OHLC_Candlestick_Patterns 3.0.0

OHLC_Candlestick_Patterns

Updates history

• 2026 Nov - Detection changes. - Optimization of detection. - New formations added. - Clean up.
• 2026 Jun - Added missing Enumerators.
• 2026 Mar - Licence changed to MIT.
• 2026 Jan - Mar - Performance speed increased by code optimisation. - Charting improved.
• 2026 Feb - Mar - Examples bugs fixed.

Purpose

The project was created due to the lack of a similar library for C#. It was preceded by the research for algorithmic formulas for individual OHLC patterns. As a result, you can test the OHLC candlestick list for:

• 40 bullish and 40 bearish candlestick patterns, 3 continuation patterns,
• 13 bullish, 13 bearish and additionally 2 continuation classic chart formations,
• 12 bullish, 12 bearish fibonacci chart patterns.

Additionally, you can test their effectiveness on the OHLCV data provided. A list of patterns, formations and an example of use can be found below.

The library is an analysis component, not a trading or investment recommendation. Validate results against your own market, timeframe, and execution rules.

NuGet package can be found here: https://www.nuget.org/packages/OHLC_Candlestick_Patterns

Features

• Counting bullish or bearish signals that appear in the OHLC list.
• Gives number of signals that appear in the OHLC list for selected multiple, single patterns or formation.
• Computes weightened index for the selected multiple or single pattern.
• Calculating signals for selected multiply, single pattern or formation.
• You can test selected pattern / formation effectiveness on the data provided.

Requirements and installation

• .NET 8.0 or later.
• NuGet package: OHLC_Candlestick_Patterns.

Additionally in the solution:

• Newtonsoft.Json in Console presentation project.
• ScottPlot in Console presentation project.

dotnet add package OHLC_Candlestick_Patterns

The package exposes the Candlestick_Patterns namespace for signal analysis and OHLC_Candlestick_Patterns for accuracy analysis. Newtonsoft.Json is used by the model attributes and is installed as a package dependency.

Patterns and Formations

Fibonacci Patterns

Complete list of supported Fibonacci patterns

Examples using of methods with Fibonacci patterns

Chart Formations

Complete list of supported chart Formations

Examples using of methods with chart Formations

Candlestick Patterns

Complete list of supported Candlestick Patterns

Quick start

using Candlestick_Patterns;

var candles = new List<OhlcvObject>
{
	new() { Open = 100m, High = 105m, Low = 98m, Close = 104m, Volume = 1_000m },
	new() { Open = 104m, High = 106m, Low = 101m, Close = 102m, Volume = 1_200m },
	new() { Open = 102m, High = 108m, Low = 100m, Close = 107m, Volume = 1_500m }
};

ISignals signals = new Signals();

int bullish = signals.GetPatternsBullishSignalsCount(candles);
int bearish = signals.GetPatternsBearishSignalsCount(candles);
int engulfing = signals.GetPatternsSignalsCount(candles, "Bullish Engulfing");
List<OhlcvObject> marked = signals.GetPatternsOhlcvWithSignals(candles, "Bullish Engulfing");

Detection methods are synchronous and accept an in-memory List<OhlcvObject>. Load and validate data asynchronously at the application boundary, then pass the resulting list to the SDK.

Input model: OhlcvObject

Each item represents one candle:

Provide candles in chronological order. Ensure High is not below Open/Close, Low is not above them, and remove incomplete or zero-price records according to the source's data policy. The library does not provide timestamps, so keep timestamps in a parallel collection when they are needed by the application.

Naming rules

The public API uses strings for pattern, formation, and Fibonacci names. Names are case-insensitive, leading/trailing whitespace is ignored, and spaces are ignored during lookup. For example, these forms resolve to the same candlestick method:

"Bullish Engulfing"
" bullish engulfing "
"BullishEngulfing"

Prefer the spaced names shown in the documentation. Use the exact supported name rather than relying on the compatibility behavior for unknown names. An unknown or blank name produces the input data with no matching signal, so validate user-supplied names before analysis.

Signal API

Instantiate Signals directly or through ISignals:

ISignals signals = new Signals();

Candlestick patterns

var count = signals.GetMultiplePatternsSignalsCount(
	candles,
	new[] { "Bearish Belt Hold", "Bearish Black Closing Marubozu" });

var index = signals.GetMultiplePatternsSignalsIndex(
	candles,
	new Dictionary<string, decimal>
	{
		["Bearish Belt Hold"] = 0.5m,
		["Bearish Black Closing Marubozu"] = 0.5m
	});

var marked = signals.GetPatternsOhlcvWithSignals(candles, "Bearish Engulfing");
var signalCount = marked.Count(x => x.Signal);

The weighted index is the sum of each selected signal count multiplied by its supplied weight. Weights are not normalized automatically; normalize them in the application when that is required by your scoring model.

Chart formations

Fibonacci patterns

GetFibonacciSignalsCount and GetFiboSignalsCount are compatibility aliases for the single Fibonacci count operation. The Fibo spelling is also used by the remaining Fibonacci methods.

Zigzag output model: ZigZagObject

Formation and Fibonacci methods return points rather than full candles:

For multi-name methods, returned lists follow the order of the input names. An empty name result is represented by an empty list in the multi-result APIs.

Accuracy trials

Use AccuracyTrials through IAccuracyTrials:

using OHLC_Candlestick_Patterns;

IAccuracyTrials accuracy = new AccuracyTrials();
AccuracyObject result = accuracy.GetAverPercentPatternAccuracy(
	candles,
	"Bullish Engulfing",
	candlesAheadQty: 30);

Console.WriteLine(result.AccuracyToAverageClose);
Console.WriteLine(result.AccuracyToEndClose);

AccuracyObject contains:

• AccuracyToAverageClose: comparison with average close prices after a signal.
• AccuracyToEndClose: comparison with the final close in the supplied evaluation range.

Positive values indicate movement in line with the detected direction; negative values indicate movement against it. These are historical measurements, not guarantees of future performance.

Accuracy method groups

topPercentage selects the best percentage of the available names. candlesAheadQty/candlesAhead changes the evaluation horizon; use a positive value appropriate for the data window and avoid interpreting a short horizon as a long-term result.

Supported names and examples

• Candlestick pattern list and pattern examples.
• Chart formation list and formation examples.
• Fibonacci pattern list and Fibonacci examples.

Common names include Bullish Engulfing, Bearish Dark Cloud Cover, Bullish Double Bottoms, Bearish Head And Shoulders, Bullish Gartley, and Bearish ABCD. The detailed lists are the source of truth for all supported names.

Data loading guidance

The SDK does not download market data. Deserialize your provider's JSON/CSV response into OhlcvObject values, check for missing or invalid prices, and preserve chronological order before calling analysis methods. The repository examples use Newtonsoft.Json and a public sample data URL, but production applications should use their own authenticated data provider and appropriate HTTP timeouts.

Repository examples

The solution includes console examples for patterns, formations, and Fibonacci analysis in Examples_Patterns, Examples_Formations, and Examples_Fibonacci. They demonstrate data loading, accuracy trials, counts, indexes, and marked outputs.

License

This project is licensed under the MIT License. See the repository LICENSE file.