Gold Sparrow
- Experts
- Version: 2.0
- Mise à jour: 24 octobre 2026
IMPORTANT: Please Read Before Using This EA
Gold Sparrow is an experimental product. It does not offer the same level of stability as a commercial paid EA, nor does it guarantee profits.
Live trading may involve substantial drawdowns. Please use only money you can afford to lose, and avoid setting the LotFactor parameter too high. The recommended range is 0.1–0.2.
1. Basic Information
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Recommended timeframe: H1 (1-hour chart)
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Recommended minimum balance: JPY 60,000–80,000 per 0.01 lot
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Recommended brokers: Brokers using GMT+3 during daylight saving time and GMT+2 during standard time, such as TitanFX
Gold Sparrow is a trading robot developed using approximately 15 years of historical XAUUSD (gold) chart data.
The EA follows market trends when entering trades. However, sudden trend reversals may result in substantial drawdowns.
During sideways or ranging markets, the EA may not open any trades for one or two weeks. This is intentional and helps the EA focus on trade quality rather than trading frequency.
To help limit drawdowns caused by sudden adverse price movements, we recommend setting MaxHoldingHours between 5 and 9.
Using Gold Sparrow with Exness
Exness is not a recommended broker for this EA. If you choose to use Exness, we recommend the following parameter settings:
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TakeProfit = 150000
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StopLoss = 45000
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MaxSpread = 800
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Slippage = 500
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WeekEndCloseTime = 18
These settings are intended specifically for Exness. Settings for other brokers may need to be adjusted according to their trading conditions.
2. Downloading and Using the Latest Model
Gold Sparrow can download and use an updated model trained on more recent market data.
To enable this feature, follow the instructions below.
Step 1: Allow the URL in MT5
In MetaTrader 5, open:
Tools → Options → Expert Advisors
Add the following URL to the list of URLs permitted for WebRequest:
https://greentrailns-stack.github.io/MetaTrader/gold_sparrow2_model.json
Important: The URL was changed in an update. Make sure you register the URL shown above.
Step 2: Download the Model File
After adding the URL, attach Gold Sparrow to a chart.
The model file, gold_sparrow2_model.json, will be downloaded to the Files folder inside the MT5 Common data folder.
Step 3: Enable the Latest Model
Set the UseRecentModel parameter to true.
Gold Sparrow will then use the downloaded updated model for trading.
Important: The model file must be downloaded before you can run a backtest with this setting. Make sure the file is available before starting the test.
While Gold Sparrow is running, it will automatically update the model file when a newer version becomes available.
3. Parameter Descriptions
TakeProfit / StopLoss
These parameters define the take-profit (TP) and stop-loss (SL) levels. Values are specified in points.
AutoTPSL
When enabled, the EA automatically sets the initial TP and SL at the time of entry, based on the ATR of the H1 timeframe.
Chandelier Trailing
This feature moves the stop-loss of profitable positions in a favorable direction using H1 highs/lows and ATR.
The stop-loss is never moved in the unfavorable direction, and the take-profit level is not modified.
Using AutoTPSL and Chandelier Trailing Together
These two features are designed to be used together.
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AutoTPSL: Sets the initial TP and SL when a trade is opened.
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Chandelier Trailing: Takes over afterward and moves the SL in a favorable direction as market conditions change.
When using both features, set ChandelierMultiplier to a value lower than AutoTPSL_SLMultiplier.
If ChandelierMultiplier is too high, the Chandelier Trailing stop may be unable to move beyond the initial stop-loss level and may never activate.
We also recommend setting ChandelierStepPoints to approximately 20 rather than 0.
HoldingHours / MaxHoldingHours
Gold Sparrow includes time-based position management.
HoldingHours
When the specified number of hours has elapsed since entry, the EA closes the position if it is profitable.
If the position is still at a loss at that time, the EA does not close it immediately. Instead, it waits until the position reaches the profit threshold specified by MinProfit.
MaxHoldingHours
When the maximum holding time has elapsed, the EA closes the position regardless of whether it is profitable or at a loss.
This means that a position can remain open beyond HoldingHours while it is losing, but it will be closed when MaxHoldingHours is reached.
MinProfit
This parameter specifies the target profit used when closing a position that is still losing after HoldingHours has elapsed. The required profit is calculated according to the position's lot size.
For example, if the position size is 0.01 lot and MinProfit = 100, the required profit is:
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JPY account: JPY 100
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USD account: 100 cents (USD 1)
For Micro accounts and other account types with different currency units or profit-calculation conventions, adjust MinProfit as necessary.
Important Notice Regarding Backtest Results
Gold Sparrow is developed using a model trained on historical market data that includes recent price movements. As a result, the EA may show favorable backtest performance.
However, if price data from the backtest period was also used to train the model, the results do not represent an independent evaluation on entirely unseen market data. Therefore, the backtest results should be regarded as theoretical reference results and should not be taken as an indication of the performance that can be expected in live trading.
Future market conditions may differ significantly from the historical data used for training. Actual trading results may therefore differ substantially from the backtest results, and significant drawdowns are possible.
4. Final Notes
Good backtest results do not guarantee future profits or similar results in live trading.
Gold Sparrow is an experimental EA, and its performance may vary depending on market conditions. Substantial losses are possible.
We strongly recommend running the EA on a demo account for an adequate period to evaluate its behavior and trading results before considering live trading.

Hello, I updated version 1.7 today, but during my backtesting, I found that when UseRecentModel was set to true, the backtesting performance was much lower than when it was set to false. Is the new parameter actually more unsuccessful? Looking forward to your explanation to clarify my confusion, thank you!
Edit:Thank you for your reply! So, in the long run, do you recommend setting it to 'true'? Or rather, set it to 'false' normally, and when the model is updated, set it to 'true' to download the latest model, and then change it back to 'false'. As you said, choose the high-performance one from multiple candidates (including the latest model just downloaded). But this seems a bit troublesome haha.