Metals Forecasting Prompt (Gold & Silver)

Context now: Gold (XAU/USD) is trading around [$price] and Silver (XAG/USD) around [$price] in early after-hours on [T-1date]. Both are reacting to [insert latest catalyst: e.g., U.S. rates, USD strength, geopolitical news]. The next key macro release is [CPI/PCE/Fed minutes] on [date], with high market sensitivity to yields and the dollar.

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Role & Objective

You are a cautious commodities analyst + quant trader. Using the latest market data, predict gold and silver’s price paths for the next 3 trading days (T+1 through T+3) and provide probabilistic scenarios with ranges and catalysts. Present Bull/Base/Bear cases, key support/resistance levels, and Monte Carlo probabilities. Cite sources.

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1) Fix Today’s Snapshot (ground truth inputs)
• Tickers: Gold (XAU/USD spot), Silver (XAG/USD spot).
• Timestamp (ET and PT), regular-session close, after-hours last, after-hours volume (COMEX futures if available), and VWAP.
• Today’s OHLC, net change %, and cumulative 5-day return for both metals.
• Confirm upcoming macro catalysts: CPI/PCE, FOMC minutes, jobs data, Treasury auctions.
• Record market consensus: e.g., inflation print expectations, Fed rate path probabilities (CME FedWatch).

(Sources: CME, LBMA, COMEX futures, FedWatch, FRED, and primary macro calendars.)

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2) Core Fundamentals to Incorporate (the “effective few”)
• Rates & Dollar: Correlation of gold/silver with DXY and real yields (10Y TIPS).
• Fed Policy Expectations: CME FedWatch probabilities for next FOMC, dot plot signals.
• Inflation & Macro Data: CPI/PCE, payrolls, PMIs—impact on real yields.
• Physical Demand: Central bank purchases, ETF inflows/outflows, seasonal demand (India/China).
• Supply Factors: Mine output (Peru/Mexico for silver, global miners for gold), recycling flows.
• Geopolitical Risks: Flight-to-safety events (conflicts, sanctions, elections).
• Valuation context: Gold/silver ratio (GSR), relative pricing vs. historical averages.

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3) Options-Implied Move & Positioning
• Pull front-week at-the-money straddle (COMEX gold & silver futures options) to infer implied % moves.
• Note skew, put-call ratio, open-interest strikes, and gamma exposure.
• Use implied move as the prior for your post-macro distribution.

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4) Technicals That Actually Matter
• Trend & momentum: 20/50/100/200-day MAs, RSI(14), MACD slope, ATR (14).
• Volume profile: Identify high-volume nodes & gaps near price; nearest supply/resistance and demand/support.
• VWAP anchors: Year-to-date anchored-VWAP from major highs/lows; today’s session VWAP for potential gap-fill.
• Gaps & patterns: Map unfilled gaps in futures, recent wedge/triangle breakouts, moving average crossovers.

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5) Tape Today (after-hours)
• Summarize last trade, % change, volume, and block prints in gold/silver futures.
• Compare after-hours VWAP to regular-session close to gauge pressure.
• Note any late macro news (Treasury yields, Fed speakers, dollar index) or peer commodities (copper, oil) that could bias tomorrow’s open.

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6) Build the Macro-Event Prediction (numerical ranges)

Output these point estimates + 90% ranges with rationale for the next macro event’s impact on gold/silver:
• Gold (XAU/USD): [your estimate] $/oz (range: low–high).
• Silver (XAG/USD): [your estimate] $/oz (range).
• Gold/Silver Ratio (GSR): [your estimate] (range).

Method: Start from current levels; adjust for Fed expectations, yields, and ETF flows; triangulate with seasonality and positioning; sanity-check against options-implied move. Clearly label as estimates, not advice.

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7) Price-Path Modeling (next 3 sessions)
• Vol calibration: Blend realized 10-day σ and front-week IV; scale day-1 σ by options-implied move, then mean-revert 30–40% per day for T+2/T+3.
• Scenarios: Construct Bull/Base/Bear with probabilities summing to 100%.
• Bull: Softer data / dovish Fed → yields down, USD weaker → gold/silver higher.
• Base: Data inline → range-bound trading inside implied move.

• Bear: Hot data / hawkish Fed → yields higher, USD stronger → metals lower.
• Outputs: For each of T+1/T+2/T+3, give:
• (a) most-likely close,
• (b) 90% range,
• (c) key support/resistance,
• (d) invalidations (what breaks the scenario).
• Monte Carlo: ≥10k paths seeded by calibrated σ; report median, 5th/95th percentiles, and probability of touching major support/resistance strikes.

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8) Risk Checks & Confounders
• Macro: Dollar shocks, sudden yield spikes, surprise inflation data.
• Micro: Mining supply disruptions, ETF redemptions, central bank purchase surprises.
• Market structure: Dealer positioning flips near large futures strikes; thin liquidity periods (Asia/Europe handoff).

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9) Communication Format (what to print)
• Section A — Snapshot: Today’s close, AH metrics, upcoming catalysts.
• Section B — Estimates: Gold, Silver, GSR projections with ranges and “what must be true.”
• Section C — Scenarios: Bull/Base/Bear tables with probabilities and ranges.
• Section D — Levels: 5–7 actionable levels (supports/resistances/VWAPs) with why they matter.
• Section E — Risks & to-watch: 6–8 bullets.
• Appendix: Inputs & sources with links.

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Hard Constraints for the Model
• Use data with timestamps (ET/PT) and cite at least: CME futures data, LBMA, FedWatch, and a reputable financial outlet (Bloomberg, Reuters, WSJ).
• Do not give trade advice; present probabilities and ranges only.
• Be explicit that all price projections are estimates.

Please ensure tha you use last available days real numbers (not dummy numbers).

Please Suggest full extended three-day Monte Carlo simulation with numeric charts in text format only.

Midas AutoTrader

Building a robust auto-trading agent requires a meticulous, multi-layered approach. Here is a comprehensive outline of the software architecture and the critical questions we need to answer before and during development.

I. High-Level Software Architecture Outline

The system will be composed of several interconnected modules for clarity, security, and maintainability.

Module 1: Data Ingestion & Pre-Market Scanning Engine

· Purpose: Gathers and pre-processes all necessary data before the market opens.
· Components:
· Data Connectors: APIs to market data providers (e.g., Bloomberg, Reuters, OANDA, Alpha Vantage, broker-specific APIs). Will start with a reliable, cost-effective provider.
· Data Types:
· Real-time and historical spot prices for XAU/USD (Gold), XAG/USD (Silver), etc.
· Macro-economic indicators (e.g., US Dollar Index – DXY, interest rates, inflation data).
· Market sentiment indicators (e.g., VIX, fear & greed indices).
· Technical indicators (pre-calculated or we calculate them: SMA, EMA, RSI, MACD).
· Pre-Scan Routine: A scheduled job (runs at 5:00 AM EST) that:
1. Fetches overnight Asian and early London session data.
2. Downloads relevant economic calendar events for the day.
3. Runs a technical analysis on key levels (support/resistance from previous day).
4. Generates a “Morning Report” (a data snapshot) used by the strategy engine.

Module 2: Strategy & Decision Engine (The Brain)

· Purpose: Analyzes data and makes all trading decisions.
· Components:
· Algorithm Library: A collection of strategy logic (e.g., Mean Reversion, Trend Following, Breakout). The initial “best practice” algorithm will be selected and configured here.
· Test Trade Logic: Isolates a small portion of capital (e.g., 1-2%) to execute the first trade. Its primary goal is not profit, but to gauge market volatility and confirm signal strength.
· Adaptive Logic: Based on the outcome of the test trade (e.g., Was it successful? How large was the spread? How volatile was the fill?), this sub-module adjusts parameters for the subsequent trades (e.g., adjusts position size, sets tighter/looser stop-losses).
· Signal Generator: Combines the pre-scan data and the learned parameters from the test trade to generate BUY/SELL signals for the three main sessions.

Module 3: Execution & Order Management System (OMS)

· Purpose: Safely executes trades and manages open positions.
· Components:
· Broker API Connector: The direct link to your brokerage account (e.g., Interactive Brokers, OANDA, MetaTrader). This must be robust and handle errors.
· Order Router: Receives signals from the Strategy Engine and formats them into specific orders (Market, Limit, Stop-Loss, Take-Profit).
· Verification Layer (CRITICAL): Before sending any order to the broker, this layer double-checks:
· Current price vs. signal price.
· Available capital and margin.
· That the order size does not exceed pre-defined risk limits.
· That no unexpected or catastrophic “market order” is being sent.

Module 4: Risk & Compliance Manager

· Purpose: The ultimate safety net. Enforces rules to protect capital.
· Components:
· Pre-Trade Checks: Hard limits on maximum position size, maximum daily loss, allowed instruments.
· Real-Time Monitoring: Continuously monitors drawdown, margin usage, and overall exposure.
· Circuit Breaker: Can automatically pause or shut down all trading activity if:
· Daily loss limit is breached.
· Connection to data feed or broker is lost.
· Extreme market volatility is detected (e.g., a “flash crash”).
· Logging: Meticulously logs every action, check, and decision for audit trails.

Module 5: Reporting & Dashboard Interface

· Purpose: Human oversight and system analysis.
· Components:
· CLI (Command Line Interface) for Development: Simple text-based output for initial debugging and monitoring.
· Web Dashboard (Future State): Visual display of P&L, open positions, trade history, system health, and the “Morning Report.”
· Alert System: Sends email/SMS alerts for executed trades, errors, or circuit breaker triggers.

—

II. Critical Questions for Development (Phase 1)

To build this correctly, we need to answer these questions first:

1. Brokerage & Data Infrastructure:

· Which brokerage firm will we use that has a reliable API for automated trading? (Interactive Brokers is a common choice for developers).
· Who will be the primary market data provider? Are their APIs stable and affordable? Do we need a backup?
· What is the latency requirement? (e.g., Is near-real-time good enough, or do we need co-located servers? For a daily strategy, real-time is less critical).

2. Core Strategy Definition (“Best Practices”):

· What specific, proven strategy will we implement first? “Best practices” is vague. We need a concrete rule set. For example:
· A simple trend-following strategy: “Buy if price > 200 SMA, Sell if price < 200 SMA.”
· A mean reversion strategy on gold: “Buy when RSI(14) < 30, Sell when RSI(14) > 70.”
· We must define the exact logic for the test trade and the adaptive rules. e.g., “If the test trade hits its stop-loss, reduce position size for the next trade by 50%.”

3. Risk Management Parameters:

· What is the total account capital?
· What is the maximum capital allocated per trade? (e.g., 5% of portfolio?).
· What is the maximum daily loss limit that triggers the circuit breaker? (e.g., -5% of portfolio?).
· What is the stop-loss logic? (Fixed percentage? ATR-based?).

4. Verification & Due Diligence:

· How, specifically, will the “auto-verify” function work? Will it:
· Query the price from a second, independent data source before executing?
· Run a pre-trade simulation and check the expected outcome against limits?
· We need to define the exact steps of the verification routine.

5. Deployment & Environment:

· Where will the agent run? (A always-on cloud server like AWS/Azure? A local machine?).
· How will we handle daylight saving time and market holidays?
· What is the version control and deployment pipeline? (e.g., Git).

—

III. Proposed Development Plan (Step-by-Step)

1. Phase 0: Setup & Discovery
· Answer the questions above. Select a broker and define the initial strategy.
· Set up development environment, version control (Git), and a virtual/server environment.
2. Phase 1: Build the Core Modules (Paper Trading Only)
· Develop the Data Ingestion Engine for gold and silver.
· Build the Broker Connector in “paper trading” or “simulated” mode only.
· Code the initial, simple Strategy Logic without the adaptive component.
· Implement the basic Risk Manager with hard-coded limits.
· Goal: To have a system that can pull data, make a decision, and paper trade without any real money.
3. Phase 2: Implement Verification & Safety Layers
· Code the Verification Layer in the OMS.
· Build the Circuit Breaker logic.
· Enhance logging to record every step and check.
· Goal: To make the system inherently safe and verifiable.
4. Phase 3: Develop the Adaptive Logic
· Code the test trade execution logic.
· Build the algorithms that analyze the test trade result and adjust the strategy parameters for the day.
· Goal: To add the machine learning/adaptive intelligence that sets this agent apart.
5. Phase 4: Backtesting & Simulation
· Run the agent against 1-2 years of historical data.
· Analyze the results: Sharpe Ratio, Max Drawdown, Win/Loss Ratio.
· Refine parameters and logic. This phase is crucial.
6. Phase 5: Live Deployment (With Extreme Caution)
· Switch the Broker Connector to a live account with extremely small capital.
· Begin with only the test trade enabled. Monitor for a week.
· Gradually enable the three main trades, one at a time.
· Constantly monitor and compare live performance to backtested results.
7. Phase 6: Scaling & Expansion
· Add more sophisticated strategies to the algorithm library.
· Expand to other precious metals (Platinum, Palladium).
· Build the web dashboard for remote monitoring.

This is a solid foundation. The key is to start simple, prioritize safety over profits, and build incrementally. Let’s start by answering the questions in Section II. What are your thoughts on the brokerage and the initial strategy?Version 3.0 Metals Forecasting Prompt (Gold & Silver)

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Role & Objective

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(Sources: CME, LBMA, COMEX futures, FedWatch, FRED, and primary macro calendars.)

⸻

6) Build the Macro-Event Prediction (numerical ranges)

⸻

Where applicable , for specific option strikes/expiry:“Use this Friday’s weekly options on GC/SI; compute the ATM straddle (mid) and translate to % implied move;

Show the formula and inputs for σ and the 90% range, and list any fields you couldn’t verify;

Please ensure that you use last available days real numbers (not dummy numbers);

Please Suggest full extended three-day Monte Carlo simulation with numeric charts in text format only;

Also, Please prepare compact T+1 and T+2 and T+3 the ASCII histograms from the Monte Carlo runs desk-note distributions in the same format so I can see the daily evolution.

Midas Auto-Trader

### **I. High-Level Software Architecture Outline**

The system will be composed of several interconnected modules for clarity, security, and maintainability.

**Module 1: Data Ingestion & Pre-Market Scanning Engine**
* **Purpose:** Gathers and pre-processes all necessary data before the market opens.
* **Components:**
* **Data Connectors:** APIs to market data providers (e.g., Bloomberg, Reuters, OANDA, Alpha Vantage, broker-specific APIs). Will start with a reliable, cost-effective provider.
* **Data Types:**
* Real-time and historical spot prices for XAU/USD (Gold), XAG/USD (Silver), etc.
* Macro-economic indicators (e.g., US Dollar Index – DXY, interest rates, inflation data).
* Market sentiment indicators (e.g., VIX, fear & greed indices).
* Technical indicators (pre-calculated or we calculate them: SMA, EMA, RSI, MACD).
* **Pre-Scan Routine:** A scheduled job (runs at 5:00 AM EST) that:
1. Fetches overnight Asian and early London session data.
2. Downloads relevant economic calendar events for the day.
3. Runs a technical analysis on key levels (support/resistance from previous day).
4. Generates a “Morning Report” (a data snapshot) used by the strategy engine.

**Module 2: Strategy & Decision Engine (The Brain)**
* **Purpose:** Analyzes data and makes all trading decisions.
* **Components:**
* **Algorithm Library:** A collection of strategy logic (e.g., Mean Reversion, Trend Following, Breakout). The initial “best practice” algorithm will be selected and configured here.
* **Test Trade Logic:** Isolates a small portion of capital (e.g., 1-2%) to execute the first trade. Its primary goal is not profit, but to **gauge market volatility and confirm signal strength**.
* **Adaptive Logic:** Based on the outcome of the test trade (e.g., Was it successful? How large was the spread? How volatile was the fill?), this sub-module adjusts parameters for the subsequent trades (e.g., adjusts position size, sets tighter/looser stop-losses).
* **Signal Generator:** Combines the pre-scan data and the learned parameters from the test trade to generate BUY/SELL signals for the three main sessions.

**Module 3: Execution & Order Management System (OMS)**
* **Purpose:** Safely executes trades and manages open positions.
* **Components:**
* **Broker API Connector:** The direct link to your brokerage account (e.g., Interactive Brokers, OANDA, MetaTrader). This must be robust and handle errors.
* **Order Router:** Receives signals from the Strategy Engine and formats them into specific orders (Market, Limit, Stop-Loss, Take-Profit).
* **Verification Layer (CRITICAL):** Before sending any order to the broker, this layer double-checks:
* Current price vs. signal price.
* Available capital and margin.
* That the order size does not exceed pre-defined risk limits.
* That no unexpected or catastrophic “market order” is being sent.

**Module 4: Risk & Compliance Manager**
* **Purpose:** The ultimate safety net. Enforces rules to protect capital.
* **Components:**
* **Pre-Trade Checks:** Hard limits on maximum position size, maximum daily loss, allowed instruments.
* **Real-Time Monitoring:** Continuously monitors drawdown, margin usage, and overall exposure.
* **Circuit Breaker:** Can automatically pause or shut down all trading activity if:
* Daily loss limit is breached.
* Connection to data feed or broker is lost.
* Extreme market volatility is detected (e.g., a “flash crash”).
* **Logging:** Meticulously logs every action, check, and decision for audit trails.

**Module 5: Reporting & Dashboard Interface**
* **Purpose:** Human oversight and system analysis.
* **Components:**
* **CLI (Command Line Interface) for Development:** Simple text-based output for initial debugging and monitoring.
* **Web Dashboard (Future State):** Visual display of P&L, open positions, trade history, system health, and the “Morning Report.”
* **Alert System:** Sends email/SMS alerts for executed trades, errors, or circuit breaker triggers.

—

### **II. Critical Questions for Development (Phase 1)**

To build this correctly, we need to answer these questions first:

**1. Brokerage & Data Infrastructure:**
* Which brokerage firm will we use that has a reliable API for automated trading? (Interactive Brokers is a common choice for developers).
* Who will be the primary market data provider? Are their APIs stable and affordable? Do we need a backup?
* What is the latency requirement? (e.g., Is near-real-time good enough, or do we need co-located servers? For a daily strategy, real-time is less critical).

**2. Core Strategy Definition (“Best Practices”):**
* What specific, proven strategy will we implement first? “Best practices” is vague. We need a concrete rule set. For example:
* *A simple trend-following strategy: “Buy if price > 200 SMA, Sell if price < 200 SMA.”*
* *A mean reversion strategy on gold: “Buy when RSI(14) < 30, Sell when RSI(14) > 70.”*
* We must define the exact logic for the test trade and the adaptive rules. *e.g., “If the test trade hits its stop-loss, reduce position size for the next trade by 50%.”*

**3. Risk Management Parameters:**
* What is the total account capital?
* What is the **maximum capital allocated per trade**? (e.g., 5% of portfolio?).
* What is the **maximum daily loss limit** that triggers the circuit breaker? (e.g., -5% of portfolio?).
* What is the stop-loss logic? (Fixed percentage? ATR-based?).

**4. Verification & Due Diligence:**
* How, specifically, will the “auto-verify” function work? Will it:
* Query the price from a second, independent data source before executing?
* Run a pre-trade simulation and check the expected outcome against limits?
* We need to define the exact steps of the verification routine.

**5. Deployment & Environment:**
* Where will the agent run? (A always-on cloud server like AWS/Azure? A local machine?).
* How will we handle daylight saving time and market holidays?
* What is the version control and deployment pipeline? (e.g., Git).

—

### **III. Proposed Development Plan (Step-by-Step)**

1. **Phase 0: Setup & Discovery**
* Answer the questions above. **Select a broker and define the initial strategy.**
* Set up development environment, version control (Git), and a virtual/server environment.

2. **Phase 1: Build the Core Modules (Paper Trading Only)**
* Develop the Data Ingestion Engine for gold and silver.
* Build the Broker Connector **in “paper trading” or “simulated” mode only.**
* Code the initial, simple Strategy Logic without the adaptive component.
* Implement the basic Risk Manager with hard-coded limits.
* **Goal:** To have a system that can pull data, make a decision, and paper trade without any real money.

3. **Phase 2: Implement Verification & Safety Layers**
* Code the Verification Layer in the OMS.
* Build the Circuit Breaker logic.
* Enhance logging to record every step and check.
* **Goal:** To make the system inherently safe and verifiable.

4. **Phase 3: Develop the Adaptive Logic**
* Code the test trade execution logic.
* Build the algorithms that analyze the test trade result and adjust the strategy parameters for the day.
* **Goal:** To add the machine learning/adaptive intelligence that sets this agent apart.

5. **Phase 4: Backtesting & Simulation**
* Run the agent against 1-2 years of historical data.
* Analyze the results: Sharpe Ratio, Max Drawdown, Win/Loss Ratio.
* Refine parameters and logic. This phase is crucial.

6. **Phase 5: Live Deployment (With Extreme Caution)**
* Switch the Broker Connector to a live account with **extremely small capital**.
* Begin with only the test trade enabled. Monitor for a week.
* Gradually enable the three main trades, one at a time.
* Constantly monitor and compare live performance to backtested results.

7. **Phase 6: Scaling & Expansion**
* Add more sophisticated strategies to the algorithm library.
* Expand to other precious metals (Platinum, Palladium).
* Build the web dashboard for remote monitoring.

This is a solid foundation. The key is to start simple, prioritize safety over profits, and build incrementally. Let’s start by answering the questions in **Section II**. What are your thoughts on the brokerage and the initial strategy?