What Is Walk-Forward Testing? Why Can Backtesting Over a Fixed Period Hide Strategy Flaws?

In cryptocurrency trading, market conditions are constantly changing. A strategy may perform exceptionally well in a bull market but get stopped out frequently in a sideways market; it may also be profitable on a certain period of historical data but fail to adapt to subsequent volatility. Walk-forward testing can help traders assess how well a strategy adapts across different periods.

First, what is walk-forward testing?

Walk-forward testing typically divides historical data into consecutive time win

Most tokenization diagrams start with the token.
Due diligence should start with the ownership chain.
Who issues it? Who holds the underlying asset? Who is obligated to pay?
A transferable token is only as credible as the documented rights behind it.
#RWA #InvestorRights

CATL is pushing to bring sodium-ion batteries to vehicles. These batteries are cheaper and perform better in cold weather, though their energy density remains a concern. The company plans to expand its passenger-car battery swap network to more than 3,000 stations by year-end, giving users enough coverage for city commutes and letting them top up for long weekend trips with a swap that takes just a few minutes.

The battery itself doesn’t hold much charge, so CATL’s solution is simple: It’s okay if one battery pack can’t take you far—as long as there are a few thousand “lifelines” along the wa

The most unusual detail in today's market is $U: the amplitude across 30 candlesticks is just 0.03%, with the Bollinger Bands compressed between 1.00047–1.00077, almost a straight line. Such extremely low volatility is often not calm but a fragile balance amid thin liquidity; once funds push the market, slippage and instantaneous volatility can be amplified. MA5 is slightly above MA20, RSI is 57.6, and the MACD histogram is marginally positive, suggesting a bullish bias but very weak momentum; the Fear and Greed Index is at 32, in fear territory, indicating insufficient appetite to chase longs

⏰ Day 407 Live Spot Execution | Picking Up Chips Below Miners’ Shutdown Price

Foreign media are buzzing this morning: #BTC miners’ average production cost is now as high as $78,000, and the price has fallen to $74k, forcing miners to pull the plug

Network mining difficulty is expected to plunge 6.4% (the biggest drop since the major bottom in late June this year)

No need to buy mining rigs or pay electricity bills—at 03:00:22 this morning, my DCA machine bought ready-made chips directly below miners’ cost:
• Bought today: 400 USDC @ $74,589.9
• Total holdings: 1.7621 / 3.00 $BTC (58.74% c

CPI day and bitcoin:native is sitting right on the floor 👀
GM Phantoms. Tuesday bitcoin:native update 👻
📍 $74.8K, 0.2% from the $74.9K support zone
📉 Down 9.3% in 30 days, 40% off the ATH
🔴 Short term: bearish
🔴 Mid term: bearish (flipped since yesterday)
🔴 Long term: bearish
⚠️ US CPI drops at 8:30am ET. Since Jan 2025, bitcoin:native’s daily range has been 26% wider the day before CPI and 10% wider on the day.
Hold the floor or lose it? 👇
Full brief 👇
#Bitcoin #Crypto #CPI #PhantomSignals

What Is Feature Engineering? How Can More Valuable Trading Signals Be Extracted from Price, Volume, and Volatility?

In crypto quantitative trading, the performance of machine learning models depends not only on the algorithm, but also on the quality of their input data. Raw price and volume records capture market movements, but may not directly express trends, momentum, and risk. Feature engineering is the process of transforming raw data into information that is more suitable for models to learn from.

First, how can price features be constructed?

You can calculate returns over different pe

$INIT
UPDATE
#INIT is getting a good volume here. We can see 60%+ gain here ✍🏻
#INITUSDT #INITBTC

What Is the F1 Score? How Can It Combine Precision and Recall to Evaluate a Trading Model?

In crypto quantitative trading, one model may issue very few bullish signals, achieving high precision but missing many opportunities to capture gains; another may identify most upward moves but generate many false signals. Looking at just one metric makes it difficult to evaluate a model comprehensively. The F1 score is a way to measure precision and recall together.

First, what is the F1 score?

The F1 score is the harmonic mean of precision and recall, calculated as: F1 = 2 × precision × recall ÷ (p

Major players’ liquidation spree! $BTC A major market shift—who gets wiped out first, longs or shorts?

Retail traders watch candlesticks; pros look at the full picture! Funds are fleeing en masse—is this an exit or a shakeout?

My take: Whale longs have lost 80 million, and funds are seeing net outflows across the board. The bears look unstoppable, but the liquidation map shows shorts above 75000 are all at risk of getting liquidated! And while miners are dumping, newly created wallets are quietly accumulating. The major players are clearly setting up a “kill longs first, then turn around an

Bitcoin is maintaining a bullish outlook for now. The wide short-term swings are more of a shakeout and consolidation before the next move, so don't let the back-and-forth news disrupt your rhythm. Despite multiple dips, the market has not seen a sustained breakdown, and buyers are firmly defending support below. The repeated volatility is essentially digesting geopolitical news while awaiting the data.

Tonight's CPI is more of a short-term disturbance and is unlikely to reverse the broader trend. As long as key support holds, a pullback that stabilizes presents an opportunity to buy the dip

Gud mornings chat <3
Finally going to gym, haven’t been there for a week, ops

#英伟达突发风波股价承压CEO
The third item was already on the table before the scandal. Sales of NVIDIA's H200 chips to China were already under Congressional scrutiny through regulations such as the Remote Access Security Act, the AI OVERWATCH Act, and the Chip Security Act.

What is new is this: Jensen Huang was the strongest voice arguing that sales to China should continue. Now the same person is at the center of a China-linked intelligence allegation. This weakens NVIDIA's bargaining power in Washington. Senator Elizabeth Warren's call in late October for the board to investigate also shows this p

Everyone, short $INIT now using 10x leverage...📉‼️Entry price: $0.2063 - $0.2070Take profit 1: $0.1901Take profit 2: $0.1840Take profit 3: $0.1738Take profit 4: $0.1600Stop loss: $0.2250Trade management: Lock in partial profits at take profit 1. After reaching take profit 1, move the stop loss to breakeven. Avoid adding to losing positions.

What is out-of-sample testing? How can you tell whether a trading strategy truly generalizes?

In cryptocurrency trading, strong historical backtest results do not mean a strategy can adapt to future markets. To keep a strategy from simply memorizing past data, traders use out-of-sample testing to check whether it still performs reasonably on data that was not used to develop or tune it.

First, what is out-of-sample testing?

Out-of-sample testing evaluates a strategy’s performance using data that was not used to select or optimize its parameters. For example, you could develop a strategy usi

Financial privacy has economic value when disclosure changes how competitors trade, price risk or negotiate.
A market maker revealing inventory, an OTC desk exposing transaction size or a fund publishing treasury movements can surrender valuable information.
But concealing information from competitors is different from withholding evidence from authorized counterparties.
That’s the institutional use case behind @primus_labs’ FHE and zkTLS infrastructure.
FHE protects supported onchain balances and transactions. zkTLS authenticates external financial records without disclosing everything.
Five

How Do Fees Change the Break-Even Win Rate? Why Can Frequent Trading Erode Profits?

In cryptocurrency trading, some strategies perform well in backtests, but their profits shrink significantly in live trading. The reason may not be market direction predictions, but trading costs such as fees, bid-ask spreads, and slippage. The more trades you make, the more important these costs become to your final returns.

First, how do fees affect profits?

Suppose the profit target for each trade is 100 USDT, and the planned loss is 100 USDT. If the total cost of each round-trip trade is 10 USDT, the net

US stocks are finally showing some signs of life! 🔥

On one hand, US October CPI is about to be released; on the other, the S&P 500 just posted its biggest single-day gain in nearly 7 months, giving the market plenty to talk about.

Here are three key areas to watch:

📊 **Inflation:** Can CPI continue to cool? If the data comes in below expectations, will rate-cut expectations rise further?

₿ **Crypto market:** Can BTC continue to rebound on macro tailwinds, or will we see another “buy the rumor, sell the news” event?

📈 **US stocks and gold:** Is this rally an oversold bounce, or are AI a