cryptofeedzz

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[Amit @HeyAmit_]

This 50-minute lecture by Jeff Bezos will teach you more about business than a 2-year MBA program.

Bookmark it and give it 50 minutes today, no matter what.

[Spencer Baggins @bigaiguy]

OpenAI and Anthropic engineers leaked a prompting technique that separates beginners from experts.

It's called "Socratic prompting" and it's insanely simple.

Instead of telling the AI what to do, you ask it questions.

My output quality: 6.2/10 → 9.1/10

Here's how it works:

[Quant Science @quantscience_]

This guy made a Bloomberg Terminal clone.

Then open sourced it (for free).

Get it here:

[Ryan Hart @thisdudelikesAI]

I DON’T UNDERSTAND WHY PEOPLE DON’T USE GROK FOR STOCKS.

Most traders are looking at charts from 6 months ago.
Grok analyzes real-time sentiment on X to predict future.

Here are 20 prompts to find the next 10x stock:

[Mr. Uppy @MisterUppy]

Gold & Silver: Crashes Inside a Bull Market

Every bull market in metals ends the same way.
According to headlines, at least.

Gold rips to new highs.
Silver goes vertical.
Then one brutal flush convinces everyone the trade is “over.”

That’s not the end of the bull.
That’s the bull doing what it always does.

If you want to understand why violent crashes are part of secular metals cycles, what actually caused the recent flush, and how I’m positioning through it, the full breakdown is linked in the comments.

[Argona @Argona0x]

i gave an AI $50 and told it "pay for yourself or you die"

48 hours later it turned $50 into $2,980

and it's still alive

autonomous trading agent on polymarket

every 10 minutes it:

→ scans 500-1000 markets
→ builds fair value estimate with claude
→ finds mispricing > 8%
→ calculates position size (kelly criterion, max 6% bankroll)
→ executes
→ pays its own API bill from profits

if balance hits $0, the agent dies

so it learned to survive

built in rust for speed

claude API for reasoning (agent pays for its own inference)

runs on a $4.5/month VPS

weather markets: parses NOAA before polymarket updates sports: scrapes injury reports, finds mispricing crypto: on-chain metrics + sentiment

$50 → $2,980 in 48 hours

how much do u think i’ll see in a week?

[Karel Mercx @KarelMercx]

Last Friday was one of the largest silver selloffs of the past 275 years, yet physical silver still left the COMEX.

Since early September, COMEX Silver Registered has dropped from 200 million ounces to 100 million ounces.

The 3.3 million ounces that left last Friday were not stopped by one of the biggest silver declines ever.

Total COMEX inventories fell by 2.4 million ounces. This means COMEX Silver Eligible increased by nearly 1 million ounces.

For clarity, the definitions:

COMEX Silver Eligible

Silver that is physically stored in approved COMEX vaults and meets all requirements (purity, weight) to be traded, but no delivery warrant has been issued.

Meaning: This is effectively private storage. It is owned by investors, banks, or refiners, but it is not offered to settle a futures contract.

Analogy: A house that is fit to be sold, but without a “For Sale” sign in the yard.

COMEX Silver Registered

Silver that is explicitly made available for delivery to someone holding a long futures contract until expiration.

Meaning: This is the market supply. This is the silver actually available for immediate delivery through the exchange.

Analogy: The house with a “For Sale” sign in the yard. You can buy it immediately.

Important: A shift from Eligible to Registered (or vice versa) is often purely administrative (a digital push of a button). The silver itself does not physically move an inch inside the vault.

The most bearish scenario imaginable is that Registered, Eligible, and Total Inventory all rise. More silver enters the vaults than leaves. There is a surplus and little demand for physical delivery. Inventories build.

The very bullish scenario is that Registered, Eligible, and total inventory all fall. Physical silver leaves the vaults entirely. There is strong demand for physical delivery and the silver is not returned to storage, but likely consumed by industry or privately stored outside the exchange. This points to tightness.

Where we are now: Registered is falling, Eligible is rising, and total inventory is rising. This means physical outflows from Registered are larger than inflows into Eligible. Silver is being bought and moved into private ownership (Eligible rises), but even more silver is leaving the vaults altogether. The “free” supply is drying up rapidly.

In a few hours, trading in the East will reopen and they can react to what happened in the West while the East was already closed last Friday. From here, it is crucial to closely monitor lease rates, swap rates, and inventory data.

Last Friday was truly extreme and I will never forget it for the rest of my life. But the idea that silver is done rising just weeks after the US government labeled it a critical material and China tightened export licenses is something I simply do not believe.

[David Lee @DavidLe76335983]

The day after Gold Silver price collapse, Shenzhen Shuibei Gold Market is packed with people. But pricing of merchandise (jewelry price, not just metal) is becoming tricky
The shops are selling at SGE+ price $5640 for #Gold and $136.8 for #Silver while buying back at global price $4827 for Gold and $91.6 for Silver

[Vertox @Vertox_DF]

An overview of mathematical optimization techniques and how to use them in trading. If you want to learn about computational optimization techniques then head over to BlackSwans thread: https://t.co/sh6LFOBzRf

1/n

[Stat Arb @quant_arb]

Day in the life of a quant

1. Stare at incorrect exchange documentation (no need to wake up, my dentist says I’m grinding in my sleep)

2. Learn mandarin (for reading original exchange docs)

3. Implement LSTM

4. Give up on LSTM and go back to linear regression

5. Optimise some parameter / timing / execution / hedge / etc

6. Eat lunch (have to pay because eugene fama outlawed free lunches)

7. Pick off grid trading bots

8. Figure out other market makers parameters and pick them off (they’re using hummingbot)

[Vertox @Vertox_DF]

A couple of Sports Betting Alphas.
From Latency Arbitrage to Relative Value Trading.

🧵
1/n

[CyrilXBT @cyrilXBT]

A month ago this #crypto wallet was sitting at $13k

But today the wallet is worth $620,502

So how did he do this and what projects did he buy into?

All this plus his wallet address in this 🧵👇

[Alf @MacroAlf]

Prepare to read plenty of takes on “liquidity” if the debt ceiling saga gets solved.

Most of them will be simplistic or outright wrong.

The good news is you can track “liquidity” yourself and here are a few things to consider.

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[Steve Burns @SJosephBurns]

How To Identify Supply And Demand Zones On A Chart:

Areas of supply for a market is at overhead price levels is what creates resistance. An area of supply is a price zone where many traders and investors are holding a stock and willing to sell it. Overhead resistance is created when people sell to lock in their gains at profit target levels.

A supply level can also be created when people just want to get back to even after being in a loss that finally rallied back to where they bought it. Supply is the inventory people are holding for a market at a price level and they want to unload it when given the opportunity.

Areas of demand for a market can be at lower price levels that creates support. An area of demand is a price zone where many traders and investors are wanting to buy a market when price gets back to that level. Lower price levels of support are created when people have been waiting to buy a market at a lower price when it gets back there.

This could be an old area of a low price, an overbought reading on a chart, or a key moving average support. Demand is the area of support where buy orders are set because people believe it is an opportunity to get a market at a value price with a good risk/reward ratio on entry.

There can be a correlation with volume and support and resistance zones. The price that markets trade at can create the areas of interest that traders and investors are making transactions.

A price zone where there was a large volume of buyers trying to get into a breakout that fails can create a lot of buyers that become trapped at higher prices in a market that is falling. These trapped buyers can create a future supply and resistance zone as they are happy to exit to get out at even when given another chance.

An old support area that had high demand in volume can create a price zone where a lot of stop losses are set. A lot of volume at an old area of support can set the stage for a breakout to the downside later if the key support area is lost later.

To summarize:
Look for price zones of meaning that set the highest highs or the lowest lows on a chart over a period time without being broken to the upside or the downside.

A supply zone is a price level where current holders of a market are located and are willing sellers when price reaches that area. 

A demand zone is a price level where traders and investors on the sidelines are willing to step in and buy when prices get that low. 

These are the principles in technical analysis that creates horizontal resistance and support lines on charts.

[Stat Arb @quant_arb]

Building an order management system (OMS) is a really good way to build trading systems experience (in digital assets especially)

…

[Mark Gadala-Maria @markgadala]

ChatGPT is the world's best money maker.

But most people don't know how to use it to generate income.

Here are 7 techniques that will turn you into an CASH machine:

[Vertox @Vertox_DF]

Momentum.

What it is, what causes it and how to interpret it mathematically

🧵
1/n

[Adam Cochran (adamscochran.eth) @adamscochran]

Synthetix is killing it with the B2B product shift.

Now 40 assets on perps at Kwenta and Decentrex.

V3 expected in June.

Options on Lyra and Thales.

Sports books on Overtime.

Vaults on Polynomial and Torus.

All benefiting stakers with real revenue and fee burn. 🔥🔥

[Vertox @Vertox_DF]

Deriving Linear Regression!
We'll be finding the equations for the slope and intercept and learn a general approach to minimize error functions (Or any multidimensional function, to some extend)

🧵
1/n

[Stat Arb @quant_arb]

Subtract median then divide by median absolute deviation is often better vs zscore w/ tails on your features.

Usually it’s worth digging into the tails though. Sometimes there’s interesting stuff in the extremes. That or get winsorizing / scaling down aggressively past 3SD