Forwards Analysis and Trading Options Arbitrage Strategy Framework
- Bryan Downing
- Jun 6
- 8 min read
Executive Summary
This document presents a comprehensive forwards analysis of short-term profit opportunities across multiple financial instruments, including commodities, currencies, indices, and crypto-reference rates. Based on volatility, recent price momentum, and cash-and-carry options arbitrage strategy opportunities, we identify the five top instruments—BRR, CC, ES, GC, and ALI—with the greatest potential for 10–30-day profits.
For each instrument, we propose a combined futures and options strategy that balances locked-in arbitrage gains with directional upside or protective downside hedges. We allocate a $100 000 portfolio proportionally to each instrument’s profit potential, then design medium-frequency trading (MFT) tactics—ranging from RSI-based scalping to Bollinger-Band breakouts—to harvest intra-day and multi-day movements.
Finally, we outline a Python-based simulation architecture (Dash + Plotly) that generates synthetic price paths with realistic bid-ask spreads, executes strategy signals, manages positions, and tracks cumulative open and closed profit and loss. A simple “Start/Stop” control and per-instrument enable/disable switches allow interactive live simulation.
Target return: 8–10% over a 10–30-day horizon with robust risk controls.
1. Introduction
Short-term trading strategies seek to exploit temporary mispricings and momentum within a finite holding period. The convergence of cash and futures prices under efficient-market theory provides reliable arbitrage profits when futures trade at a significant premium to spot. Coupling these arbitrage trades with options structures—either bull call spreads to capture additional upside or protective puts to guard against adverse moves—enables both profit enhancement and risk mitigation.
Key analysis inputs include:
5-Day Momentum: Log-returns to detect recent directional bias.
20-Day and Annualized Volatility: Measures of price variability driving both profit potential and option premium.
Cash-and-Carry Arbitrage: Locked-in gain by buying spot and selling futures until expiration.
ARIMA-Based Floors: Short-term model forecasts to bias trade direction.
By synthesizing these metrics, we rank instruments and tailor strategies. A subsequent Python simulation validates behavior under realistic trading conditions, including bid-ask spreads, partial fills, and discrete time steps.
2. Instrument Selection & Short-Term Profit Potential
2.1. Analysis Methodology
Momentum Measurement
Compute log-returns for the last five trading sessions.
Identify sustained positive or negative momentum.
Volatility Assessment
Calculate 20-day historical standard deviation and annualize.
Higher volatility implies larger intra-period swings, increasing strategy opportunity.
Arbitrage Profit Calculation
Estimate per-unit profit by subtracting spot cost from futures proceeds (net of financing).
Favor instruments with large positive spreads.
Forecast Direction
Where ARIMA projections are available, use 10-day forecast floors as directional cues for options trade construction.
2.2. Top-Ranked Instruments
Based on the above metrics, the following five instruments emerged as the most attractive for short-term profit:
BRR (Bitcoin Reference Rate)
Volatility: ≈ 51.7% annualized
5-day returns oscillate across ±3% range
Arbitrage: $9 397.66 per unit
Recommendation: High-volatility arbitrage with protective put
CC (Cocoa)
Volatility: ≈ 51.7% annualized
Recent log-returns up to +7%
Arbitrage: $586.00 per unit
Recommendation: Bull call spread layered atop cash carry
ES (S&P 500 E-Mini)
Volatility: ≈ 19.9% annualized
Consistent mild uptrend (≈ +1% log-returns)
Arbitrage: $383.50 per unit
Recommendation: Bull call spread with trend-following filter
GC (Gold)
Volatility: ≈ 18.1% annualized
Mixed short-term log-returns (–1% to +3%)
Arbitrage: $86.90 per unit
Recommendation: Protective put overlay on cash carry
ALI (Aluminum)
Volatility: ≈ 21.7% annualized
Modest positive momentum (+1% log-returns)
Arbitrage: $79.75 per unit
Recommendation: Bull call spread plus breakout scalping
2.3. Secondary Instruments
Secondary candidates (HO, BZ, CL, etc.) display lower arbitrage or volatility profiles and are deprioritized for the 10–30-day time frame.
3. Combined Futures & Options Strategy Design
For each top instrument we combine a cash-and-carry arbitrage base with an options overlay to either cap downside or enhance upside:
Instrument | Base Futures Strategy | Options Overlay | Rationale |
BRR | Buy spot @ $96 492.34, sell futures @ $105 890.00 → $9 397.66 locked | Buy protective put @ strike $105 890 | High vols: downside hedge pays off on deep corrections while base arb secures profit. |
CC | Buy spot @ $8 743.00, sell futures @ $9 329.00 → $586.00 locked | Bull call spread 9 329/9 500 | Upward momentum: spread limits premium outlay, captures additional upside above strike. |
ES | Buy spot @ $5 667.25, sell futures @ $6 050.75 → $383.50 locked | Bull call spread 6 050.75/6 200 | Steady uptrend: spread magnifies small directional moves at reduced cost. |
GC | Buy spot @ $3 265.30, sell futures @ $3 352.20 → $86.90 locked | Buy protective put @ strike $3 352.20 | Mixed short-term trend: downside protection preserves arb profit if gold tumbles. |
ALI | Buy spot @ $2 330.50, sell futures @ $2 410.25 → $79.75 locked | Bull call spread 2 410.25/2 450 | Mild positive bias: capture upside with limited net premium exposure. |
Notes on Overlay Construction:
Bull Call Spread: Purchase at‐the‐money call, sell out-of-the-money call to reduce net cost. Maximum gain occurs if underlying exceeds sold strike.
Protective Put: Purchase a put at near futures price to cap maximum loss on futures de-mark to spot. Intrinsic value accrues if price falls beneath strike.
4. Portfolio Weighting & Capital Allocation
To maximize expected return while controlling risk, we allocate our $100 000 equity proportionally to each instrument’s arbitrage magnitude and volatility:
Instrument | Relative Profit Potential | Allocation (%) | Allocation ($) |
BRR | 9 397.66 per unit | 35% | 35 000 |
CC | 586.00 per unit | 25% | 25 000 |
ES | 383.50 per unit | 20% | 20 000 |
GC | 86.90 per unit | 10% | 10 000 |
ALI | 79.75 per unit | 10% | 10 000 |
Total | 100% | 100 000 | |
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Allocation Rationale:
BRR dominates with outsized arb profit and extreme volatility, warranting the largest share.
CC offers high relative volatility plus moderate arb; second largest.
ES benefits from reliable small gains; mid-size allocation.
GC and ALI provide smaller base arb but help diversify betas across commodities.
5. Medium-Frequency Trading Strategies
Each instrument’s MFT approach runs independently on discrete timeframes (1 m, 1 h, 4 h, daily), generating trading signals to complement the base arbitrage trade:
5.1. BRR – Momentum Scalping with RSI
Timeframe: 1-hour bars
Indicator: 14-period RSI
Entry Signals:
RSI < 30 → initiate small long futures tranche
RSI > 70 → initiate small short futures tranche
Exit Signals:
RSI reverts to 50 or 2% adverse move
Static stop-loss at 3%
Size: 0.1 unit lots; multiple scalps per day
5.2. CC – Volatility Breakout with Bollinger Bands
Timeframe: Daily bars
Indicator: 20-day moving average ±2 σ
Entry Signals:
Price > upper band → buy futures tranche
Price < lower band → sell futures tranche
Exit Signals:
Reversion to MA or 3% adverse move
Size: 0.5 units per breakout; ~3–5 trades/week
5.3. ES – Trend-Following with Moving Averages
Timeframe: 4-hour bars
Indicator: 50- and 200-period moving averages
Entry Signals:
50 MA crosses above 200 MA → buy futures tranche
50 MA crosses below 200 MA → sell futures tranche
Exit Signals:
Opposite crossover or 2% adverse move
Size: 0.5 units; ~2–4 trades/week
5.4. GC – Mean Reversion via RSI
Timeframe: Daily bars
Indicator: 14-period RSI
Entry Signals:
RSI < 30 → buy futures tranche
RSI > 70 → sell futures tranche
Exit Signals:
RSI returns to 50 or 2% adverse move
Size: 0.25 units; ~1–3 trades/week
5.5. ALI – Hourly Bollinger-Band Scalping
Timeframe: 1-hour bars
Indicator: 20-period MA ±2 σ
Entry Signals:
Upper band breakout → buy futures tranche
Lower band breakout → sell futures tranche
Exit Signals:
MA reversion or 1.5% adverse move
Size: 0.5 units; ~5–8 trades/week
Expected MFT Contribution:
Each instrument’s MFT layer adds roughly 1–3% of its allocated capital in additional profit, boosting overall portfolio returns.
6. Simulation Architecture (Python3)
To validate strategy performance under realistic market micro-structure, we design a modular Python simulation comprising six core components:
Configuration Module
Defines instrument parameters: initial price, drift, volatility, bid-ask spread, allocation percentage, strategy identifier, bar interval.
Stores futures & options overlay details (strike levels, premiums).
Price Simulator
Generates synthetic mid-price paths via Geometric Brownian Motion.
Injects random noise and discrete time steps matching desired bar frequency (e.g., 60 s for 1-min bars).
Derives bid and ask by applying half-spread offsets around the mid-price.
Strategy Engine
Houses a library of indicator calculators (RSI, moving averages, Bollinger Bands).
Exposes per-instrument signal functions that consume the latest price history and return position-sizing signals (positive for buy, negative for sell, zero for hold).
Order Manager
Translates signals into limit or market orders executed at current bid/ask.
Enforces per-instrument enable/disable flags.
Applies size caps based on remaining allocation.
Immediately or asynchronously closes positions when exit conditions are signaled.
Position Tracker & P&L Calculator
Maintains lists of open positions per instrument with entry price, size, timestamp.
Upon closure, computes realized P&L (qty × (exit price – entry price)).
Tracks unrealized P&L optionally for mid-position reporting.
Aggregates cumulative P&L across instruments and over time.
Dashboard Controller (Dash + Plotly)
Provides a web interface with:
• A “Start Simulation” button to spawn the event loop
• A “Stop” button to gracefully halt simulation
• Checkbox toggles to enable or disable each instrument in real time
• Live-updating price series plots for selected instruments
• Cumulative P&L graph with timestamps
• A tabular view of the most recent closed trades (time, instrument, qty, entry, exit, P&L)
6.1. Data Flow & Event Loop
On Start, the dashboard spawns a background thread or asynchronous task that:
Advances the simulation clock in fixed intervals (e.g., 1 s per “tick”).
Updates price paths for all enabled instruments.
Invokes strategy logic to produce signals.
Routes orders through the Order Manager, updating positions.
Recomputes P&L and writes new entries to the trade log.
The Dash Interval component polls at a user-specified refresh rate (e.g., 1 s) to fetch the latest data for rendering.
6.2. Design Patterns & Best Practices
Observer Pattern: Price Simulator acts as the data source, notifying Strategy Engine and Dashboard of new values.
Strategy Pattern: Each instrument’s trade logic is encapsulated in a dedicated strategy class or function, allowing easy extension.
Command Pattern: Orders and executions are represented as command objects, queued and processed deterministically.
Dependency Injection: Configuration and logger instances are passed explicitly to modules, enhancing testability.
Thread Safety: Position updates and trade log writes are synchronized via locks or thread-safe queues to avoid race conditions.
7. Dashboard & Control Mechanisms
The interactive dashboard empowers traders and researchers to:
Enable/Disable Instruments: Instantly include or exclude any symbol from price simulation and strategy evaluation.
Start/Stop Simulation: Control the event loop without restarting the application.
Monitor Live Metrics: Observe mid-price trajectories, bid/ask spreads, open positions, and cumulative P&L in real time.
Inspect Trade History: Review the last N closed trades in a scrollable table, with time stamps and profit outcomes.
Parameter Tuning: (Future extension) Adjust strategy inputs—RSI thresholds, band deviations, stop-loss levels—on the fly via interactive widgets.
All UI components are responsive, ensuring usability on diverse screen sizes from desktops to tablets.
8. Risk Management & Performance Evaluation
8.1. Risk Controls
Stop-Loss Limits: Configurable per-strategy adverse-move thresholds (1.5–3%).
Position Sizing Caps: Fractional lots tied to capital allocation, preventing over-exposure.
Options Overlays: Protective puts cap drawdowns; call spreads limit premium spending.
Instrument Diversification: Five uncorrelated assets dilute idiosyncratic shocks.
8.2. Key Performance Indicators
Cumulative Return: Net P&L as a percentage of starting $100 000.
Sharpe Ratio: Excess return per unit volatility over the simulation period.
Max Drawdown: Largest peak-to-trough loss experienced.
Win Rate: Proportion of profitable closed trades.
Profit Factor: Gross profit divided by gross loss.
These metrics are computed in real time and displayed in summary panels for rapid diagnosis.
9. Conclusion & Next Steps
This forwards analysis and simulation framework equips quantitative traders with:
A ranked universe of high-arbitrage, high-volatility instruments for 10–30-day trades.
Tailored strategy constructs combining cash-and-carry trades with options overlays to optimize profit and limit downside.
A structured portfolio allocation that maximizes expected return while controlling risk.
Medium-frequency overlays that extract incremental profits through momentum, trend-following, breakouts, and mean reversion.
A fully interactive Python simulation dashboard (Dash + Plotly) that models realistic market dynamics, order execution, P&L accounting, and control toggles, supporting thorough back-testing and live prototyping.
Next Steps:
Live Data Integration: Replace GBM with real market feeds (e.g., WebSocket streams, REST APIs).
Parameter Optimization: Employ grid search or Bayesian methods to fine-tune RSI windows, band widths, and stop-loss thresholds.
Transaction Cost Modeling: Incorporate slippage, commission tiers, and dynamic spreads.
Execution Algorithms: Introduce VWAP/TWAP and smart order routing for large notional trades.
Risk Aggregation: Implement real-time margin monitoring and cross-instrument exposure limits.
This framework provides a robust foundation for both research and production deployment, targeting an 8–10% return in the next 10–30 days under controlled risk parameters. We welcome feedback and collaboration on further enhancements.
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