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.
Dokumentu skaits: 7,910
The Dual Thrust method sets breakout thresholds from recent price ranges, then anchors them to the next session’s opening price. It calculates a range using the larger of two lookback measures: the highest high minus the lowest close, or the highest close…
This strategy uses Donchian Channel boundaries to enter trades when price breaks beyond recent highs or lows. Long entries use an upper-channel breakout and exit at the lower boundary; short entries reverse those rules. Channel lookback lengths can differ…
This strategy template generates signals when the MACD line crosses its signal line, with configurable fast, slow, and signal periods. By default, it filters long entries for a rising simple moving average and short entries for a falling one. Users can…
This example shows how to customize a trading algorithm’s fill model so a market order is completed in portions. It attaches a custom model to an hourly equity subscription, then overrides market-fill handling to cap each simulated fill at a fixed quantity.…
This strategy combines a 200-period EMA as a broad direction filter, a 50-period EMA to define a pullback zone, and a WaveTrend-style oscillator crossover as the entry trigger. It goes long when price is above the 200 EMA but below the 50 EMA and the fast…
This strategy measures momentum by comparing a selected price series with its value a chosen number of periods earlier. It then combines that lookback momentum with either the change in momentum from the prior bar or the one-bar price change. A long signal…
The strategy opens a long position when the 14-period RSI crosses below 30, then records the entry price and sets a stop 1.5% below it. It closes the position when the bar’s closing price falls below that stop, clears the stored prices, and waits for another…
This strategy uses a 20-period simple moving average as the Bollinger middle band, with upper and lower bands formed using two standard deviations. It treats a close crossing above the middle band as a long signal and describes a cross below as a short…
This strategy combines a 123-style reversal signal with a volume-weighted exponential moving average (EMA). The reversal component checks recent closing-price patterns and stochastic values; the second component compares an EMA of volume-weighted prices with…
This strategy builds on MACD crossovers by adding RSI and volume conditions. A long signal requires a bullish MACD crossover above zero, RSI below its overbought threshold, and volume above its moving average. A short signal requires a bearish crossover…
This short-term strategy combines several technical signals to seek directional entries. It uses fast and slow Hull moving averages, Ichimoku lines and cloud structure, Donchian channel levels, and MACD crossovers. A long signal requires bullish conditions…
This strategy uses three exponential moving averages to classify directional conditions. When the shortest average is above the middle average and the middle is above the longest, it signals an uptrend; the reverse ordering signals a downtrend. It enters…
This strategy trains an XGBoost model on six price and volume features: normalized RSI, distance from a 50-period EMA, rate of change, ATR relative to price, relative volume, and the close's location within a recent price range. It periodically fits the…
This strategy uses Keltner Channel bands to frame directional entries. A move above the upper band triggers a prospective long entry, while a break below the lower band triggers a short entry. It also describes conditions for canceling pending entries when…
The available source describes a long-oriented reversal strategy that identifies a bullish bar when its close is above the bar's midpoint and its low matches the lowest low over the previous seven bars. It also calculates confirmed pivot highs and lows,…
This strategy calculates Williams %R over a configurable lookback and enters long when the indicator crosses upward through -80 or short when it crosses downward through -20. It offers configurable take-profit and stop-loss distances, optional trading hours,…
This long-only strategy enters when a fast simple moving average crosses above a slower one. While the trade is active, it calculates a stop as a fixed percentage below the bar’s high and carries forward the higher of that stop and the previous stop. The…
This strategy compares short and long exponential moving averages, entering long on an upward cross or short on a downward cross. It sets percentage-based stop-loss and profit-target levels from the entry price, and offers controls for long-only or…
This trend-following method uses the Ichimoku conversion line and base line, calculated from median prices over 9 and 26 days, to generate signals. A conversion-line cross above the base line triggers a long entry only when price is above the 200-day EMA; a…
This intraday strategy uses fast and slow simple moving averages to follow short-term trends. A cross above the slower average opens a long position, while a cross below opens a short. Users can set the average lengths, a per-trade monetary stop, a trailing…
This strategy combines a smoothed Stochastic oscillator with high and low simple moving averages. It describes a long signal when Stochastic crosses upward through a risk-mode threshold while price is below the low SMA, and a short signal when it crosses…
This strategy combines three technical signals: fast and slow EMA crossovers for trend changes, RSI thresholds for overbought or oversold conditions, and MACD line crossovers. Its signal logic can require any enabled indicator to trigger or require all…
This trend-following method compares a fast and a slow exponential moving average. The described defaults are 8 and 21 periods: a fast-line cross above the slow line signals bullish direction, while a cross below signals bearish direction. Users can select…
This example describes a monthly U.S. stock strategy that uses a machine-learning model to rank stocks by their predicted likelihood of outperforming the cross-sectional median. Its input features are past closing-price returns measured over several lookback…