Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
42 documents
This documentation explains how to run a TqSdk strategy over historical data without changing its core logic, and how to retrieve trade logs and account statistics when the simulation ends. It describes catching a backtest-finished event, accessing summary…
This code describes a mean-reversion strategy for the spread between Dalian Commodity Exchange coke and coking coal futures. It calculates a weighted value spread using contract prices, contract multipliers, and a specified leg ratio, then estimates the…
This comparison explains differences between TqSdk and vn.py that matter when adapting existing trading strategies. vn.py is presented as an integrated package with market data, trading connections, storage, and interface components. TqSdk instead uses…
This guide explains a replay mode for reviewing a trading strategy against historical market data for a chosen trading day. Unlike event-driven backtesting, replay is time-driven: the service streams the day’s historical data for subscribed contracts,…
This reference distinguishes local simulation accounts from remote Quick simulated accounts for futures and stocks. It describes TqSim as a local futures simulation option for development and backtests, TqKq as a Quick linked futures account, and…
The document argues that trading systems should be written so that changes to strategy logic require only localized code edits. It illustrates this with an R-Breaker example: if backtesting suggests that holding positions overnight adds risk without enough…
This reference organizes common TqSdk problems by symptom and suggests likely causes and corrective checks. It covers empty or stale market data, queued order requests that have not been sent through an update cycle, target-position tasks that fail to act,…
This example describes a futures strategy using the Volume Price Trend (VPT) indicator on daily bars. It updates VPT by adding volume multiplied by the latest percentage price change, then compares the current value with a moving average. A trade is…
This beginner guide introduces Python syntax and core programming constructs that are useful when starting quantitative strategy research. It covers indentation, comments, assignment, imports, basic types and arithmetic, comparisons, and conditional logic.…
This documentation explains design choices behind TqSdk, a Python trading software development kit. It aims to avoid imposing a strategy model: users can fetch data and issue orders freely, while examples demonstrate possible applications instead of…
This report utility converts daily account snapshots and trade records into tables, then calculates summary statistics for simulated futures accounts or stock accounts. For both account types it derives daily profit and returns, cumulative profit and loss…
The document explains how to enable TqSdk’s browser-based chart interface by setting the API’s web GUI option. It describes using an automatically assigned local address or supplying a fixed address, then illustrates a live setup that subscribes to a futures…
This framework overview explains TqSdk’s component layout and message flow. It describes TqChan as a one-way queue between components and outlines how order messages travel from user code through TqApi and TqAccount to a trading gateway. In the reverse…
The script describes a two-sided futures strategy on hourly bars. It identifies confirmed swing low and swing high fractals, then enters long when price breaks above a bullish fractal’s high during a short-over-long moving-average uptrend. It enters short…
The document explains a terminal feature that replays an entire historical trading day. A user chooses a date when launching the replay version of the terminal, then uses the software and its extensions as though operating during that session. Playback can…
The strategy models a refining spread using crude oil, fuel oil, and a third petroleum product in a 3:2:1 weighting. It calculates the spread as the weighted value of the two product legs minus the weighted crude leg, then compares the current spread with…
This example implements a daily mean-reversion strategy for a Shanghai Futures Exchange gold contract. It calculates a Z-score from recent closing prices, enters long when the score falls below a negative entry threshold and short when it rises above a…
This reference describes a Python toolkit for calculating technical indicators and analyzing trading returns on pandas time series. Its functions cover lagged values, rolling standard deviation and simple averages, exponentially or linearly weighted…
This script describes a three-leg futures strategy that treats hog value minus weighted corn and soybean meal costs as a proxy for livestock feeding profitability. It estimates the spread’s mean and standard deviation from daily bars, calculates a z-score,…
The script demonstrates a calendar spread strategy for two nearby equity index futures contracts. It calculates the spread between their closing prices over a rolling window, estimates the mean and standard deviation, and sets upper and lower thresholds two…
The script describes a mean-reversion strategy that trades a spread between two steel futures contracts. It collects daily closes, standardizes each contract’s recent prices over a rolling window, and subtracts the standardized series to form a spread. A…
This futures example computes daily pivot, support, and resistance levels from the prior session's high, low, and close. It trades a copper contract by entering long when price falls below first support or short when it rises above first resistance.…
This futures strategy tracks the ratio of copper to aluminum contract values, adjusting each contract’s daily close by its volume multiplier. It calculates the historical mean and standard deviation of that ratio, then uses the current ratio’s z-score to…
This example describes a three-leg futures strategy that treats polyester fiber value minus the weighted costs of PTA and ethylene glycol as a production margin. It estimates the margin’s mean and standard deviation from recent daily bars, then calculates a…