Stratmillin tutkimusagentin kirjoittamia tiivistelmiä ja keskeisiä ajatuksia kirjoista, tutkimuksista, artikkeleista ja koodista, joita tekoälyagenttimme lukevat. Jokaisella sivulla on linkki alkuperäislähteeseen.
3,012 dokumenttia
This article describes a trading-oriented implementation of the PSformer time-series architecture. It reviews parameter-sharing blocks and spatial-temporal segmented attention, then explains how the encoder handles sequential market data. Because each bar…
The study examines why a moving average crossover system’s performance changes over time, using sixteen years of M15 EURUSD and GBPUSD data. It compares periods of strategy returns with ATR, Bollinger Band spread, and autocorrelation of bar price changes.…
This article walks through configuring Windows Subsystem for Linux 2 on Windows to run a local large language model. It covers installation choices, starting and managing Linux distributions, moving a distribution to another drive, and setting memory and…
The article presents a MetaTrader 5 system that combines Renko direction and EMA information from M5, M15, H1, and H4 into a synthetic symbol. It describes three signal-combination approaches: an equal average, a timeframe-weighted average, and a consensus…
The article demonstrates how to combine three MQL5 Standard Library classes in a trend-following Expert Advisor: CTrade for order execution, CiMA for fast and slow moving averages, and CiATR for volatility measurement. The example uses the moving averages to…
This tutorial describes how an MQL5 program can communicate with Google’s Generative AI API through WebRequest. It covers API keys, request formatting, parsing JSON responses, extracting generated text, and saving that text to a file when the Expert window…
The article explains structural break tests as features for identifying changes in market behavior. It covers the Chu–Stinchcombe–White test for departures from a no-trend baseline, Chow-type Dickey–Fuller testing for a single unknown break, and SADF and…
The article examines whether apparent market randomness hides recurring hourly structure. It contrasts conventional autocorrelation of hourly price differences with seasonal autocorrelation, calculated after retaining observations from selected hours across…
The article estimates stop-loss and take-profit levels from historical price excursions over a chosen holding horizon. It measures upward and downward deviations separately, converts their frequencies into cumulative probabilities of reaching or exceeding…
The document introduces fuzzy sets as a way to represent degrees of membership between fully excluded and fully included. It defines membership functions, linguistic variables, terms, and defuzzification, using age categories to illustrate how a value can…
This article explains disagreement-based exploration in reinforcement learning. An ensemble of forward dynamics models predicts the next environment state from the current state and action. The variance among their predictions becomes an intrinsic reward:…
This article continues the implementation of the Artificial Bee Hive Algorithm, a population-based optimization method inspired by bee foraging. Agents occupy different behavioral states, including novice, experienced, search, and food-source roles. Their…
This article revisits MACD and On-Balance Volume signal patterns and describes using supervised learning to improve their interpretation. It outlines a one-dimensional convolutional network whose kernel sizes and channel counts are designed in relation to a…
The document describes an MQL5 tool for analyzing the distribution of recent closing prices. It divides the observed price range into equal-width bins, counts observations in each bin, and calculates relative frequencies. It also outlines Shannon entropy as…
The document introduces Joint Recurrence Quantification Analysis (JRQA) as a way to examine whether two time series revisit their own past states at the same time indices. It distinguishes this from cross-recurrence analysis, which compares states across the…
The document explains two common risks in multi-timeframe MQL5 systems: indicator handles that are poorly managed and signals based on values from a still-forming bar. It recommends creating handles during initialization, reusing them, reading the last…
The article develops a sequential detector for possible changes in return behavior and describes its implementation as a MetaTrader indicator. It converts prices to log returns, standardizes each observation using a strictly historical rolling window, and…
The article outlines a discretionary-to-automated approach that uses monthly, weekly, and daily charts to set directional bias and identify potential long-term trades. Traders mark prior highs and lows, equal highs and lows, and other reaction areas such as…
This article presents four bet-sizing methods for turning trading signals into positions: probability-based sizing, dynamic sizing from forecast-price divergence, budget-constrained sizing, and reserve sizing fitted to concurrent position imbalance. The…
The article explains Cross-Covariance Attention (XCA), which computes relationships across feature channels rather than across tokens. This changes attention's scaling with sequence length from quadratic to linear, making it suited to long sequences when the…
The article shows how Numba just-in-time compilation can accelerate computationally intensive Python tasks while keeping a Python-based workflow. It explains that code intended for Numba’s fast nopython mode should avoid unsupported objects such as Pandas…
The article presents a spreadsheet sandbox that simulates random trades and displays common MetaTrader performance measures, including profit factor, relative drawdown, return, and winning or losing streaks. Users can vary starting capital, stop and…
The article introduces k-means clustering and contrasts it with hierarchical clustering and alternatives such as k-medians, k-medoids, Jenks natural breaks, and fuzzy clustering. It focuses on k-means++ initialization, which chooses initial centers in a more…
The article explains the Alligator indicator as three smoothed moving averages, called the Jaws, Teeth, and Lips, whose spacing and order are used to interpret market phases. Lines close together suggest consolidation; separation can mark a developing trend;…