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Zināšanu bibliotēka

Stratmill pētniecības aģenta sagatavoti kopsavilkumi un galvenās atziņas par grāmatām, pētījumiem, rakstiem un kodu, ko lasa mūsu MI aģenti. Katrā lapā ir saite uz oriģinālu.

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

Meklēt bibliotēkā

Dokumentu skaits: 79,386

SuperMind

This Chinese A-share screening idea selects stocks whose intraday high-low range exceeds 1%, whose day low is between 4% and 5% below the prior close, and whose MACD is above zero. The rationale combines elevated volatility and a sharp intraday decline with…

AkcijasTehniskie indikatoriSvārstīgumsAtgriešanās pie vidējās vērtības
SuperMind

This proposed stock screen selects shares with a daily high-low range above 1, three consecutive limit-up sessions as of the previous day, and at least two limit-up events during the prior 500 days. The post interprets the range as a sign of activity and the…

AkcijasĶīnas tirgiCenas impulssCenas izrāviens
SuperMind

This proposed Chinese stock screen looks for a daily price range above 1, a ratio between 0.5 and 2 formed from the previous day’s turnover rate and the current auction volume relative to the previous day’s volume, and a current large-order accumulation…

AkcijasĶīnas tirgiTirgus mikrostruktūraTehniskie indikatori
SuperMind

This Chinese stock screen combines a positive MACD reading, an external-to-internal trading volume ratio above 1.3, and more than two limit-up days in the prior ten days. It ranks qualifying stocks by percentage gain, favoring recent price strength and…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
MQL5 code base

This document describes an example of a multicurrency Expert Advisor that processes symbols one at a time in a loop. It uses a timer event to run trading logic independently of ticks arriving for any particular symbol, and Bollinger Band values provide the…

Valūtu tirgusTehniskie indikatoriRīkojumu izpilde
SuperMind

This example turns a CAPM regression into a monthly stock-selection process. It takes a recent window of daily returns for eligible constituents, adjusts stock and benchmark returns by a stated daily risk-free rate, and regresses each stock’s returns against…

AkcijasStatistikaFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This brief coding question outlines a way to calculate fund performance statistics from a price series. It first derives periodic returns from price changes, then uses a performance-analysis library to compute cumulative return, annualized return, Sharpe…

StatistikaRiska pārvaldībaSvārstīgums
SuperMind

This screening proposal combines three conditions: price amplitude above one, a value for today’s control measure above 21, and a date in or after 2021. The description frames the first two conditions as filters for more volatile stocks and stocks with…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This stock-screening proposal targets companies in the metaverse industry that have had more than two limit-up sessions in the recent ten-day window and have just formed a KDJ golden cross. It calls for screening before 10:00 on each trading day and…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
BigQuant

The document summarizes CapTE, a model for predicting stock movements from social media text. A Transformer encoder extracts semantic features from posts, while a capsule network is used to represent structural relationships in the text. The approach is…

AkcijasMašīnmācīšanāsTirgus noskaņojumsStatistika
BigQuant

This short platform discussion explains that an adjust factor is used to convert a stock’s real price into an adjusted price. Adjusted prices, including forward- and backward-adjusted series, are intended to keep price charts continuous across corporate…

AkcijasVēsturisko datu pārbaude
SuperMind

This Chinese stock-screening post describes a rule based on price range, recent turnover, and limit-up frequency. Its initial description calls for an amplitude above 1, prior-day actual turnover between 3% and 28%, and more than two limit-up sessions in a…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
BigQuant

This short forum exchange explains how to configure BigQuant’s trading engine to rebalance on a weekly or monthly schedule. For weekly scheduling, it specifies the weekly trading-day mode and a day value of 5; for monthly scheduling, it specifies the monthly…

Portfeļa veidošanaVēsturisko datu pārbaudeRīkojumu izpilde
SuperMind

This stock-screening rule selects shares with turnover between 3% and 12%, a seven-day falling-price pattern, and no limit-up session on the previous day. The document gives both a platform-style condition and a Python example, and explains the intended…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
Qlib

The document introduces Temporal Routing Adaptor (TRA), a model designed to learn multiple trading patterns from stock market data. It describes using TRA with Qlib datasets and workflows, and notes that the paper’s reproduction setup first trains a backbone…

AkcijasMašīnmācīšanāsVēsturisko datu pārbaudeStatistika
SuperMind

This Chinese equity screen combines price amplitude above one, appearance on the previous day’s market top list, and a positive price-to-earnings ratio. The article interprets amplitude as a sign of short-term volatility, top-list inclusion as a possible…

AkcijasSvārstīgumsUz notikumiem balstīta tirdzniecībaĶīnas tirgi
SuperMind

This Chinese equity screen looks for stocks with price amplitude above one, an opening price near the ten-day moving average, and simultaneous bullish crossover signals from three indicators. The examples use MACD, RSI, and KDJ: MACD and KDJ cross above…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

This article introduces the autoregressive moving-average model as a combination of AR terms, which use past observations, and MA terms, which represent past shocks. It describes choosing the orders p and q with autocorrelation and partial autocorrelation…

StatistikaSvārstīgums
SuperMind

This stock screen combines three conditions: net buying today must exceed five percent, the previous day’s turnover must be above 60 million, and the ten-day price gain must be positive but below 35 percent. The document presents these as signs of buying…

AkcijasCenas impulssĶīnas tirgi
MQL5 code base

The document describes an Expert Advisor that trades when the i-KlPrice histogram crosses an overbought or oversold level. A signal is confirmed at bar close, so the strategy acts on completed-bar threshold breaks rather than intrabar movement. The advisor…

Tehniskie indikatoriValūtu tirgusVēsturisko datu pārbaude
MQL5 code base

The document explains that MetaTrader 5 exposes generic, loss-side, and profit-side tick values for each symbol, and that these values may not be identical. This matters when an expert advisor calculates trade size from a risk budget: using a tick value that…

Riska pārvaldībaPozīcijas apjoma noteikšanaValūtu tirgus
BigQuant

The document summarizes a study that develops a probabilistic classifier to identify high-frequency trading activity from intraday order data. Using French BEDOFIH market records, the researchers engineered features describing orders, including their prices,…

Augstas frekvences tirdzniecībaMašīnmācīšanāsStatistikaTirgus mikrostruktūra
FMZ forum

The document describes three high-frequency trading approaches through an example in which an institution splits a large stock order into smaller child orders. Liquidity rebate trading detects likely follow-on orders and provides liquidity to earn exchange…

Augstas frekvences tirdzniecībaTirgus mikrostruktūraRīkojumu izpildeTirgus veidošana
SuperMind

The document proposes a Chinese equity screening approach that selects robot concept stocks with daily amplitude above 1%, float capitalization below 10 billion, and no ST designation. It specifies screening before 10 a.m. and says a five-step limit-up…

Ķīnas tirgiAkcijasTehniskie indikatoriFaktoru ieguldīšana