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Frances Marsha

@Tech_Marsha • 17,151 subscribers

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Build a new income stream in just 7 days with OpenClaw / Clawdbot. OpenClaw has already generated $113.73 fully autonomously — and with just a laptop, scaling to $5,000/month is within reach. Note: You must be following Frances Marsha to receive the guide. I’ve put together a clear, step-by-step guide to help you get started. It’s usually priced at $200, but I’m offering it free for the next 24 hours. Here’s how to access it: 1. Comment “ GO ” 2. Like and repost this post 3. Follow Frances Marsha (so I can send you the guide via DM)

Build a new income stream in just 7 days with OpenClaw / Clawdbot. OpenClaw has already generated $113.73 fully autonomously — and with just a laptop, scaling to $5,000/month is within reach. Note: You must be following Frances Marsha to receive the guide. I’ve put together a clear, step-by-step guide to help you get started. It’s usually priced at $200, but I’m offering it free for the next 24 hours. Here’s how to access it: 1. Comment “ GO ” 2. Like and repost this post 3. Follow Frances Marsha (so I can send you the guide via DM)

83,056 просмотров • 2 месяцев назад

I’m shifting my focus toward using Claude for research-driven trading insights and automation workflows. Recently, I explored a workflow where Claude was used to: “Identify potentially mispriced prediction markets on Polymarket and detect wallets that consistently execute similar strategies for potential copy-trading analysis.” In a short test period, this approach reportedly identified rapid gains in certain tracked examples, including high-performing wallets with extensive trade histories and moderate win rates. One example I reviewed showed: * Significant long-term growth (from small capital to substantially larger returns) * ~1,900+ trades executed * Around a 55% win rate What stood out most is that there are sophisticated arbitrage and automation systems operating at scale—many of which are difficult to compete with without technical experience. Instead of trying to outbuild them immediately, the approach here is to observe, analyze, and learn from these systems, then selectively follow or mirror strategies where appropriate. Using an AI-assisted setup, I built a monitoring workflow that: * Tracks and ranks active wallets * Flags consistent high-performing behavior * Helps surface potential copy-trading candidates The process is intentionally simple: * Select a trader * Enable priority monitoring mode for faster updates * Start tracking or mirroring activity through the system For those interested in building similar systems, I can share a step-by-step breakdown of how to set up a basic version from scratch. I’ve already helped a number of people explore structured approaches to trading automation and workflow design. The goal is not unrealistic returns, but more consistent and informed decision-making. If you want the guide: 1. Comment “ SEND ” 2. Like and repost this 3. Follow Frances Marsha (so I can reach out to you)

I’m shifting my focus toward using Claude for research-driven trading insights and automation workflows. Recently, I explored a workflow where Claude was used to: “Identify potentially mispriced prediction markets on Polymarket and detect wallets that consistently execute similar strategies for potential copy-trading analysis.” In a short test period, this approach reportedly identified rapid gains in certain tracked examples, including high-performing wallets with extensive trade histories and moderate win rates. One example I reviewed showed: * Significant long-term growth (from small capital to substantially larger returns) * ~1,900+ trades executed * Around a 55% win rate What stood out most is that there are sophisticated arbitrage and automation systems operating at scale—many of which are difficult to compete with without technical experience. Instead of trying to outbuild them immediately, the approach here is to observe, analyze, and learn from these systems, then selectively follow or mirror strategies where appropriate. Using an AI-assisted setup, I built a monitoring workflow that: * Tracks and ranks active wallets * Flags consistent high-performing behavior * Helps surface potential copy-trading candidates The process is intentionally simple: * Select a trader * Enable priority monitoring mode for faster updates * Start tracking or mirroring activity through the system For those interested in building similar systems, I can share a step-by-step breakdown of how to set up a basic version from scratch. I’ve already helped a number of people explore structured approaches to trading automation and workflow design. The goal is not unrealistic returns, but more consistent and informed decision-making. If you want the guide: 1. Comment “ SEND ” 2. Like and repost this 3. Follow Frances Marsha (so I can reach out to you)

61,620 просмотров • 2 месяцев назад

ANOTHER QUIET POLYMARKET ACCOUNT JUST TURNED $50 INTO $625,000. Claude rebuilt the strategy in 40 minutes, and the edge was simple: hit BTC contract lag before Polymarket catches up. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me Frances Marsha (so i can DM you)

ANOTHER QUIET POLYMARKET ACCOUNT JUST TURNED $50 INTO $625,000. Claude rebuilt the strategy in 40 minutes, and the edge was simple: hit BTC contract lag before Polymarket catches up. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me Frances Marsha (so i can DM you)

54,014 просмотров • 3 месяцев назад

A former Goldman Sachs quant trader shared a simple but powerful principle with me in a single conversation: “We don’t forecast. We only take positions when pricing deviates from estimated probability by more than ~6%.” That was it. No complexity—just execution logic used on institutional desks. The result was a system that evaluates 400+ markets per hour. It focuses on contracts in the ~7–19 cent range where implied pricing diverges significantly from modeled probability (roughly 60–90% confidence zones in backtested scenarios). Over a three-month period (paper + small capital testing phase): * Starting point: $2,000 * Peak value: $8,191 * 99 executed trades * Sharpe ratio: ~2.3 A few example signals the system flagged: * ETH Merge-related event: 72¢ market price vs ~88% modeled probability → +19¢ move * SOL above $200 breakout scenario: 44¢ vs ~81% model probability → +15¢ * Florida hurricane (Cat 3+ threshold): 81¢ vs ~92% → +7¢ * Wheat above $800: 53¢ vs ~68% → +20¢ Each of these was identified automatically by the scanner based on deviation thresholds and liquidity filters. When I showed the setup to him recently, he said: “This is essentially what we run with an 800M AUM team and dozens of engineers.” The difference is cost. My current stack: * Claude: ~$20/month * VPS: ~$5/month * Open-source repos: free * APIs: minimal / free tiers On top of that, I’ve deployed multiple lightweight agents running continuously to monitor different market clusters. Performance tracking labels (internal testing): * velvet_void: +$697 * nano_alpha: +$541 * ratking_eth: +$407 * darkpool_7: +$356 For context, his fund reportedly returned ~19% last year, while this experimental setup has shown significantly higher short-term volatility-adjusted returns in a limited testing window. Nothing here is magic or prediction-based—just probabilistic pricing, systematic filtering, and execution discipline. If you want access to the breakdown and setup details: 1. Comment “ Claude ” 2. Like + repost 3. Follow Frances Marsha (so I can DM you)

A former Goldman Sachs quant trader shared a simple but powerful principle with me in a single conversation: “We don’t forecast. We only take positions when pricing deviates from estimated probability by more than ~6%.” That was it. No complexity—just execution logic used on institutional desks. The result was a system that evaluates 400+ markets per hour. It focuses on contracts in the ~7–19 cent range where implied pricing diverges significantly from modeled probability (roughly 60–90% confidence zones in backtested scenarios). Over a three-month period (paper + small capital testing phase): * Starting point: $2,000 * Peak value: $8,191 * 99 executed trades * Sharpe ratio: ~2.3 A few example signals the system flagged: * ETH Merge-related event: 72¢ market price vs ~88% modeled probability → +19¢ move * SOL above $200 breakout scenario: 44¢ vs ~81% model probability → +15¢ * Florida hurricane (Cat 3+ threshold): 81¢ vs ~92% → +7¢ * Wheat above $800: 53¢ vs ~68% → +20¢ Each of these was identified automatically by the scanner based on deviation thresholds and liquidity filters. When I showed the setup to him recently, he said: “This is essentially what we run with an 800M AUM team and dozens of engineers.” The difference is cost. My current stack: * Claude: ~$20/month * VPS: ~$5/month * Open-source repos: free * APIs: minimal / free tiers On top of that, I’ve deployed multiple lightweight agents running continuously to monitor different market clusters. Performance tracking labels (internal testing): * velvet_void: +$697 * nano_alpha: +$541 * ratking_eth: +$407 * darkpool_7: +$356 For context, his fund reportedly returned ~19% last year, while this experimental setup has shown significantly higher short-term volatility-adjusted returns in a limited testing window. Nothing here is magic or prediction-based—just probabilistic pricing, systematic filtering, and execution discipline. If you want access to the breakdown and setup details: 1. Comment “ Claude ” 2. Like + repost 3. Follow Frances Marsha (so I can DM you)

55,204 просмотров • 3 месяцев назад

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