Update strategy-customization documentation

docs/strategy-customization.md

@@ -58,12 +58,12 @@ file as reference.**
 
 !!! Note "Strategies and Backtesting"
     To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
-    that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
+    that during backtesting the full time range is passed to the `populate_*()` methods at once.
     It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
     avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
 
 !!! Warning "Warning: Using future data"
-    Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
+    Since backtesting passes the full time range to the `populate_*()` methods, the strategy author
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
 
@@ -251,7 +251,7 @@ minimal_roi = {
 While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
 
 To use times based on candle duration (timeframe), the following snippet can be handy.
-This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
+This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
 
 ``` python
 from freqtrade.exchange import timeframe_to_minutes
@@ -285,7 +285,7 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
 
 For more information on order_types please look [here](configuration.md#understand-order_types).
 
-### Timeframe (ticker interval)
+### Timeframe (formerly ticker interval)
 
 This is the set of candles the bot should download and use for the analysis.
 Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
@@ -333,10 +333,10 @@ class Awesomestrategy(IStrategy):
 #### Get data for non-tradeable pairs
 
 Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
-Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
+OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
 These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
 
-The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
+The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
 
 Sample:
 
@@ -349,8 +349,8 @@ def informative_pairs(self):
 
 !!! Warning
     As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
-    All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
+    All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange.
-    It is however better to use resampling to longer time-intervals when possible
+    It is however better to use resampling to longer timeframes whenever possible
     to avoid hammering the exchange with too many requests and risk being blocked.
 
 ***
@@ -363,10 +363,14 @@ All methods return `None` in case of failure (do not raise an exception).
 
 Please always check the mode of operation to select the correct method to get data (samples see below).
 
+!!! Warning "Hyperopt"
+    Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()`.
+    It is not available in `populate_buy()` and `populate_sell()` methods.
+
 ### Possible options for DataProvider
 
-- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
+- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
-- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
+- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. VolumePairlist)
 - [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
 - [`get_analyzed_dataframe(pair, timeframe)`](#get_analyzed_dataframepair-timeframe) - Returns the analyzed dataframe (after calling `populate_indicators()`, `populate_buy()`, `populate_sell()`) and the time of the latest analysis.
 - `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
@@ -401,58 +405,13 @@ Since we can't resample our data we will have to use an informative pair; and si
 This is where calling `self.dp.current_whitelist()` comes in handy.
 
 ```python
-class SampleStrategy(IStrategy):
-    # strategy init stuff...
-
-    timeframe = '5m'
-
-    # more strategy init stuff..
-
     def informative_pairs(self):
 
         # get access to all pairs available in whitelist.
         pairs = self.dp.current_whitelist()
         # Assign tf to each pair so they can be downloaded and cached for strategy.
         informative_pairs = [(pair, '1d') for pair in pairs]
-        return informative_pairs
+        return informative_pairs   
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        inf_tf = '1d'
-        # Get the informative pair
-        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
-        # Get the 14 day rsi
-        informative['rsi'] = ta.RSI(informative, timeperiod=14)
-
-        # Rename columns to be unique
-        informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
-        # Assuming inf_tf = '1d' - then the columns will now be:
-        # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
-
-        # Combine the 2 dataframes
-        # all indicators on the informative sample MUST be calculated before this point
-        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
-        # FFill to have the 1d value available in every row throughout the day.
-        # Without this, comparisons would only work once per day.
-        dataframe = dataframe.ffill()
-        # Calculate rsi of the original dataframe (5m timeframe)
-        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
-
-        # Do other stuff
-        # ...
-
-        return dataframe
-
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        dataframe.loc[
-            (
-                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
-                (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
-                (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
-            ),
-            'buy'] = 1
-
 ```
 
 ### *get_pair_dataframe(pair, timeframe)*
@@ -479,7 +438,7 @@ It can also be used in specific callbacks to get the signal that caused the acti
 # fetch current dataframe
 if self.dp:
     dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
-                                                             timeframe=self.ticker_interval)
+                                                             timeframe=self.timeframe)
 ```
 
 !!! Note "No data available"
@@ -516,6 +475,74 @@ if self.dp:
     does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
     data returned from the exchange and add appropriate error handling / defaults.
 
+!!! Warning "Warning about backtesting"
+    This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
+
+### Complete Data-provider sample
+
+```python
+class SampleStrategy(IStrategy):
+    # strategy init stuff...
+
+    timeframe = '5m'
+
+    # more strategy init stuff..
+
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        # Optionally Add additional "static" pairs
+        informative_pairs += [("ETH/USDT", "5m"),
+                              ("BTC/TUSD", "15m"),
+                            ]
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if not self.dp:
+            # Don't do anything if DataProvider is not available.
+            return dataframe
+
+        inf_tf = '1d'
+        # Get the informative pair
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        # Get the 14 day rsi
+        informative['rsi'] = ta.RSI(informative, timeperiod=14)
+
+        # Rename columns to be unique
+        informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
+        # Assuming inf_tf = '1d' - then the columns will now be:
+        # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
+        # Combine the 2 dataframes
+        # all indicators on the informative sample MUST be calculated before this point
+        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
+        # FFill to have the 1d value available in every row throughout the day.
+        # Without this, comparisons would only work once per day.
+        dataframe = dataframe.ffill()
+
+        # Calculate rsi of the original dataframe (5m timeframe)
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        # Do other stuff
+        # ...
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
+                (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
+            ),
+            'buy'] = 1
+
+```
+
 ***
 
 ## Additional data (Wallets)