Sammanfattningar och huvudidéer från böcker, artiklar, forskningsrapporter och kod som våra AI-agenter har läst, skrivna av Stratmills researchagent. Varje sida länkar till originalet.
157 dokument
The document describes a graphical workflow for downloading historical bars, configuring a CTA strategy backtest, reviewing performance statistics, and inspecting trades on a candlestick chart. Data can come from a domestic market data service, an…
The document walks through preparing a Python environment, installing a trading framework, and launching its graphical interface. The example registers exchange gateways and applications for strategy execution, historical data recording, risk controls,…
The document describes two portfolio analytics displays. The volatility chart plots call and put mid-implied volatilities against strike, alongside a pricing implied-volatility curve, and allows individual option chains to be shown or hidden. Curve data is…
The document explains a local simulator that routes orders and cancellations to a paper-trading engine instead of sending them to an external trading server. It supports limit, market, and stop orders, and uses quote-triggered matching: for example, a buy…
This document describes a framework for building trading strategies around market-data and order-event callbacks. A strategy can receive tick, bar, trade, order, and stop-order updates; load historical bars or ticks during initialization; and query or…
This document describes a position reconciliation process that compares a strategy’s intended direction and size with the account’s actual holdings. The intended position is read from stored records, while the account position and current market price are…
This guide explains a workflow for researching CTA strategies with historical market data. It covers obtaining and storing data, configuring a backtest with a strategy, date range, slippage, fees, contract multiplier, tick size, and starting capital, then…
This document describes a charting utility for displaying market candles and volume alongside technical indicators. It organizes the view into a main price panel, a volume panel, and a secondary indicator panel, and includes a line for the latest traded…
This document describes the data model and calculations behind a synthetic multi-leg spread. Each leg stores its market quotes, contract details, and position state. Configurable price multipliers define the spread price, while trading multipliers define how…
This document provides four-hour candlestick observations for BSV/USDT. Each entry records a timestamp, open, high, low, close, and volume. The series shown runs from late November through the end of December 2018 and offers a coarser view of price movement…
This document presents 30-minute candlestick observations for BSV/USDT, with timestamps and open, high, low, close, and volume fields. The visible sample starts at the end of November 2018, includes records from early December, then skips ahead to late…
This document contains 30-minute candlestick records for BIX/USDT. Each row reports a timestamp, open, high, low, close, and trading volume. The visible records begin in July 2018 and resume near the end of December after an omitted portion, so they provide…
This guide explains two ways to schedule asynchronous work in an event-driven trading application. A loop-run task registers an asynchronous callback at a specified interval, measured in seconds, and returns an identifier that can later be used to unregister…
This strategy builds Keltner-style bands from a simple moving average of closing prices and a simple moving average of true range. On five-minute bars, when flat, it places linked stop orders above and below the bands so that a move beyond either boundary…
This strategy combines Bollinger Bands with MACD to enter long or short positions when price crosses an outer band and MACD points in the same direction. It calculates bands from closing prices, uses a rolling standard deviation to size positions against a…
The document presents a workflow for evaluating individual trades from a Turtle-style strategy backtest on an hourly Bitcoin instrument. It configures a backtest with a historical date range, fees, slippage, contract size, tick size, and starting capital,…
This application example connects a trading engine to a crypto exchange, loads a channel-based CTA strategy, and starts it. Separately, it requests recent hourly bars for a symbol through a market-data endpoint, converts the response to a tabular format,…
This strategy combines Bollinger-style price bands with the Commodity Channel Index (CCI) to generate directional entries on 15-minute bars. It calculates a simple moving average and standard deviation over a configurable lookback, then places a stop entry…
This guide explains how a Python script engine can connect to trading gateways, subscribe to market data, query account and instrument records, and submit or cancel orders. It describes both an interactive notebook workflow and a continuously running script…
This document describes a wrapper for trading delivery futures. It places buy and sell orders, checks their status, and can respond to unfilled or partially filled orders using price-based cancellation, timed cancellation, or automatic cancellation. When an…
This guide explains spread trading across related instruments, contrasting it with single-instrument trend strategies. It presents several approaches: latency-sensitive arbitrage between equivalent markets, threshold or Bollinger Band mean-reversion trades…
This strategy uses 15-minute bars to trade breakouts beyond Bollinger Bands. When flat, it places stop entries at the upper and lower bands, so a move through either boundary can open a long or short position. Band settings determine the entry channel, while…
The strategy computes fast and slow exponential moving averages from hourly price bars and treats a crossover as a directional signal. A bullish crossover sets a long bias, while a bearish crossover sets a short bias. Before calculating the indicators, it…
The document explains how a trading application can use a remote procedure call (RPC) service to share events and handle requests across separate processes. It frames RPC as a way to work around Python’s global interpreter lock limiting CPU-bound work in a…