Add example calculation
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Matthias
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@ -629,7 +629,7 @@ class AwesomeStrategy(IStrategy):
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The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
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For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
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`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease winning positions.
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`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
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`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
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@ -652,12 +652,12 @@ Position adjustments will always be applied in the direction of the trade, so a
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!!! Warning
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Stoploss is still calculated from the initial opening price, not averaged price.
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Regular stoploss rules still apply (cannot move down).
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!!! Warning "/stopbuy"
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While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
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!!! Warning "Backtesting"
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During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
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During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
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``` python
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from freqtrade.persistence import Trade
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@ -678,7 +678,7 @@ class DigDeeperStrategy(IStrategy):
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max_dca_multiplier = 5.5
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# This is called when placing the initial order (opening trade)
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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proposed_stake: float, min_stake: Optional[float], max_stake: float,
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leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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@ -757,6 +757,25 @@ def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: f
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```
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### Position adjust calculations
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* Entry rates are calculated using weighted averages.
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* Exits will not influence the average entry rate.
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* Partial exit relative profit is relative to the average entry price at this point.
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* Final exit relative profit is calculated based on the total invested capital. (See example below)
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??? example "Calculation example"
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*This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*
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* Buy 100@8\$
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* Buy 100@9\$ -> Avg price: 8.5\$
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* Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
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* Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
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* Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
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* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
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The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
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## Adjust Entry Price
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The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
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@ -485,6 +485,7 @@ class Telegram(RPCHandler):
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sumA += amount * filled_orders[y]["safe_price"]
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sumB += amount
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prev_avg_price = sumA / sumB
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# TODO: This calculation ignores fees.
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price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg)
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minus_on_entry = 0
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if prev_avg_price:
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@ -2835,7 +2835,7 @@ def test_order_to_ccxt(limit_buy_order_open):
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(('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
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(('sell', 50, 12), (150.0, 12.5, 1875.0, -25.0, -25.0, -0.04)),
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(('sell', 100, 20), (50.0, 12.5, 625.0, 725.0, 750.0, 0.60)),
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(('sell', 50, 5), (50.0, 12.5, 625.0, 725.0, -375.0, -0.60)),
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(('sell', 50, 5), (50.0, 12.5, 625.0, 350.0, -375.0, -0.60)),
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],
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'end_profit': 350.0,
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'end_profit_ratio': 0.14,
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@ -2847,7 +2847,7 @@ def test_order_to_ccxt(limit_buy_order_open):
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(('buy', 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
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(('sell', 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.044788)),
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(('sell', 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.59201995)),
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(('sell', 50, 5), (50.0, 12.5, 625.0, 713.8125, -377.1875, -0.60199501)),
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(('sell', 50, 5), (50.0, 12.5, 625.0, 336.625, -377.1875, -0.60199501)),
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],
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'end_profit': 336.625,
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'end_profit_ratio': 0.1343142,
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@ -2860,7 +2860,7 @@ def test_order_to_ccxt(limit_buy_order_open):
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(('sell', 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 1.189027)),
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(('buy', 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 1.189027)),
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(('sell', 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.7186579)),
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(('sell', 150, 23), (150.0, 11.0, 1650.0, 1388.5, 1787.25, 1.08048062)),
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(('sell', 150, 23), (150.0, 11.0, 1650.0, 3175.75, 1787.25, 1.08048062)),
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],
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'end_profit': 3175.75,
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'end_profit_ratio': 0.9747170,
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@ -2874,11 +2874,24 @@ def test_order_to_ccxt(limit_buy_order_open):
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(('sell', 100, 11), (100.0, 5.0, 500.0, 600.0, 600.0, 1.2)),
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(('buy', 150, 15), (250.0, 11.0, 2750.0, 600.0, 600.0, 1.2)),
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(('sell', 100, 19), (150.0, 11.0, 1650.0, 1400.0, 800.0, 0.72727273)),
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(('sell', 150, 23), (150.0, 11.0, 1650.0, 1400.0, 1800.0, 1.09090909)),
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(('sell', 150, 23), (150.0, 11.0, 1650.0, 3200.0, 1800.0, 1.09090909)),
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],
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'end_profit': 3200.0,
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'fee': 0.0,
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'end_profit_ratio': 0.98461538,
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'fee': 0.0,
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},
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{
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'orders': [
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(('buy', 100, 8), (100.0, 8.0, 800.0, 0.0, None, None)),
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(('buy', 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)),
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(('sell', 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.17647059)),
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(('buy', 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.17647059)),
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(('sell', 100, 12), (150.0, 10.0, 1500.0, 350.0, 350.0, 0.2)),
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(('sell', 150, 14), (150.0, 10.0, 1500.0, 950.0, 950.0, 0.40)),
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],
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'end_profit': 950.0,
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'end_profit_ratio': 0.283582,
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'fee': 0.0,
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},
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])
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def test_recalc_trade_from_orders_dca(data) -> None:
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@ -2940,7 +2953,7 @@ def test_recalc_trade_from_orders_dca(data) -> None:
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assert trade.open_rate == result[1]
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assert trade.stake_amount == result[2]
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# TODO: enable the below.
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# assert pytest.approx(trade.realized_profit) == result[3]
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assert pytest.approx(trade.realized_profit) == result[3]
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# assert pytest.approx(trade.close_profit_abs) == result[4]
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assert pytest.approx(trade.close_profit) == result[5]
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