Repository: freqtrade/freqtrade

Issue:
Backtesting with missing candels and 1000% higher price afterwards
======== SUMMARY METRICS ======
| Metric                      | Value                      |
|-----------------+--------------|
| Starting balance          | 1000 BNB           |
| Final balance               | 8859.982 BNB    |
| Total profit %              | 786.00%             |

So much profit.... It's because of missing candles

![image](https://user-images.githubusercontent.com/563328/189613084-863e0df2-cabd-478f-8e51-b2af7e19be0e.png)

**How can I avoid this problem in backtesting?** 
If I have an open position, the candels are missing, afterwards the price is 1000% higher than before.


Discussion:
Please get used to fill out the issue template. Repeat offenders to this will have their issues closed without further comment.
We're asking some very basic questions - by deleting the issue template, you're explicitly choosing not to answer our questions - which makes me doubt why you think we should chose to answer your questions.

---

Freqtrade itself will try hard to not download data with huge gaps in it.

If you encounter missing candles - best delete the affected pair, and re-download the data again. (i'd be curious how you came to this data - but the probability of having the download-data logs for this available is most likely pretty low) - so i'm not even asking for it actively (that can have happened weeks ago ...).
deleted the pair, did a download of that specific date range:

> (.env) root@s2 /home/lenny/freqtrade # freqtrade download-data -p COCOS/BNB -t 5m --timerange=20210118-20210125
> 2022-09-12 14:15:37,194 - freqtrade.configuration.load_config - INFO - Using config: config.json ...
> 2022-09-12 14:15:37,195 - freqtrade.loggers - INFO - Verbosity set to 0
> 2022-09-12 14:15:37,195 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20210118-20210125 ...
> 2022-09-12 14:15:37,195 - freqtrade.configuration.configuration - INFO - Using user-data directory: /home/lenny/freqtrade/user_data ...
> 2022-09-12 14:15:37,195 - freqtrade.configuration.configuration - INFO - Using data directory: /home/lenny/freqtrade/user_data/data/binance ...
> 2022-09-12 14:15:37,195 - freqtrade.configuration.configuration - INFO - Using pairs ['COCOS/BNB']
> 2022-09-12 14:15:37,195 - freqtrade.configuration.configuration - INFO - timeframes --timeframes: ['5m']
> 2022-09-12 14:15:37,196 - freqtrade.configuration.check_exchange - INFO - Checking exchange...
> 2022-09-12 14:15:37,207 - freqtrade.configuration.check_exchange - INFO - Exchange "binance" is officially supported by the Freqtrade development team.
> 2022-09-12 14:15:37,207 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
> 2022-09-12 14:15:37,211 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled
> 2022-09-12 14:15:37,211 - freqtrade.exchange.exchange - INFO - Using CCXT 1.92.52
> 2022-09-12 14:15:37,227 - freqtrade.exchange.exchange - INFO - Using Exchange "Binance"
> 2022-09-12 14:15:37,227 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Binance'...
> 2022-09-12 14:15:37,227 - freqtrade.exchange.exchange - INFO - Markets were not loaded. Loading them now..
> 2022-09-12 14:15:38,809 - freqtrade.commands.data_commands - INFO - About to download pairs: ['COCOS/BNB'], intervals: ['5m'] to /home/lenny/freqtrade/user_data/data/binance
> 2022-09-12 14:15:38,810 - freqtrade.data.history.history_utils - INFO - Downloading pair COCOS/BNB, interval 5m.
> 2022-09-12 14:15:38,811 - freqtrade.data.history.history_utils - INFO - (1/1) - Download history data for "COCOS/BNB", 5m, spot and store in /home/lenny/freqtrade/user_data/data/binance. From 2021-01-18T01:00:00 to 2021-01-25T01:00:00
> 2022-09-12 14:15:40,028 - freqtrade.exchange.exchange - INFO - Downloaded data for COCOS/BNB with length 3000.
> 2022-09-12 14:15:40,038 - freqtrade.exchange.exchange - INFO - Closing async ccxt session.

github does not allow json files, so here is the raw json:

<details>
  <summary>COCOS_BNB-5m.json</summary>

```json
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```
</details>

you can see the missing data is still the same:

[1611021300000,0.0000151,0.0000155,0.0000151,0.0000154,3443372.0],
[1611367200000,0.0154,0.0249,0.01214,0.01621,40493.0],

all the newest freqtrade - but I think there will never be a solution, because sometimes candles are missing and the price will jump quite a bit as soon as the candles are back.

So this is a more high level question:
**How to handle missing candles with big price jumps if you are holding that position (in backtesting/hyperot)?**

Shouldn't there be something like "if more than X candles are missing and the price diff is more than Y%, force exit that position to the old price" ?

Perhaps the precision of a pair can change on the exchange, with a new contract etc....
seems like 4 days are missing - but have a huge jump in between ...
Maybe the pair wasn't trading at that time?

well from freqtrade's perspective, that's quite difficult to handle (we'll need to also think "what would've happened if this were live").
From the data available via API - all we can see is that there's 4 days of missing candles - so either the exchange was down (would affect all candles) - the pair was suspended (affects only this pair).

Selling at "last old price" is probably the worst solution - you don't have that option in live, either (if the pair is suspended, worst case may be a loss of 100%).
You have to assume that you've been in that position in live - too - so you'd have had to wait for the pair to "reappear" - with whatever changes that happened in between.

Now maybe there was a token exchange (maybe 1000:1 or so) - but that's information we don't have / which the API doesn't provide- so can't account into backtesting results.

As is - i don't see any way to handle this "properly" - so we can either assume the position as a 100% loss (it sold at 0.0) - or accept eventual downsides of not having that information.

A gap (of more than X) won't even help us identify such jumps - they could've done such an exchange within an hour - which can however also be a regular exchange downtime - where we'd clearly not want this to trigger.


Yes, there is no good solution - but atm we have no solution at all :-D

Here is the reason what happened: https://www.binance.com/en/support/announcement/f555f8013eff42bc9c91eaa48b9da24b

But this can happen all the time to many pairs and exchanges... So I think a solution like "if candles missing and price change of X% ---> do something with open positions" is needed (whatever to do with them). At least a huge error message during hyperopt/backtesting...
> if candles missing

that's a premise that may be true for this case - but is not guaranteed for future token swaps (not even for future token swaps based on 2021-01 - which would've happened between then and now.

I currently don't see this as something freqtrade will / can provide - simply because of the lack of data, which means most of the things will be based on assumptions (including "price change of X%, Y candles missing").
We can take assumptions - but while the assumption is true in this case - it may be wrong for 90% of the other cases - so it's actually a very bad solution.

If you find an announcement (valid for all exchanges) - that such token swaps MUST have a pair downtime of 2+ days - we can talk again - but i doubt there exists such a thing - letalone one that's valid across exchanges.
Ok, what about a warning if the pricediff is higher than X% between two candles? f.e. 5m candles, a price diff of 10.000% is something not happening.
a way to handle that may be to detect outliers :
-> you make a rolling of 100 candles (to have a valid sample if the mean change from 1 to 1000 because Redenomination Plan -> if you take all the candles you mean will be not consistent ) and apply a [outlier function](https://stephenallwright.com/remove-outliers-pandas/) on each candle with a large std threshold like 5-6 (with represent a probability to append respectively 1 in 174427  and 1 in 26330254 in normal distribution [sigma values and probability](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule#Table_of_numerical_values) , trading returns quite follow this type) and return a warning with the date impacted (you can invest to exclude this pair from backtest).
-> do it only one at the end of the data-downloading as rolling with a function that compute the mean and std may have compute cost.



@Kipperlenny what markets do you use for backtesting?
dangerous markets ;-) f.e. the BNB pairs on binance with low volume most of the time.
@Kipperlenny I personally use following ways to feed data:
- wrote custom data feeding from gateio, ftx, binance that fetches all of the trades in real time, been running for about 6 months already.
- Meta Trader allows to fetch 2-10 years for spots markets for free. The data can be exported into CSV. So potentially can be easily fed into freqtrade for backtesting.
- https://www.reddit.com/r/algotrading/comments/xleral/yfinance_alteranative/ here's some post on reddit with more data sources
- as of free rest api usage by freqtrade, i'd say it's for the sake of demo.

Been working recently on support line indicator strategy. Just downloaded 10 spot pairs, 2 years from Metatrader. And believe me, still more than enough. The strategy by itself takes most of the time.

Did a stupid attempt before, took few months from gateio, wrote some bullshit that showed profit on backtest. Put the strategy in real trading with 1$ and quit within few weeks. That was just overfit.......

Resolving patch:
diff --git a/freqtrade/data/history/idatahandler.py b/freqtrade/data/history/idatahandler.py
--- a/freqtrade/data/history/idatahandler.py
+++ b/freqtrade/data/history/idatahandler.py
@@ -303,7 +303,7 @@ def ohlcv_load(self, pair, timeframe: str,
             timerange=timerange_startup,
             candle_type=candle_type
         )
-        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
+        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
             return pairdf
         else:
             enddate = pairdf.iloc[-1]['date']
@@ -323,8 +323,9 @@ def ohlcv_load(self, pair, timeframe: str,
             self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
             return pairdf
 
-    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
-                        candle_type: CandleType, warn_no_data: bool):
+    def _check_empty_df(
+            self, pairdf: DataFrame, pair: str, timeframe: str, candle_type: CandleType,
+            warn_no_data: bool, warn_price: bool = False) -> bool:
         """
         Warn on empty dataframe
         """
@@ -335,6 +336,20 @@ def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
                     "Use `freqtrade download-data` to download the data"
                 )
             return True
+        elif warn_price:
+            candle_price_gap = 0
+            if (candle_type in (CandleType.SPOT, CandleType.FUTURES) and
+                    not pairdf.empty
+                    and 'close' in pairdf.columns and 'open' in pairdf.columns):
+                # Detect gaps between prior close and open
+                gaps = ((pairdf['open'] - pairdf['close'].shift(1)) / pairdf['close'].shift(1))
+                gaps = gaps.dropna()
+                if len(gaps):
+                    candle_price_gap = max(abs(gaps))
+            if candle_price_gap > 0.1:
+                logger.info(f"Price jump in {pair}, {timeframe}, {candle_type} between two candles "
+                            f"of {candle_price_gap:.2%} detected.")
+
         return False
 
     def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str,


Test patch:
diff --git a/tests/data/test_datahandler.py b/tests/data/test_datahandler.py
--- a/tests/data/test_datahandler.py
+++ b/tests/data/test_datahandler.py
@@ -15,7 +15,7 @@
 from freqtrade.data.history.jsondatahandler import JsonDataHandler, JsonGzDataHandler
 from freqtrade.data.history.parquetdatahandler import ParquetDataHandler
 from freqtrade.enums import CandleType, TradingMode
-from tests.conftest import log_has
+from tests.conftest import log_has, log_has_re
 
 
 def test_datahandler_ohlcv_get_pairs(testdatadir):
@@ -154,6 +154,85 @@ def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
     assert df.columns.equals(df1.columns)
 
 
+def test_datahandler__check_empty_df(testdatadir, caplog):
+    dh = JsonDataHandler(testdatadir)
+    expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between"
+    df = DataFrame([
+        [
+            1511686200000,  # 8:50:00
+            8.794,  # open
+            8.948,  # high
+            8.794,  # low
+            8.88,  # close
+            2255,  # volume (in quote currency)
+        ],
+        [
+            1511686500000,  # 8:55:00
+            8.88,
+            8.942,
+            8.88,
+            8.893,
+            9911,
+        ],
+        [
+            1511687100000,  # 9:05:00
+            8.891,
+            8.893,
+            8.875,
+            8.877,
+            2251
+        ],
+        [
+            1511687400000,  # 9:10:00
+            8.877,
+            8.883,
+            8.895,
+            8.817,
+            123551
+        ]
+    ], columns=['date', 'open', 'high', 'low', 'close', 'volume'])
+
+    dh._check_empty_df(df, 'UNITTEST/USDT', '1h', CandleType.SPOT, True, True)
+    assert not log_has_re(expected_text, caplog)
+    df = DataFrame([
+        [
+            1511686200000,  # 8:50:00
+            8.794,  # open
+            8.948,  # high
+            8.794,  # low
+            8.88,  # close
+            2255,  # volume (in quote currency)
+        ],
+        [
+            1511686500000,  # 8:55:00
+            8.88,
+            8.942,
+            8.88,
+            8.893,
+            9911,
+        ],
+        [
+            1511687100000,  # 9:05:00
+            889.1,   # Price jump by several decimals
+            889.3,
+            887.5,
+            887.7,
+            2251
+        ],
+        [
+            1511687400000,  # 9:10:00
+            8.877,
+            8.883,
+            8.895,
+            8.817,
+            123551
+        ]
+    ], columns=['date', 'open', 'high', 'low', 'close', 'volume'])
+
+    dh._check_empty_df(df, 'UNITTEST/USDT', '1h', CandleType.SPOT, True, True)
+    assert log_has_re(expected_text, caplog)
+
+
 @pytest.mark.parametrize('datahandler', ['feather', 'parquet'])
 def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
     dh = get_datahandler(testdatadir, datahandler)