This strategy implements a simple grid approach that always keeps one long and one short position open. When the market moves enough to hit the take profit of one side, the opposite side is closed as well and the next grid level is opened with a larger volume. The volume grows geometrically according to the VolumeMultiplier parameter.
Parameters
Parameter
Description
TakeProfitPoints
Take profit distance measured in price steps.
InitialVolume
Volume used for the first pair of orders.
VolumeMultiplier
Multiplier applied to the volume for each new grid level.
MaxTrades
Maximum number of grid levels allowed.
CandleType
Candle data type used to trigger the strategy logic.
Trading Logic
Start – The strategy subscribes to the specified candle series and opens the first pair of buy and sell market orders.
Monitoring – On each finished candle the last price is checked against the entry prices. If the profit target for one side is reached, both positions are closed.
Grid Progression – After closing all positions the next grid level is opened with volume multiplied by VolumeMultiplier.
Limits – The process repeats until MaxTrades levels are opened.
The strategy does not use any indicators or complex calculations which makes it suitable for demonstration of order management and position handling within StockSharp.
Notes
All comments in the code are written in English as required.
The strategy uses the high-level API with SubscribeCandles for market data.
using System;
using System.Linq;
using System.Collections.Generic;
using Ecng.Common;
using Ecng.Collections;
using Ecng.Serialization;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Messages;
namespace StockSharp.Samples.Strategies;
/// <summary>
/// Grid strategy using EMA mean-reversion.
/// Buys when price drops below EMA by a threshold, sells when it rises above.
/// </summary>
public class BuySellGridStrategy : Strategy
{
private readonly StrategyParam<decimal> _gridStepPct;
private readonly StrategyParam<int> _emaPeriod;
private readonly StrategyParam<DataType> _candleType;
private decimal _entryPrice;
/// <summary>
/// Grid step as percentage from EMA.
/// </summary>
public decimal GridStepPct
{
get => _gridStepPct.Value;
set => _gridStepPct.Value = value;
}
/// <summary>
/// EMA period for mean reference.
/// </summary>
public int EmaPeriod
{
get => _emaPeriod.Value;
set => _emaPeriod.Value = value;
}
/// <summary>
/// Candle type used to trigger strategy logic.
/// </summary>
public DataType CandleType
{
get => _candleType.Value;
set => _candleType.Value = value;
}
/// <summary>
/// Initializes a new instance of <see cref="BuySellGridStrategy"/>.
/// </summary>
public BuySellGridStrategy()
{
_gridStepPct = Param(nameof(GridStepPct), 0.3m)
.SetDisplay("Grid Step %", "Distance from EMA for grid entry", "General")
.SetGreaterThanZero();
_emaPeriod = Param(nameof(EmaPeriod), 20)
.SetDisplay("EMA Period", "EMA period for grid center", "Indicators")
.SetGreaterThanZero();
_candleType = Param(nameof(CandleType), TimeSpan.FromHours(4).TimeFrame())
.SetDisplay("Candle Type", "Candle type for processing", "General");
}
/// <inheritdoc />
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
return [(Security, CandleType)];
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
var ema = new ExponentialMovingAverage { Length = EmaPeriod };
var subscription = SubscribeCandles(CandleType);
subscription
.Bind(ema, ProcessCandle)
.Start();
}
private void ProcessCandle(ICandleMessage candle, decimal emaValue)
{
if (candle.State != CandleStates.Finished)
return;
var close = candle.ClosePrice;
var lowerGrid = emaValue * (1m - GridStepPct / 100m);
var upperGrid = emaValue * (1m + GridStepPct / 100m);
if (Position == 0)
{
if (close <= lowerGrid)
{
BuyMarket();
_entryPrice = close;
}
else if (close >= upperGrid)
{
SellMarket();
_entryPrice = close;
}
}
else if (Position > 0)
{
// Take profit at EMA or above
if (close >= emaValue)
{
SellMarket();
}
// Add on further dip
else if (close <= _entryPrice * (1m - GridStepPct / 100m))
{
BuyMarket();
_entryPrice = close;
}
}
else if (Position < 0)
{
// Take profit at EMA or below
if (close <= emaValue)
{
BuyMarket();
}
// Add on further rally
else if (close >= _entryPrice * (1m + GridStepPct / 100m))
{
SellMarket();
_entryPrice = close;
}
}
}
/// <inheritdoc />
protected override void OnReseted()
{
base.OnReseted();
_entryPrice = 0m;
}
}
import clr
clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")
from System import TimeSpan
from StockSharp.Messages import DataType, CandleStates
from StockSharp.Algo.Indicators import ExponentialMovingAverage
from StockSharp.Algo.Strategies import Strategy
class buy_sell_grid_strategy(Strategy):
def __init__(self):
super(buy_sell_grid_strategy, self).__init__()
self._grid_step_pct = self.Param("GridStepPct", 0.3) \
.SetDisplay("Grid Step %", "Distance from EMA for grid entry", "General")
self._ema_period = self.Param("EmaPeriod", 20) \
.SetDisplay("EMA Period", "EMA period for grid center", "Indicators")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromHours(4))) \
.SetDisplay("Candle Type", "Candle type for processing", "General")
self._entry_price = 0.0
@property
def grid_step_pct(self):
return self._grid_step_pct.Value
@property
def ema_period(self):
return self._ema_period.Value
@property
def candle_type(self):
return self._candle_type.Value
def OnReseted(self):
super(buy_sell_grid_strategy, self).OnReseted()
self._entry_price = 0.0
def OnStarted2(self, time):
super(buy_sell_grid_strategy, self).OnStarted2(time)
ema = ExponentialMovingAverage()
ema.Length = self.ema_period
subscription = self.SubscribeCandles(self.candle_type)
subscription.Bind(ema, self.process_candle).Start()
def process_candle(self, candle, ema_value):
if candle.State != CandleStates.Finished:
return
ema_value = float(ema_value)
close = float(candle.ClosePrice)
step = float(self.grid_step_pct)
lower_grid = ema_value * (1.0 - step / 100.0)
upper_grid = ema_value * (1.0 + step / 100.0)
if self.Position == 0:
if close <= lower_grid:
self.BuyMarket()
self._entry_price = close
elif close >= upper_grid:
self.SellMarket()
self._entry_price = close
elif self.Position > 0:
if close >= ema_value:
self.SellMarket()
elif close <= self._entry_price * (1.0 - step / 100.0):
self.BuyMarket()
self._entry_price = close
elif self.Position < 0:
if close <= ema_value:
self.BuyMarket()
elif close >= self._entry_price * (1.0 + step / 100.0):
self.SellMarket()
self._entry_price = close
def CreateClone(self):
return buy_sell_grid_strategy()