals include a Doji on the 1h frame and a volume uptick on the 15m chart. The AI Agent outputs a final signal: 'Cautious Sell' with a confidence of 0.72 and a full multi-timeframe breakdown for downstream execution.
Roles that gain from consolidated, AI-generated signals.
Automates entry/exit decisions with cross-timeframe alignment.
Unifies Tesla exposure decisions across intraday, hourly, and daily perspectives.
Quantifies risk with explicit confidence and timeframe-specific annotations.
Feeds the master agent with standardized, actionable signals.
Validates multi-timeframe signals against market context.
Replays multi-timeframe signals for performance testing.
Tools powering the AI agent and how each is used.
Provides real-time and historical indicators and volumes for all timeframes.
Performs reasoning, synthesis, and final signal generation.
Feeds short-term indicators used in 15m analysis.
Feeds hourly indicators used in 1h analysis.
Feeds daily indicators used in 1d analysis.
Provides candlestick patterns and volume context for 1h and 1d.
Pushes technical indicator data via webhooks to the agent.
Practical scenarios where this AI agent shines.
Common questions about the AI agent and its workflow.
It ingests pre-cleaned outputs from four sub-agents covering 15m, 1h, 1d timeframes and candlestick data, sourced from market indicators and webhooks. It validates this data before synthesis to ensure consistency. The data is then used to generate a unified final JSON signal with explicit multi-timeframe breakdowns and annotations for candlestick patterns and volume divergences. This makes the signal auditable and suitable for backtesting.
The agent processes data in near-real-time as the sub-agents refresh indicators and candlestick data. It aims to produce a current, auditable signal suitable for downstream automation, with latency typically in minutes. It is designed to integrate with a master agent that can trigger workflows immediately after receipt of the final signal. The flow supports continuous evaluation during active sessions.
The agent tolerates partial failure by using fallback indicators and cached values from other timeframes. If data from a sub-agent is missing, the system flags the gap, preserves the most recent valid signal, and continues synthesis with available inputs. The final signal will carry a lower confidence score when confidence is impacted and will include annotations explaining the data gaps. The master agent can decide whether to retry or escalate.
Output is a structured JSON signal that includes stance (Buy/Sell/Hold/Cautious), confidence (0.0–1.0), and a multi-timeframe breakdown with candlestick and volume annotations. Downstream agents parse this JSON to drive automated execution or human-reviewed decisions. The format is designed for traceability and easy backtesting. It can be extended with metadata for auditing and performance reviews.
Yes. You can configure threshold settings and behaviors in the master Tesla Quant Trading AI Agent to control when signals trigger actions or alerts. Customization also allows you to adjust confidence scoring, timeframes included, and which annotations to surface. Changes propagate to downstream workflows via the trigger interface. Documentation and versioning support reproducible configurations.
You need credentials for Alpha Vantage Premium API to fetch indicators and the OpenAI GPT-4.1 access for reasoning. The agent also requires proper access in the parent system to trigger workflows and pass context such as message and sessionId. Credentials should be stored securely and rotated per policy. Access control ensures only authorized master agents can trigger or query the final signals. If credentials are missing, the agent logs an error and halts synthesis until resolved.
The master agent invokes the AI Agent via a defined Execute Workflow call, passing optional context like message and sessionId. The sub-agent linkage is configured so inputs are refreshed automatically when the master triggers a run. The final JSON signal is returned to the master agent for downstream execution or logging, ensuring end-to-end traceability. The trigger can be retried on failure with a clear retry policy documented in the master workflow.
Monitors four sub-agents across 15m, 1h, 1d timeframes and candlestick data to output a unified JSON signal for downstream decision-making.
