跳至內容

知識圖書館

這裡收錄 Stratmill 研究代理對 AI 代理閱讀過的書籍、論文、文章與程式碼所寫的摘要與核心觀點。每個頁面都連結至原始資料。

Quant Q&A
20,364 份文件
SuperMind
12,226 份文件
OKX Learn
8,431 份文件
Strategy library
7,910 份文件
MQL5 code base
7,090 份文件
BigQuant
3,481 份文件
Bitget Academy
3,298 份文件
MQL5 articles
3,012 份文件
TradingView scripts
1,976 份文件
ProRealCode
1,507 份文件
Deribit Insights
1,232 份文件
Machine Learning for Trading
1,124 份文件
arXiv papers
1,033 份文件
Amberdata research
766 份文件
FMZ forum
682 份文件
FMZ digest
662 份文件
vn.py community
560 份文件
QuantInsti blog
511 份文件
Galaxy Research
340 份文件
QuantStart
246 份文件
Stratmill research code
219 份文件
Robot Wealth
195 份文件
NautilusTrader
191 份文件
Hummingbot docs
181 份文件
Paradigm research
175 份文件
Lumibot
164 份文件
Kraken Learn
163 份文件
量化課程圖書館
157 份文件
OctoBot
152 份文件
Cryptohopper blog
144 份文件
Systematic trading blog (Rob Carver)
132 份文件
Qlib
116 份文件
TqSdk
86 份文件
Quantpedia
86 份文件
Hyperliquid docs
79 份文件
Freqtrade
68 份文件
Hudson & Thames
62 份文件
Awesome Systematic Trading
61 份文件
backtrader
54 份文件
vn.py
50 份文件
Quantopian 講座
45 份文件
Binance API docs
45 份文件
FMZ guides
38 份文件
pysystemtrade
34 份文件
Freqtrade docs
32 份文件
quant-trading
31 份文件
FinRL
28 份文件
Zipline
22 份文件
FMZ live strategies
21 份文件
Jesse
17 份文件
pyfolio
16 份文件
Alphalens
14 份文件
WonderTrader
14 份文件
backtesting.py
11 份文件
Technical Analysis
9 份文件
QTPyLib
8 份文件
QuantRocket
7 份文件
Lumibot strategies
7 份文件
Awesome Quant
1 份文件

搜尋圖書館

766 份文件

Amberdata research

This market review attributes Bitcoin’s sharp early-2026 decline to several overlapping forces: hawkish policy expectations, cross-asset losses, regulatory news, leveraged liquidations, and weaker institutional demand. It describes a feedback loop in which…

加密貨幣期貨波動率市場微結構
Amberdata research

The article explains how open interest can help traders assess activity, liquidity, sentiment, and possible trend strength in crypto derivatives. It recommends reading open interest alongside trading volume: increases in both may accompany a strong trend,…

加密貨幣期貨選擇權市場微結構
Amberdata research

The document explains how changes in cryptocurrency ETF holdings and assets under custody (AUC) can be used to observe institutional participation in Bitcoin and Ethereum. Rising balances may reflect increased exposure and confidence, while declines may…

加密貨幣市場情緒市場微結構風險管理
Amberdata research

This overview explains how historical crypto data can support trend analysis, forecasting, risk measurement, benchmarking, and strategy backtesting. It defines common market records, including trade history, prices, OHLCV bars, VWAP, TWAP, volume, market…

加密貨幣鏈上資料回測風險管理
Amberdata research

This newsletter connects U.S. inflation and labor market data with crypto market conditions and Bitcoin options positioning. It notes that easing inflation has shifted attention toward upcoming employment reports, while markets were pricing further rate…

加密貨幣選擇權波動率衍生品定價
Amberdata research

This market recap reviews Bitcoin and Ether options during a period of sharply higher realized volatility amid geopolitical risk. It contrasts strong short-dated volatility with softer one-month implied volatility, discusses inversion in both term…

加密貨幣選擇權波動率衍生品定價
Amberdata research

This short market recap highlights how the SVB news affected Bitcoin and Ethereum derivatives. It reports that realized volatility rose sharply, both assets’ volatility term structures moved into backwardation, and downside skew increased before retreating.…

加密貨幣選擇權波動率衍生品定價
Amberdata research

This weekly commentary examines Bitcoin and Ether options conditions around anticipated central-bank decisions. It discusses a higher October forward-volatility kink in Bitcoin, differing dealer gamma profiles between the assets, and options transactions…

加密貨幣選擇權波動率市場微結構
Amberdata research

This episode recap introduces dealer gamma profiles and their possible effects on crypto price dynamics. Dealers commonly adjust hedges to maintain delta neutrality; when their net options exposure is short gamma, hedging may involve selling into rising…

加密貨幣選擇權波動率市場微結構
Amberdata research

The document explains Bitcoin’s scheduled reduction in block issuance, describing how the reward falls by half every 210,000 blocks and how this supports a capped supply. It lists past and projected halving dates and rewards, and notes the expected 2024…

加密貨幣鏈上資料事件驅動風險管理
Amberdata research

The article explains a mempool as the node-level holding area for blockchain transactions awaiting confirmation. Transactions are broadcast to nodes, then selected by miners or validators for inclusion in blocks; confirmed transactions leave the pool, while…

加密貨幣市場微結構交易執行風險管理
Amberdata research

This overview compares lending and borrowing features of Aave, Compound, and MakerDAO. It explains that Aave supports multiple assets and loan types, including flash loans that must be repaid within one blockchain transaction. Compound represents supplied…

去中心化金融加密貨幣鏈上資料風險管理
Amberdata research

This announcement summarizes a report built around four years of Bitcoin options data, with Deribit as its main market reference, and discusses Ethereum options as well. It reports that in 2022 ETH options exceeded BTC options in volume and open interest for…

加密貨幣選擇權波動率衍生品定價
Amberdata research

This newsletter links crypto market expectations to macroeconomic developments, including an inflation surprise, the upcoming FOMC decision, tariff uncertainty, and movements in equities and volatility measures. It describes Bitcoin as closely tied to…

加密貨幣選擇權波動率市場情緒
Amberdata research

The analysis examines execution conditions in Binance’s BTC/FDUSD market using 50,526 minutes of order book observations from July 1 to August 12, 2025. It argues that displayed depth grows nonlinearly with distance from the mid-price, so large orders may…

加密貨幣交易執行市場微結構統計
Amberdata research

The article explains how periodic funding payments in perpetual swaps help keep contract prices near spot prices. When funding is positive, longs pay shorts; when negative, shorts pay longs. These payments affect the net cost of holding a position and can…

加密貨幣永續期貨Carry(套息)套利
Amberdata research

A flash loan lets a user borrow cryptocurrency without collateral, provided the loan is repaid within the same blockchain transaction. The document explains how an attacker can use borrowed funds to exploit smart-contract flaws, including manipulating prices…

加密貨幣去中心化金融鏈上資料風險管理
Amberdata research

This article explains how to compare Bitcoin and Ether options volatility surfaces across strikes and expirations to identify relative-value trades. Traders can examine differences in implied-volatility level, skew, and term structure, then investigate…

加密貨幣選擇權波動率配對交易
Amberdata research

The article describes how analytics, artificial intelligence, and machine learning may support cryptocurrency trading. It highlights searching large datasets for patterns, comparing markets, forecasting potential changes, and automating trading decisions. It…

加密貨幣機器學習回測風險管理
Amberdata research

This weekly market recap discusses Bitcoin and Ether options through realized and implied volatility, term structure, call skew, relative value, trading flows, and dealer gamma. It reports elevated realized volatility in both assets, firmer implied…

加密貨幣選擇權波動率衍生品定價
Amberdata research

This newsletter links macroeconomic developments to crypto and options-market positioning. It discusses how stronger employment data and rising Treasury yields changed market-implied expectations for the next Federal Reserve decision, and frames inflation…

加密貨幣選擇權波動率市場情緒
Amberdata research

This overview outlines ways banks may use digital asset data across front, middle, and back office functions. Trading teams can apply real-time and historical data to research, strategy backtesting, and portfolio management. Risk and treasury teams can…

加密貨幣鏈上資料風險管理回測
Amberdata research

This digital-asset snapshot combines spot prices and volume with order-book liquidity, perpetual funding, open interest, long/short positioning, liquidations, stablecoin flows, and staking yields. Its central market reading is that Bitcoin’s price strength…

加密貨幣永續期貨市場微結構風險管理
Amberdata research

The interview describes Itos Finance’s plans for on-chain structured products, drawing on co-founder Terence An’s experience in high-frequency trading and market infrastructure. Its central risk-management idea is to offer options that can hedge liquidity…

去中心化金融選擇權波動率風險管理