Merge pull request #4454 from freqtrade/backtest_compound_speed

Backtest compound, wallet, ...
2021-03-10 10:07:40 +01:00 · 2021-03-10 10:07:40 +01:00 · 0db5c9746f
commit 0db5c9746f
parent 60f6b998d3 4b550dab17
38 changed files with 715 additions and 315 deletions
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@ -16,6 +16,7 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                             [--max-open-trades INT]
                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                             [--eps] [--dmmp] [--enable-protections]
                             [--dry-run-wallet DRY_RUN_WALLET]
                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                             [--export EXPORT] [--export-filename PATH]
@ -48,6 +49,9 @@ optional arguments:
                        Enable protections for backtesting.Will slow
                        backtesting down by a considerable amount, but will
                        include configured protections
  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                        Starting balance, used for backtesting / hyperopt and
                        dry-runs.
  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
                        Provide a space-separated list of strategies to
                        backtest. Please note that ticker-interval needs to be
@ -91,8 +95,7 @@ Strategy arguments:
 ## Test your strategy with Backtesting
 Now you have good Buy and Sell strategies and some historic data, you want to test it against
-real data. This is what we call
+real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
 If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
@ -100,6 +103,8 @@ For details on downloading, please refer to the [Data Downloading](data-download
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
 !!! Warning "Using dynamic pairlists for backtesting"
    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
    Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
@ -107,38 +112,56 @@ The result of backtesting will confirm if your bot has better odds of making a p
    To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
-### Run a backtesting against the currencies listed in your config file
+### Starting balance
-#### With 5 min candle (OHLCV) data (per default)
+Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
 This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
 ### Dynamic stake amount
 Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
 Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
 ### Example backtesting commands
 With 5 min candle (OHLCV) data (per default)
 ```bash
-freqtrade backtesting
+freqtrade backtesting --strategy AwesomeStrategy
 ```
-#### With 1 min candle (OHLCV) data
+Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
 ---
 With 1 min candle (OHLCV) data
 ```bash
-freqtrade backtesting --timeframe 1m
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
 ```
-#### Using a different on-disk historical candle (OHLCV) data source
+---
 Providing a custom starting balance of 1000 (in stake currency)
 ```bash
 freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
 ```
 ---
 Using a different on-disk historical candle (OHLCV) data source
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
 ```bash
-freqtrade --datadir user_data/data/bittrex-20180101 backtesting
+freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/bittrex-20180101 
 ```
-#### With a (custom) strategy file
+---
-```bash
+Comparing multiple Strategies
 freqtrade backtesting -s SampleStrategy
 ```
 Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
 #### Comparing multiple Strategies
 ```bash
 freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
@ -146,23 +169,29 @@ freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timefram
 Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
-#### Exporting trades to file
+---
 Exporting trades to file
 ```bash
-freqtrade backtesting --export trades --config config.json --strategy SampleStrategy
+freqtrade backtesting --strategy backtesting --export trades --config config.json 
 ```
 The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
-#### Exporting trades to file specifying a custom filename
+---
 Exporting trades to file specifying a custom filename
 ```bash
-freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
+freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
-#### Supplying custom fee value
+---
 Supplying custom fee value
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
 To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
@ -177,26 +206,26 @@ freqtrade backtesting --fee 0.001
 !!! Note
    Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
-#### Running backtest with smaller testset by using timerange
+---
-Use the `--timerange` argument to change how much of the testset you want to use.
+Running backtest with smaller test-set by using timerange
 Use the `--timerange` argument to change how much of the test-set you want to use.
-For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
+For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
 ```bash
 freqtrade backtesting --timerange=20190501-
 ```
-You can also specify particular dates or a range span indexed by start and stop.
+You can also specify particular date ranges.
 The full timerange specification:
- Use tickframes till 2018/01/31: `--timerange=-20180131`
+- Use data until 2018/01/31: `--timerange=-20180131`
- Use tickframes since 2018/01/31: `--timerange=20180131-`
+- Use data since 2018/01/31: `--timerange=20180131-`
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
+- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use tickframes between POSIX timestamps 1527595200 1527618600:
+- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
                                                `--timerange=1527595200-1527618600`
 ## Understand the backtesting result
@ -248,19 +277,30 @@ A backtesting result will look like that:
 | Max open trades       | 3                   |
 |                       |                     |
 | Total trades          | 429                 |
-| Total Profit %        | 152.41%             |
+| Starting balance      | 0.01000000 BTC      |
 | Final balance         | 0.01762792 BTC      |
 | Absolute profit       | 0.00762792 BTC      |
 | Total profit %        | 76.2%               |
 | Trades per day        | 3.575               |
 | Avg. stake amount     | 0.001      BTC      |
 | Total trade volume    | 0.429      BTC      |
 |                       |                     |
 | Best Pair             | LSK/BTC 26.26%      |
 | Worst Pair            | ZEC/BTC -10.18%     |
 | Best Trade            | LSK/BTC 4.25%       |
 | Worst Trade           | ZEC/BTC -10.25%     |
-| Best day              | 25.27%              |
+| Best day              | 0.00076 BTC         |
-| Worst day             | -30.67%             |
+| Worst day             | -0.00036 BTC        |
 | Days win/draw/lose    | 12 / 82 / 25        |
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
 |                       |                     |
-| Max Drawdown          | 50.63%              |
+| Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
 | Drawdown              | 50.63%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
 | Drawdown Start        | 2019-02-15 14:10:00 |
 | Drawdown End          | 2019-04-11 18:15:00 |
 | Market change         | -5.88%              |
@ -281,9 +321,9 @@ here:
 The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
 earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
-The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
+The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
-The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
+The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
-In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config  so `tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
+In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
 Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
@ -324,19 +364,30 @@ It contains some useful key metrics about performance of your strategy on backte
 | Max open trades       | 3                   |
 |                       |                     |
 | Total trades          | 429                 |
-| Total Profit %        | 152.41%             |
+| Starting balance      | 0.01000000 BTC      |
 | Final balance         | 0.01762792 BTC      |
 | Absolute profit       | 0.00762792 BTC      |
 | Total profit %        | 76.2%               |
 | Trades per day        | 3.575               |
 | Avg. stake amount     | 0.001      BTC      |
 | Total trade volume    | 0.429      BTC      |
 |                       |                     |
 | Best Pair             | LSK/BTC 26.26%      |
 | Worst Pair            | ZEC/BTC -10.18%     |
 | Best Trade            | LSK/BTC 4.25%       |
 | Worst Trade           | ZEC/BTC -10.25%     |
-| Best day              | 25.27%              |
+| Best day              | 0.00076 BTC         |
-| Worst day             | -30.67%             |
+| Worst day             | -0.00036 BTC        |
 | Days win/draw/lose    | 12 / 82 / 25        |
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
 |                       |                     |
-| Max Drawdown          | 50.63%              |
+| Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
 | Drawdown              | 50.63%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
 | Drawdown Start        | 2019-02-15 14:10:00 |
 | Drawdown End          | 2019-04-11 18:15:00 |
 | Market change         | -5.88%              |
@ -347,13 +398,21 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
 - `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
 - `Total trades`: Identical to the total trades of the backtest output table.
- `Total Profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table.
+- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
 - `Final balance`: Final balance - starting balance + absolute profit.
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
 - `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.
 - `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
+- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
 - `Best day` / `Worst day`: Best and worst day based on daily profit.
 - `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
 - `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
 - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
 - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
 - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
@ -418,6 +477,5 @@ Detailed output for all strategies one after the other will be available, so mak
 ## Next step
-Great, your strategy is profitable. What if the bot can give your the
+Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
 optimal parameters to use for your strategy?
 Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)
--- a/docs/bot-usage.md
+++ b/docs/bot-usage.md
@ -56,6 +56,7 @@ optional arguments:
 usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                       [--userdir PATH] [-s NAME] [--strategy-path PATH]
                       [--db-url PATH] [--sd-notify] [--dry-run]
                       [--dry-run-wallet DRY_RUN_WALLET]
 optional arguments:
  -h, --help            show this help message and exit
@ -66,6 +67,9 @@ optional arguments:
  --sd-notify           Notify systemd service manager.
  --dry-run             Enforce dry-run for trading (removes Exchange secrets
                        and simulates trades).
  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                        Starting balance, used for backtesting / hyperopt and
                        dry-runs.
 Common arguments:
  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
--- a/docs/configuration.md
+++ b/docs/configuration.md
@ -49,7 +49,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `timeframe` | The timeframe (former ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
 | `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
 | `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
+| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
 | `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
 | `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
 | `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
@ -219,11 +219,12 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
 "tradable_balance_ratio": 0.99,
 ```
-!!! Note
+!!! Tip "Compounding profits"
-    This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
+    This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available), and will result in profit compounding.
 !!! Note "When using Dry-Run Mode"
-    When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
+    When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time.  
    It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
 --8<-- "includes/pricing.md"
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@ -43,7 +43,8 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                          [--max-open-trades INT]
                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
-                          [--dmmp] [--enable-protections] [-e INT]
+                          [--dmmp] [--enable-protections]
                          [--dry-run-wallet DRY_RUN_WALLET] [-e INT]
                          [--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
                          [--print-all] [--no-color] [--print-json] [-j JOBS]
                          [--random-state INT] [--min-trades INT]
@ -82,6 +83,9 @@ optional arguments:
                        Enable protections for backtesting.Will slow
                        backtesting down by a considerable amount, but will
                        include configured protections
  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
                        Starting balance, used for backtesting / hyperopt and
                        dry-runs.
  -e INT, --epochs INT  Specify number of epochs (default: 100).
  --spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]
                        Specify which parameters to hyperopt. Space-separated
--- a/docs/strategy-customization.md
+++ b/docs/strategy-customization.md
@ -678,7 +678,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
    Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
 !!! Warning
-    Locking pairs is not available during backtesting.
+    Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
 #### Pair locking example
--- a/freqtrade/commands/arguments.py
+++ b/freqtrade/commands/arguments.py
@ -14,18 +14,18 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
 ARGS_STRATEGY = ["strategy", "strategy_path"]
-ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
+ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", ]
 ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
                        "max_open_trades", "stake_amount", "fee"]
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
-                                        "enable_protections",
+                                        "enable_protections", "dry_run_wallet",
                                        "strategy_list", "export", "exportfilename"]
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                        "position_stacking", "use_max_market_positions",
-                                        "enable_protections",
+                                        "enable_protections", "dry_run_wallet",
                                        "epochs", "spaces", "print_all",
                                        "print_colorized", "print_json", "hyperopt_jobs",
                                        "hyperopt_random_state", "hyperopt_min_trades",
--- a/freqtrade/commands/cli_options.py
+++ b/freqtrade/commands/cli_options.py
@ -110,6 +110,11 @@ AVAILABLE_CLI_OPTIONS = {
        help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
        action='store_true',
    ),
    "dry_run_wallet": Arg(
        '--dry-run-wallet', '--starting-balance',
        help='Starting balance, used for backtesting / hyperopt and dry-runs.',
        type=float,
    ),
    # Optimize common
    "timeframe": Arg(
        '-i', '--timeframe', '--ticker-interval',
@ -128,7 +133,6 @@ AVAILABLE_CLI_OPTIONS = {
    "stake_amount": Arg(
        '--stake-amount',
        help='Override the value of the `stake_amount` configuration setting.',
        type=float,
    ),
    # Backtesting
    "position_stacking": Arg(
--- a/freqtrade/commands/optimize_commands.py
+++ b/freqtrade/commands/optimize_commands.py
@ -3,7 +3,8 @@ from typing import Any, Dict
 from freqtrade import constants
 from freqtrade.configuration import setup_utils_configuration
-from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exceptions import OperationalException
 from freqtrade.misc import round_coin_value
 from freqtrade.state import RunMode
@ -22,11 +23,13 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
        RunMode.BACKTEST: 'backtesting',
        RunMode.HYPEROPT: 'hyperoptimization',
    }
-    if (method in no_unlimited_runmodes.keys() and
+    if method in no_unlimited_runmodes.keys():
-            config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
+        if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
-        raise DependencyException(
+                and config['stake_amount'] > config['dry_run_wallet']):
-            f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
+            wallet = round_coin_value(config['dry_run_wallet'], config['stake_currency'])
-            f'for {no_unlimited_runmodes[method]}')
+            stake = round_coin_value(config['stake_amount'], config['stake_currency'])
            raise OperationalException(f"Starting balance ({wallet}) "
                                       f"is smaller than stake_amount {stake}.")
    return config
--- a/freqtrade/configuration/configuration.py
+++ b/freqtrade/configuration/configuration.py
@ -214,9 +214,6 @@ class Configuration:
        self._args_to_config(
            config, argname='enable_protections',
            logstring='Parameter --enable-protections detected, enabling Protections. ...')
        # Setting max_open_trades to infinite if -1
        if config.get('max_open_trades') == -1:
            config['max_open_trades'] = float('inf')
        if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
            config.update({'use_max_market_positions': False})
@ -228,11 +225,23 @@ class Configuration:
                        'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
        elif config['runmode'] in NON_UTIL_MODES:
            logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
        # Setting max_open_trades to infinite if -1
        if config.get('max_open_trades') == -1:
            config['max_open_trades'] = float('inf')
        if self.args.get('stake_amount', None):
            # Convert explicitly to float to support CLI argument for both unlimited and value
            try:
                self.args['stake_amount'] = float(self.args['stake_amount'])
            except ValueError:
                pass
        self._args_to_config(config, argname='stake_amount',
                             logstring='Parameter --stake-amount detected, '
                             'overriding stake_amount to: {} ...')
-
+        self._args_to_config(config, argname='dry_run_wallet',
                             logstring='Parameter --dry-run-wallet detected, '
                             'overriding dry_run_wallet to: {} ...')
        self._args_to_config(config, argname='fee',
                             logstring='Parameter --fee detected, '
                             'setting fee to: {} ...')
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@ -10,7 +10,7 @@ import pandas as pd
 from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.misc import json_load
-from freqtrade.persistence import Trade, init_db
+from freqtrade.persistence import LocalTrade, Trade, init_db
 logger = logging.getLogger(__name__)
@ -224,7 +224,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
    return df_final[df_final['open_trades'] > max_open_trades]
-def trade_list_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
+def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
    """
    Convert list of Trade objects to pandas Dataframe
    :param trades: List of trade objects
@ -360,13 +360,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
 def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
                           value_col: str = 'profit_ratio'
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
    """
    Calculate max drawdown and the corresponding close dates
    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
    :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
-    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
             high and low time and high and low value.
    :raise: ValueError if trade-dataframe was found empty.
    """
    if len(trades) == 0:
@ -382,13 +383,17 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
        raise ValueError("No losing trade, therefore no drawdown.")
    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
    low_date = profit_results.loc[idxmin, date_col]
-    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
+    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
                                   ['high_value'].idxmax(), 'cumulative']
    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
-def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:
+def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
    """
    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
    :param starting_balance: Add starting balance to results, to show the wallets high / low points
    :return: Tuple (float, float) with cumsum of profit_abs
    :raise: ValueError if trade-dataframe was found empty.
    """
@ -397,7 +402,7 @@ def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:
    csum_df = pd.DataFrame()
    csum_df['sum'] = trades['profit_abs'].cumsum()
-    csum_min = csum_df['sum'].min()
+    csum_min = csum_df['sum'].min() + starting_balance
-    csum_max = csum_df['sum'].max()
+    csum_max = csum_df['sum'].max() + starting_balance
    return csum_min, csum_max
--- a/freqtrade/exchange/exchange.py
+++ b/freqtrade/exchange/exchange.py
@ -147,6 +147,9 @@ class Exchange:
        """
        Destructor - clean up async stuff
        """
        self.close()
    def close(self):
        logger.debug("Exchange object destroyed, closing async loop")
        if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
            asyncio.get_event_loop().run_until_complete(self._api_async.close())
--- a/freqtrade/freqtradebot.py
+++ b/freqtrade/freqtradebot.py
@ -937,7 +937,7 @@ class FreqtradeBot(LoggingMixin):
        Check and execute sell
        """
        should_sell = self.strategy.should_sell(
-            trade, sell_rate, datetime.utcnow(), buy, sell,
+            trade, sell_rate, datetime.now(timezone.utc), buy, sell,
            force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
        )
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@ -17,17 +17,18 @@ from freqtrade.data import history
 from freqtrade.data.btanalysis import trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.exceptions import OperationalException
+from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                 store_backtest_stats)
-from freqtrade.persistence import PairLocks, Trade
+from freqtrade.persistence import LocalTrade, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 logger = logging.getLogger(__name__)
@ -114,6 +115,8 @@ class Backtesting:
        if self.config.get('enable_protections', False):
            self.protections = ProtectionManager(self.config)
        self.wallets = Wallets(self.config, self.exchange, log=False)
        # Get maximum required startup period
        self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
        # Load one (first) strategy
@ -124,7 +127,7 @@ class Backtesting:
        PairLocks.use_db = True
        Trade.use_db = True
-    def _set_strategy(self, strategy):
+    def _set_strategy(self, strategy: IStrategy):
        """
        Load strategy into backtesting
        """
@ -171,8 +174,6 @@ class Backtesting:
        PairLocks.use_db = False
        PairLocks.timeframe = self.config['timeframe']
        Trade.use_db = False
        if enable_protections:
            # Reset persisted data - used for protections only
        PairLocks.reset_locks()
        Trade.reset_trades()
@ -203,10 +204,10 @@ class Backtesting:
            # Convert from Pandas to list for performance reasons
            # (Looping Pandas is slow.)
-            data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
+            data[pair] = df_analyzed.values.tolist()
        return data
-    def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
+    def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
                        trade_dur: int) -> float:
        """
        Get close rate for backtesting result
@ -246,24 +247,48 @@ class Backtesting:
        else:
            return sell_row[OPEN_IDX]
-    def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[Trade]:
+    def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
-        sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
+        sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
-                                         sell_row[BUY_IDX], sell_row[SELL_IDX],
+                                         sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
                                         low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
        if sell.sell_flag:
            trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
            closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
            trade.close_date = sell_row[DATE_IDX]
-            trade.sell_reason = sell.sell_type
+            trade.sell_reason = sell.sell_type.value
            trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
            closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
            trade.close(closerate, show_msg=False)
            return trade
        return None
-    def handle_left_open(self, open_trades: Dict[str, List[Trade]],
+    def _enter_trade(self, pair: str, row: List, max_open_trades: int,
-                         data: Dict[str, List[Tuple]]) -> List[Trade]:
+                     open_trade_count: int) -> Optional[LocalTrade]:
        try:
            stake_amount = self.wallets.get_trade_stake_amount(
                pair, max_open_trades - open_trade_count, None)
        except DependencyException:
            return None
        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05)
        if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
            # Enter trade
            trade = LocalTrade(
                pair=pair,
                open_rate=row[OPEN_IDX],
                open_date=row[DATE_IDX],
                stake_amount=stake_amount,
                amount=round(stake_amount / row[OPEN_IDX], 8),
                fee_open=self.fee,
                fee_close=self.fee,
                is_open=True,
                exchange='backtesting',
            )
            return trade
        return None
    def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
                         data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
        """
        Handling of left open trades at the end of backtesting
        """
@ -274,13 +299,15 @@ class Backtesting:
                    sell_row = data[pair][-1]
                    trade.close_date = sell_row[DATE_IDX]
-                    trade.sell_reason = SellType.FORCE_SELL
+                    trade.sell_reason = SellType.FORCE_SELL.value
                    trade.close(sell_row[OPEN_IDX], show_msg=False)
-                    trade.is_open = True
+                    # Deepcopy object to have wallets update correctly
-                    trades.append(trade)
+                    trade1 = deepcopy(trade)
                    trade1.is_open = True
                    trades.append(trade1)
        return trades
-    def backtest(self, processed: Dict, stake_amount: float,
+    def backtest(self, processed: Dict,
                 start_date: datetime, end_date: datetime,
                 max_open_trades: int = 0, position_stacking: bool = False,
                 enable_protections: bool = False) -> DataFrame:
@ -292,7 +319,6 @@ class Backtesting:
        Avoid extensive logging in this method and functions it calls.
        :param processed: a processed dictionary with format {pair, data}
        :param stake_amount: amount to use for each trade
        :param start_date: backtesting timerange start datetime
        :param end_date: backtesting timerange end datetime
        :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@ -300,11 +326,7 @@ class Backtesting:
        :param enable_protections: Should protections be enabled?
        :return: DataFrame with trades (results of backtesting)
        """
-        logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
+        trades: List[LocalTrade] = []
                     f"start_date: {start_date}, end_date: {end_date}, "
                     f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
                     )
        trades: List[Trade] = []
        self.prepare_backtest(enable_protections)
        # Use dict of lists with data for performance
@ -315,7 +337,7 @@ class Backtesting:
        indexes: Dict = {}
        tmp = start_date + timedelta(minutes=self.timeframe_min)
-        open_trades: Dict[str, List] = defaultdict(list)
+        open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
        open_trade_count = 0
        # Loop timerange and get candle for each pair at that point in time
@ -346,28 +368,18 @@ class Backtesting:
                        and tmp != end_date
                        and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
                        and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
-                    # Enter trade
+                    trade = self._enter_trade(pair, row, max_open_trades, open_trade_count_start)
-                    trade = Trade(
+                    if trade:
                        pair=pair,
                        open_rate=row[OPEN_IDX],
                        open_date=row[DATE_IDX],
                        stake_amount=stake_amount,
                        amount=round(stake_amount / row[OPEN_IDX], 8),
                        fee_open=self.fee,
                        fee_close=self.fee,
                        is_open=True,
                    )
                        # TODO: hacky workaround to avoid opening > max_open_trades
                        # This emulates previous behaviour - not sure if this is correct
                        # Prevents buying if the trade-slot was freed in this candle
                        open_trade_count_start += 1
                        open_trade_count += 1
-                    # logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
+                        # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
                        open_trades[pair].append(trade)
-                    Trade.trades.append(trade)
+                        LocalTrade.trades.append(trade)
                for trade in open_trades[pair]:
                    # since indexes has been incremented before, we need to go one step back to
                    # also check the buying candle for sell conditions.
                    trade_entry = self._get_sell_trade_entry(trade, row)
                    # Sell occured
@ -384,6 +396,7 @@ class Backtesting:
            tmp += timedelta(minutes=self.timeframe_min)
        trades += self.handle_left_open(open_trades, data=data)
        self.wallets.update()
        return trade_list_to_dataframe(trades)
@ -417,7 +430,6 @@ class Backtesting:
        # Execute backtest and store results
        results = self.backtest(
            processed=preprocessed,
            stake_amount=self.config['stake_amount'],
            start_date=min_date.datetime,
            end_date=max_date.datetime,
            max_open_trades=max_open_trades,
@ -428,7 +440,8 @@ class Backtesting:
        self.all_results[self.strategy.get_strategy_name()] = {
            'results': results,
            'config': self.strategy.config,
-            'locks': PairLocks.locks,
+            'locks': PairLocks.get_all_locks(),
            'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
            'backtest_start_time': int(backtest_start_time.timestamp()),
            'backtest_end_time': int(backtest_end_time.timestamp()),
        }
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@ -541,7 +541,6 @@ class Hyperopt:
        backtesting_results = self.backtesting.backtest(
            processed=processed,
            stake_amount=self.config['stake_amount'],
            start_date=min_date.datetime,
            end_date=max_date.datetime,
            max_open_trades=self.max_open_trades,
@ -665,7 +664,10 @@ class Hyperopt:
        dump(preprocessed, self.data_pickle_file)
        # We don't need exchange instance anymore while running hyperopt
-        self.backtesting.exchange = None  # type: ignore
+        self.backtesting.exchange.close()
        self.backtesting.exchange._api = None  # type: ignore
        self.backtesting.exchange._api_async = None  # type: ignore
        # self.backtesting.exchange = None  # type: ignore
        self.backtesting.pairlists = None  # type: ignore
        self.backtesting.strategy.dp = None  # type: ignore
        IStrategy.dp = None  # type: ignore
--- a/freqtrade/optimize/optimize_reports.py
+++ b/freqtrade/optimize/optimize_reports.py
@ -8,7 +8,7 @@ from numpy import int64
 from pandas import DataFrame
 from tabulate import tabulate
-from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
+from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
                                       calculate_max_drawdown)
 from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
@ -56,12 +56,13 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
            'Wins', 'Draws', 'Losses']
-def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
+def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
    """
    Generate one result dict, with "first_column" as key.
    """
    profit_sum = result['profit_ratio'].sum()
-    profit_total = profit_sum / max_open_trades
+    # (end-capital - starting capital) / starting capital
    profit_total = result['profit_abs'].sum() / starting_balance
    return {
        'key': first_column,
@ -88,13 +89,13 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
    }
-def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
+def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
                          results: DataFrame, skip_nan: bool = False) -> List[Dict]:
    """
    Generates and returns a list  for the given backtest data and the results dataframe
    :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
    :param stake_currency: stake-currency - used to correctly name headers
-    :param max_open_trades: Maximum allowed open trades
+    :param starting_balance: Starting balance
    :param results: Dataframe containing the backtest results
    :param skip_nan: Print "left open" open trades
    :return: List of Dicts containing the metrics per pair
@ -107,10 +108,10 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_t
        if skip_nan and result['profit_abs'].isnull().all():
            continue
-        tabular_data.append(_generate_result_line(result, max_open_trades, pair))
+        tabular_data.append(_generate_result_line(result, starting_balance, pair))
    # Append Total
-    tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
+    tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
    return tabular_data
@ -132,7 +133,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
        tabular_data.append(
            {
-                'sell_reason': reason.value,
+                'sell_reason': reason,
                'trades': count,
                'wins': len(result[result['profit_abs'] > 0]),
                'draws': len(result[result['profit_abs'] == 0]),
@ -159,7 +160,7 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
    tabular_data = []
    for strategy, results in all_results.items():
        tabular_data.append(_generate_result_line(
-            results['results'], results['config']['max_open_trades'], strategy)
+            results['results'], results['config']['dry_run_wallet'], strategy)
            )
    return tabular_data
@ -195,13 +196,18 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
        return {
            'backtest_best_day': 0,
            'backtest_worst_day': 0,
            'backtest_best_day_abs': 0,
            'backtest_worst_day_abs': 0,
            'winning_days': 0,
            'draw_days': 0,
            'losing_days': 0,
            'winner_holding_avg': timedelta(),
            'loser_holding_avg': timedelta(),
        }
-    daily_profit = results.resample('1d', on='close_date')['profit_ratio'].sum()
+    daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
    daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
    worst_rel = min(daily_profit_rel)
    best_rel = max(daily_profit_rel)
    worst = min(daily_profit)
    best = max(daily_profit)
    winning_days = sum(daily_profit > 0)
@ -212,8 +218,10 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
    losing_trades = results.loc[results['profit_ratio'] < 0]
    return {
-        'backtest_best_day': best,
+        'backtest_best_day': best_rel,
-        'backtest_worst_day': worst,
+        'backtest_worst_day': worst_rel,
        'backtest_best_day_abs': best,
        'backtest_worst_day_abs': worst,
        'winning_days': winning_days,
        'draw_days': draw_days,
        'losing_days': losing_days,
@ -246,15 +254,16 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
            continue
        config = content['config']
        max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
        starting_balance = config['dry_run_wallet']
        stake_currency = config['stake_currency']
        pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
-                                             max_open_trades=max_open_trades,
+                                             starting_balance=starting_balance,
                                             results=results, skip_nan=False)
        sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
                                                       results=results)
        left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
-                                                  max_open_trades=max_open_trades,
+                                                  starting_balance=starting_balance,
                                                  results=results.loc[results['is_open']],
                                                  skip_nan=True)
        daily_stats = generate_daily_stats(results)
@ -275,8 +284,10 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
            'sell_reason_summary': sell_reason_stats,
            'left_open_trades': left_open_results,
            'total_trades': len(results),
            'total_volume': float(results['stake_amount'].sum()),
            'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
            'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
-            'profit_total': results['profit_ratio'].sum() / max_open_trades,
+            'profit_total': results['profit_abs'].sum() / starting_balance,
            'profit_total_abs': results['profit_abs'].sum(),
            'backtest_start': min_date.datetime,
            'backtest_start_ts': min_date.int_timestamp * 1000,
@ -292,6 +303,10 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
            'pairlist': list(btdata.keys()),
            'stake_amount': config['stake_amount'],
            'stake_currency': config['stake_currency'],
            'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
            'starting_balance': starting_balance,
            'dry_run_wallet': starting_balance,
            'final_balance': content['final_balance'],
            'max_open_trades': max_open_trades,
            'max_open_trades_setting': (config['max_open_trades']
                                        if config['max_open_trades'] != float('inf') else -1),
@ -316,17 +331,23 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
        result['strategy'][strategy] = strat_stats
        try:
-            max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
+            max_drawdown, _, _, _, _ = calculate_max_drawdown(
                results, value_col='profit_ratio')
            drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
                results, value_col='profit_abs')
            strat_stats.update({
                'max_drawdown': max_drawdown,
                'max_drawdown_abs': drawdown_abs,
                'drawdown_start': drawdown_start,
                'drawdown_start_ts': drawdown_start.timestamp() * 1000,
                'drawdown_end': drawdown_end,
                'drawdown_end_ts': drawdown_end.timestamp() * 1000,
                'max_drawdown_low': low_val,
                'max_drawdown_high': high_val,
            })
-            csum_min, csum_max = calculate_csum(results)
+            csum_min, csum_max = calculate_csum(results, starting_balance)
            strat_stats.update({
                'csum_min': csum_min,
                'csum_max': csum_max
@ -335,6 +356,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
        except ValueError:
            strat_stats.update({
                'max_drawdown': 0.0,
                'max_drawdown_abs': 0.0,
                'max_drawdown_low': 0.0,
                'max_drawdown_high': 0.0,
                'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
                'drawdown_start_ts': 0,
                'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
@ -431,8 +455,19 @@ def text_table_add_metrics(strat_results: Dict) -> str:
            ('Max open trades', strat_results['max_open_trades']),
            ('', ''),  # Empty line to improve readability
            ('Total trades', strat_results['total_trades']),
-            ('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
+            ('Starting balance', round_coin_value(strat_results['starting_balance'],
                                                  strat_results['stake_currency'])),
            ('Final balance', round_coin_value(strat_results['final_balance'],
                                               strat_results['stake_currency'])),
            ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
                                                  strat_results['stake_currency'])),
            ('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
            ('Trades per day', strat_results['trades_per_day']),
            ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
                                                   strat_results['stake_currency'])),
            ('Total trade volume', round_coin_value(strat_results['total_volume'],
                                                    strat_results['stake_currency'])),
            ('', ''),  # Empty line to improve readability
            ('Best Pair', f"{strat_results['best_pair']['key']} "
                          f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@ -442,20 +477,28 @@ def text_table_add_metrics(strat_results: Dict) -> str:
            ('Worst trade', f"{worst_trade['pair']} "
                            f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
-            ('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
+            ('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
-            ('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
+                                          strat_results['stake_currency'])),
            ('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
                                           strat_results['stake_currency'])),
            ('Days win/draw/lose', f"{strat_results['winning_days']} / "
                f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
            ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
            ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
            ('', ''),  # Empty line to improve readability
-            ('Abs Profit Min', round_coin_value(strat_results['csum_min'],
+            ('Min balance', round_coin_value(strat_results['csum_min'],
                                             strat_results['stake_currency'])),
-            ('Abs Profit Max', round_coin_value(strat_results['csum_max'],
+            ('Max balance', round_coin_value(strat_results['csum_max'],
                                             strat_results['stake_currency'])),
-            ('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
+            ('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
            ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
                                          strat_results['stake_currency'])),
            ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
                                               strat_results['stake_currency'])),
            ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
                                              strat_results['stake_currency'])),
            ('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
            ('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
            ('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
@ -463,7 +506,17 @@ def text_table_add_metrics(strat_results: Dict) -> str:
        return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
    else:
-        return ''
+        start_balance = round_coin_value(strat_results['starting_balance'],
                                         strat_results['stake_currency'])
        stake_amount = round_coin_value(
            strat_results['stake_amount'], strat_results['stake_currency']
            ) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
        message = ("No trades made. "
                   f"Your starting balance was {start_balance}, "
                   f"and your stake was {stake_amount}."
                   )
        return message
 def show_backtest_results(config: Dict, backtest_stats: Dict):
--- a/freqtrade/persistence/init.py
+++ b/freqtrade/persistence/init.py
@ -1,4 +1,5 @@
 # flake8: noqa: F401
-from freqtrade.persistence.models import Order, Trade, clean_dry_run_db, cleanup_db, init_db
+from freqtrade.persistence.models import (LocalTrade, Order, Trade, clean_dry_run_db, cleanup_db,
                                          init_db)
 from freqtrade.persistence.pairlock_middleware import PairLocks
--- a/freqtrade/persistence/migrations.py
+++ b/freqtrade/persistence/migrations.py
@ -141,7 +141,7 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
        inspector = inspect(engine)
        cols = inspector.get_columns('trades')
-    if 'orders' not in previous_tables:
+    if 'orders' not in previous_tables and 'trades' in previous_tables:
        logger.info('Moving open orders to Orders table.')
        migrate_open_orders_to_trades(engine)
    else:
--- a/freqtrade/persistence/models.py
+++ b/freqtrade/persistence/models.py
@ -199,67 +199,67 @@ class Order(_DECL_BASE):
        return Order.query.filter(Order.ft_is_open.is_(True)).all()
-class Trade(_DECL_BASE):
+class LocalTrade():
    """
    Trade database model.
-    Also handles updating and querying trades
+    Used in backtesting - must be aligned to Trade model!
    """
-    __tablename__ = 'trades'
+    use_db: bool = False
    use_db: bool = True
    # Trades container for backtesting
-    trades: List['Trade'] = []
+    trades: List['LocalTrade'] = []
-    id = Column(Integer, primary_key=True)
+    id: int = 0
-    orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
+    orders: List[Order] = []
-    exchange = Column(String, nullable=False)
+    exchange: str = ''
-    pair = Column(String, nullable=False, index=True)
+    pair: str = ''
-    is_open = Column(Boolean, nullable=False, default=True, index=True)
+    is_open: bool = True
-    fee_open = Column(Float, nullable=False, default=0.0)
+    fee_open: float = 0.0
-    fee_open_cost = Column(Float, nullable=True)
+    fee_open_cost: Optional[float] = None
-    fee_open_currency = Column(String, nullable=True)
+    fee_open_currency: str = ''
-    fee_close = Column(Float, nullable=False, default=0.0)
+    fee_close: float = 0.0
-    fee_close_cost = Column(Float, nullable=True)
+    fee_close_cost: Optional[float] = None
-    fee_close_currency = Column(String, nullable=True)
+    fee_close_currency: str = ''
-    open_rate = Column(Float)
+    open_rate: float = 0.0
-    open_rate_requested = Column(Float)
+    open_rate_requested: Optional[float] = None
    # open_trade_value - calculated via _calc_open_trade_value
-    open_trade_value = Column(Float)
+    open_trade_value: float = 0.0
-    close_rate = Column(Float)
+    close_rate: Optional[float] = None
-    close_rate_requested = Column(Float)
+    close_rate_requested: Optional[float] = None
-    close_profit = Column(Float)
+    close_profit: Optional[float] = None
-    close_profit_abs = Column(Float)
+    close_profit_abs: Optional[float] = None
-    stake_amount = Column(Float, nullable=False)
+    stake_amount: float = 0.0
-    amount = Column(Float)
+    amount: float = 0.0
-    amount_requested = Column(Float)
+    amount_requested: Optional[float] = None
-    open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
+    open_date: datetime
-    close_date = Column(DateTime)
+    close_date: Optional[datetime] = None
-    open_order_id = Column(String)
+    open_order_id: Optional[str] = None
    # absolute value of the stop loss
-    stop_loss = Column(Float, nullable=True, default=0.0)
+    stop_loss: float = 0.0
    # percentage value of the stop loss
-    stop_loss_pct = Column(Float, nullable=True)
+    stop_loss_pct: float = 0.0
    # absolute value of the initial stop loss
-    initial_stop_loss = Column(Float, nullable=True, default=0.0)
+    initial_stop_loss: float = 0.0
    # percentage value of the initial stop loss
-    initial_stop_loss_pct = Column(Float, nullable=True)
+    initial_stop_loss_pct: float = 0.0
    # stoploss order id which is on exchange
-    stoploss_order_id = Column(String, nullable=True, index=True)
+    stoploss_order_id: Optional[str] = None
    # last update time of the stoploss order on exchange
-    stoploss_last_update = Column(DateTime, nullable=True)
+    stoploss_last_update: Optional[datetime] = None
    # absolute value of the highest reached price
-    max_rate = Column(Float, nullable=True, default=0.0)
+    max_rate: float = 0.0
    # Lowest price reached
-    min_rate = Column(Float, nullable=True)
+    min_rate: float = 0.0
-    sell_reason = Column(String, nullable=True)
+    sell_reason: str = ''
-    sell_order_status = Column(String, nullable=True)
+    sell_order_status: str = ''
-    strategy = Column(String, nullable=True)
+    strategy: str = ''
-    timeframe = Column(Integer, nullable=True)
+    timeframe: Optional[int] = None
    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+        for key in kwargs:
            setattr(self, key, kwargs[key])
        self.recalc_open_trade_value()
    def __repr__(self):
@ -268,6 +268,14 @@ class Trade(_DECL_BASE):
        return (f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
                f'open_rate={self.open_rate:.8f}, open_since={open_since})')
    @property
    def open_date_utc(self):
        return self.open_date.replace(tzinfo=timezone.utc)
    @property
    def close_date_utc(self):
        return self.close_date.replace(tzinfo=timezone.utc)
    def to_json(self) -> Dict[str, Any]:
        return {
            'trade_id': self.id,
@ -306,9 +314,9 @@ class Trade(_DECL_BASE):
            'close_profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
            'close_profit_abs': self.close_profit_abs,  # Deprecated
-            'trade_duration_s': (int((self.close_date - self.open_date).total_seconds())
+            'trade_duration_s': (int((self.close_date_utc - self.open_date_utc).total_seconds())
                                 if self.close_date else None),
-            'trade_duration': (int((self.close_date - self.open_date).total_seconds() // 60)
+            'trade_duration': (int((self.close_date_utc - self.open_date_utc).total_seconds() // 60)
                               if self.close_date else None),
            'profit_ratio': self.close_profit,
@ -341,8 +349,7 @@ class Trade(_DECL_BASE):
        """
        Resets all trades. Only active for backtesting mode.
        """
-        if not Trade.use_db:
+        LocalTrade.trades = []
            Trade.trades = []
    def adjust_min_max_rates(self, current_price: float) -> None:
        """
@ -410,8 +417,8 @@ class Trade(_DECL_BASE):
        if order_type in ('market', 'limit') and order['side'] == 'buy':
            # Update open rate and actual amount
-            self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
+            self.open_rate = float(safe_value_fallback(order, 'average', 'price'))
-            self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
+            self.amount = float(safe_value_fallback(order, 'filled', 'amount'))
            self.recalc_open_trade_value()
            if self.is_open:
                logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
@ -435,7 +442,7 @@ class Trade(_DECL_BASE):
        Sets close_rate to the given rate, calculates total profit
        and marks trade as closed
        """
-        self.close_rate = Decimal(rate)
+        self.close_rate = rate
        self.close_profit = self.calc_profit_ratio()
        self.close_profit_abs = self.calc_profit()
        self.close_date = self.close_date or datetime.utcnow()
@ -480,14 +487,6 @@ class Trade(_DECL_BASE):
    def update_order(self, order: Dict) -> None:
        Order.update_orders(self.orders, order)
    def delete(self) -> None:
        for order in self.orders:
            Order.session.delete(order)
        Trade.session.delete(self)
        Trade.session.flush()
    def _calc_open_trade_value(self) -> float:
        """
        Calculate the open_rate including open_fee.
@ -517,7 +516,7 @@ class Trade(_DECL_BASE):
        if rate is None and not self.close_rate:
            return 0.0
-        sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)
+        sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)  # type: ignore
        fees = sell_trade * Decimal(fee or self.fee_close)
        return float(sell_trade - fees)
@ -589,7 +588,7 @@ class Trade(_DECL_BASE):
    @staticmethod
    def get_trades_proxy(*, pair: str = None, is_open: bool = None,
                         open_date: datetime = None, close_date: datetime = None,
-                         ) -> List['Trade']:
+                         ) -> List['LocalTrade']:
        """
        Helper function to query Trades.
        Returns a List of trades, filtered on the parameters given.
@ -598,20 +597,9 @@ class Trade(_DECL_BASE):
        :return: unsorted List[Trade]
        """
-        if Trade.use_db:
+
            trade_filter = []
            if pair:
                trade_filter.append(Trade.pair == pair)
            if open_date:
                trade_filter.append(Trade.open_date > open_date)
            if close_date:
                trade_filter.append(Trade.close_date > close_date)
            if is_open is not None:
                trade_filter.append(Trade.is_open.is_(is_open))
            return Trade.get_trades(trade_filter).all()
        else:
        # Offline mode - without database
-            sel_trades = [trade for trade in Trade.trades]
+        sel_trades = [trade for trade in LocalTrade.trades]
        if pair:
            sel_trades = [trade for trade in sel_trades if trade.pair == pair]
        if open_date:
@ -663,9 +651,12 @@ class Trade(_DECL_BASE):
        Calculates total invested amount in open trades
        in stake currency
        """
-        total_open_stake_amount = Trade.session.query(func.sum(Trade.stake_amount))\
+        if Trade.use_db:
-            .filter(Trade.is_open.is_(True))\
+            total_open_stake_amount = Trade.session.query(
-            .scalar()
+                func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
        else:
            total_open_stake_amount = sum(
                t.stake_amount for t in Trade.get_trades_proxy(is_open=True))
        return total_open_stake_amount or 0
    @staticmethod
@ -723,6 +714,108 @@ class Trade(_DECL_BASE):
                logger.info(f"New stoploss: {trade.stop_loss}.")
 class Trade(_DECL_BASE, LocalTrade):
    """
    Trade database model.
    Also handles updating and querying trades
    Note: Fields must be aligned with LocalTrade class
    """
    __tablename__ = 'trades'
    use_db: bool = True
    id = Column(Integer, primary_key=True)
    orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
    exchange = Column(String, nullable=False)
    pair = Column(String, nullable=False, index=True)
    is_open = Column(Boolean, nullable=False, default=True, index=True)
    fee_open = Column(Float, nullable=False, default=0.0)
    fee_open_cost = Column(Float, nullable=True)
    fee_open_currency = Column(String, nullable=True)
    fee_close = Column(Float, nullable=False, default=0.0)
    fee_close_cost = Column(Float, nullable=True)
    fee_close_currency = Column(String, nullable=True)
    open_rate = Column(Float)
    open_rate_requested = Column(Float)
    # open_trade_value - calculated via _calc_open_trade_value
    open_trade_value = Column(Float)
    close_rate = Column(Float)
    close_rate_requested = Column(Float)
    close_profit = Column(Float)
    close_profit_abs = Column(Float)
    stake_amount = Column(Float, nullable=False)
    amount = Column(Float)
    amount_requested = Column(Float)
    open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
    close_date = Column(DateTime)
    open_order_id = Column(String)
    # absolute value of the stop loss
    stop_loss = Column(Float, nullable=True, default=0.0)
    # percentage value of the stop loss
    stop_loss_pct = Column(Float, nullable=True)
    # absolute value of the initial stop loss
    initial_stop_loss = Column(Float, nullable=True, default=0.0)
    # percentage value of the initial stop loss
    initial_stop_loss_pct = Column(Float, nullable=True)
    # stoploss order id which is on exchange
    stoploss_order_id = Column(String, nullable=True, index=True)
    # last update time of the stoploss order on exchange
    stoploss_last_update = Column(DateTime, nullable=True)
    # absolute value of the highest reached price
    max_rate = Column(Float, nullable=True, default=0.0)
    # Lowest price reached
    min_rate = Column(Float, nullable=True)
    sell_reason = Column(String, nullable=True)
    sell_order_status = Column(String, nullable=True)
    strategy = Column(String, nullable=True)
    timeframe = Column(Integer, nullable=True)
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.recalc_open_trade_value()
    def delete(self) -> None:
        for order in self.orders:
            Order.session.delete(order)
        Trade.session.delete(self)
        Trade.session.flush()
    @staticmethod
    def get_trades_proxy(*, pair: str = None, is_open: bool = None,
                         open_date: datetime = None, close_date: datetime = None,
                         ) -> List['LocalTrade']:
        """
        Helper function to query Trades.
        Returns a List of trades, filtered on the parameters given.
        In live mode, converts the filter to a database query and returns all rows
        In Backtest mode, uses filters on Trade.trades to get the result.
        :return: unsorted List[Trade]
        """
        if Trade.use_db:
            trade_filter = []
            if pair:
                trade_filter.append(Trade.pair == pair)
            if open_date:
                trade_filter.append(Trade.open_date > open_date)
            if close_date:
                trade_filter.append(Trade.close_date > close_date)
            if is_open is not None:
                trade_filter.append(Trade.is_open.is_(is_open))
            return Trade.get_trades(trade_filter).all()
        else:
            return LocalTrade.get_trades_proxy(
                pair=pair, is_open=is_open,
                open_date=open_date,
                close_date=close_date
            )
 class PairLock(_DECL_BASE):
    """
    Pair Locks database model.
--- a/freqtrade/persistence/pairlock_middleware.py
+++ b/freqtrade/persistence/pairlock_middleware.py
@ -123,3 +123,11 @@ class PairLocks():
            now = datetime.now(timezone.utc)
        return len(PairLocks.get_pair_locks(pair, now)) > 0 or PairLocks.is_global_lock(now)
    @staticmethod
    def get_all_locks() -> List[PairLock]:
        if PairLocks.use_db:
            return PairLock.query.all()
        else:
            return PairLocks.locks
--- a/freqtrade/plot/plotting.py
+++ b/freqtrade/plot/plotting.py
@ -145,7 +145,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
    Add scatter points indicating max drawdown
    """
    try:
-        max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
+        max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
        drawdown = go.Scatter(
            x=[highdate, lowdate],
--- a/freqtrade/plugins/protections/cooldown_period.py
+++ b/freqtrade/plugins/protections/cooldown_period.py
@ -44,7 +44,8 @@ class CooldownPeriod(IProtection):
        trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
        if trades:
            # Get latest trade
-            trade = sorted(trades, key=lambda t: t.close_date)[-1]
+            # Ignore type error as we know we only get closed trades.
            trade = sorted(trades, key=lambda t: t.close_date)[-1]  # type: ignore
            self.log_once(f"Cooldown for {pair} for {self.stop_duration_str}.", logger.info)
            until = self.calculate_lock_end([trade], self._stop_duration)
--- a/freqtrade/plugins/protections/iprotection.py
+++ b/freqtrade/plugins/protections/iprotection.py
@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Tuple
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import plural
 from freqtrade.mixins import LoggingMixin
-from freqtrade.persistence import Trade
+from freqtrade.persistence import LocalTrade
 logger = logging.getLogger(__name__)
@ -93,11 +93,11 @@ class IProtection(LoggingMixin, ABC):
        """
    @staticmethod
-    def calculate_lock_end(trades: List[Trade], stop_minutes: int) -> datetime:
+    def calculate_lock_end(trades: List[LocalTrade], stop_minutes: int) -> datetime:
        """
        Get lock end time
        """
-        max_date: datetime = max([trade.close_date for trade in trades])
+        max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
        # comming from Database, tzinfo is not set.
        if max_date.tzinfo is None:
            max_date = max_date.replace(tzinfo=timezone.utc)
--- a/freqtrade/plugins/protections/low_profit_pairs.py
+++ b/freqtrade/plugins/protections/low_profit_pairs.py
@ -53,7 +53,7 @@ class LowProfitPairs(IProtection):
            # Not enough trades in the relevant period
            return False, None, None
-        profit = sum(trade.close_profit for trade in trades)
+        profit = sum(trade.close_profit for trade in trades if trade.close_profit)
        if profit < self._required_profit:
            self.log_once(
                f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "
--- a/freqtrade/plugins/protections/max_drawdown_protection.py
+++ b/freqtrade/plugins/protections/max_drawdown_protection.py
@ -55,7 +55,7 @@ class MaxDrawdown(IProtection):
        # Drawdown is always positive
        try:
-            drawdown, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
+            drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
        except ValueError:
            return False, None, None
--- a/freqtrade/plugins/protections/stoploss_guard.py
+++ b/freqtrade/plugins/protections/stoploss_guard.py
@ -56,7 +56,7 @@ class StoplossGuard(IProtection):
        trades = [trade for trade in trades1 if (str(trade.sell_reason) in (
                    SellType.TRAILING_STOP_LOSS.value, SellType.STOP_LOSS.value,
                    SellType.STOPLOSS_ON_EXCHANGE.value)
-                      and trade.close_profit < 0)]
+                      and trade.close_profit and trade.close_profit < 0)]
        if len(trades) < self._trade_limit:
            return False, None, None
--- a/freqtrade/strategy/interface.py
+++ b/freqtrade/strategy/interface.py
@ -649,7 +649,7 @@ class IStrategy(ABC):
        :return: True if bot should sell at current rate
        """
        # Check if time matches and current rate is above threshold
-        trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
+        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        _, roi = self.min_roi_reached_entry(trade_dur)
        if roi is None:
            return False
--- a/freqtrade/wallets.py
+++ b/freqtrade/wallets.py
@ -11,6 +11,7 @@ from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import DependencyException
 from freqtrade.exchange import Exchange
 from freqtrade.persistence import Trade
 from freqtrade.state import RunMode
 logger = logging.getLogger(__name__)
@ -26,8 +27,9 @@ class Wallet(NamedTuple):
 class Wallets:
-    def __init__(self, config: dict, exchange: Exchange) -> None:
+    def __init__(self, config: dict, exchange: Exchange, log: bool = True) -> None:
        self._config = config
        self._log = log
        self._exchange = exchange
        self._wallets: Dict[str, Wallet] = {}
        self.start_cap = config['dry_run_wallet']
@ -64,9 +66,9 @@ class Wallets:
        """
        # Recreate _wallets to reset closed trade balances
        _wallets = {}
-        closed_trades = Trade.get_trades(Trade.is_open.is_(False)).all()
+        closed_trades = Trade.get_trades_proxy(is_open=False)
-        open_trades = Trade.get_trades(Trade.is_open.is_(True)).all()
+        open_trades = Trade.get_trades_proxy(is_open=True)
-        tot_profit = sum([trade.calc_profit() for trade in closed_trades])
+        tot_profit = sum([trade.close_profit_abs for trade in closed_trades])
        tot_in_trades = sum([trade.stake_amount for trade in open_trades])
        current_stake = self.start_cap + tot_profit - tot_in_trades
@ -111,10 +113,11 @@ class Wallets:
        :param require_update: Allow skipping an update if balances were recently refreshed
        """
        if (require_update or (self._last_wallet_refresh + 3600 < arrow.utcnow().int_timestamp)):
-            if self._config['dry_run']:
+            if (not self._config['dry_run'] or self._config.get('runmode') == RunMode.LIVE):
                self._update_dry()
            else:
                self._update_live()
            else:
                self._update_dry()
            if self._log:
                logger.info('Wallets synced.')
            self._last_wallet_refresh = arrow.utcnow().int_timestamp
@ -154,6 +157,7 @@ class Wallets:
        Check if stake amount can be fulfilled with the available balance
        for the stake currency
        :return: float: Stake amount
        :raise: DependencyException if balance is lower than stake-amount
        """
        available_amount = self._get_available_stake_amount()
--- a/tests/conftest.py
+++ b/tests/conftest.py
@ -19,7 +19,7 @@ from freqtrade.data.converter import ohlcv_to_dataframe
 from freqtrade.edge import Edge, PairInfo
 from freqtrade.exchange import Exchange
 from freqtrade.freqtradebot import FreqtradeBot
-from freqtrade.persistence import Trade, init_db
+from freqtrade.persistence import LocalTrade, Trade, init_db
 from freqtrade.resolvers import ExchangeResolver
 from freqtrade.worker import Worker
 from tests.conftest_trades import (mock_trade_1, mock_trade_2, mock_trade_3, mock_trade_4,
@ -183,28 +183,34 @@ def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False)) -> None:
    freqtrade.exchange.refresh_latest_ohlcv = lambda p: None
-def create_mock_trades(fee):
+def create_mock_trades(fee, use_db: bool = True):
    """
    Create some fake trades ...
    """
    def add_trade(trade):
        if use_db:
            Trade.session.add(trade)
        else:
            LocalTrade.trades.append(trade)
    # Simulate dry_run entries
    trade = mock_trade_1(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
    trade = mock_trade_2(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
    trade = mock_trade_3(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
    trade = mock_trade_4(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
    trade = mock_trade_5(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
    trade = mock_trade_6(fee)
-    Trade.session.add(trade)
+    add_trade(trade)
@pytest.fixture(autouse=True)
@ -255,6 +261,7 @@ def get_default_conf(testdatadir):
            "20": 0.02,
            "0": 0.04
        },
        "dry_run_wallet": 1000,
        "stoploss": -0.10,
        "unfilledtimeout": {
            "buy": 10,
--- a/tests/conftest_trades.py
+++ b/tests/conftest_trades.py
@ -28,6 +28,7 @@ def mock_trade_1(fee):
        amount_requested=123.0,
        fee_open=fee.return_value,
        fee_close=fee.return_value,
        is_open=True,
        open_rate=0.123,
        exchange='bittrex',
        open_order_id='dry_run_buy_12345',
@ -81,6 +82,7 @@ def mock_trade_2(fee):
        open_rate=0.123,
        close_rate=0.128,
        close_profit=0.005,
        close_profit_abs=0.000584127,
        exchange='bittrex',
        is_open=False,
        open_order_id='dry_run_sell_12345',
@ -140,6 +142,7 @@ def mock_trade_3(fee):
        open_rate=0.05,
        close_rate=0.06,
        close_profit=0.01,
        close_profit_abs=0.000155,
        exchange='bittrex',
        is_open=False,
        strategy='DefaultStrategy',
@ -180,6 +183,7 @@ def mock_trade_4(fee):
        amount_requested=124.0,
        fee_open=fee.return_value,
        fee_close=fee.return_value,
        is_open=True,
        open_rate=0.123,
        exchange='bittrex',
        open_order_id='prod_buy_12345',
@ -230,6 +234,7 @@ def mock_trade_5(fee):
        amount_requested=124.0,
        fee_open=fee.return_value,
        fee_close=fee.return_value,
        is_open=True,
        open_rate=0.123,
        exchange='bittrex',
        strategy='SampleStrategy',
@ -281,6 +286,7 @@ def mock_trade_6(fee):
        amount_requested=2.0,
        fee_open=fee.return_value,
        fee_close=fee.return_value,
        is_open=True,
        open_rate=0.15,
        exchange='bittrex',
        strategy='SampleStrategy',
--- a/tests/data/test_btanalysis.py
+++ b/tests/data/test_btanalysis.py
@ -274,15 +274,17 @@ def test_create_cum_profit1(testdatadir):
 def test_calculate_max_drawdown(testdatadir):
    filename = testdatadir / "backtest-result_test.json"
    bt_data = load_backtest_data(filename)
-    drawdown, h, low = calculate_max_drawdown(bt_data)
+    drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(bt_data)
    assert isinstance(drawdown, float)
    assert pytest.approx(drawdown) == 0.21142322
-    assert isinstance(h, Timestamp)
+    assert isinstance(hdate, Timestamp)
-    assert isinstance(low, Timestamp)
+    assert isinstance(lowdate, Timestamp)
-    assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
+    assert isinstance(hval, float)
-    assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
+    assert isinstance(lval, float)
    assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
    assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
    with pytest.raises(ValueError, match='Trade dataframe empty.'):
-        drawdown, h, low = calculate_max_drawdown(DataFrame())
+        drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
 def test_calculate_csum(testdatadir):
@ -294,6 +296,10 @@ def test_calculate_csum(testdatadir):
    assert isinstance(csum_max, float)
    assert csum_min < 0.01
    assert csum_max > 0.02
    csum_min1, csum_max1 = calculate_csum(bt_data, 5)
    assert csum_min1 == csum_min + 5
    assert csum_max1 == csum_max + 5
    with pytest.raises(ValueError, match='Trade dataframe empty.'):
        csum_min, csum_max = calculate_csum(DataFrame())
@ -310,13 +316,16 @@ def test_calculate_max_drawdown2():
    # sort by profit and reset index
    df = df.sort_values('profit').reset_index(drop=True)
    df1 = df.copy()
-    drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
+    drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
        df, date_col='open_date', value_col='profit')
    # Ensure df has not been altered.
    assert df.equals(df1)
    assert isinstance(drawdown, float)
    # High must be before low
-    assert h < low
+    assert hdate < ldate
    # High value must be higher than low value
    assert hval > lval
    assert drawdown == 0.091755
    df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
--- a/tests/optimize/test_backtest_detail.py
+++ b/tests/optimize/test_backtest_detail.py
@ -1,6 +1,5 @@
 # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, C0330, unused-argument
 import logging
 from unittest.mock import MagicMock
 import pytest
@ -489,7 +488,8 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
    default_conf["trailing_stop_positive_offset"] = data.trailing_stop_positive_offset
    default_conf["ask_strategy"] = {"use_sell_signal": data.use_sell_signal}
-    mocker.patch("freqtrade.exchange.Exchange.get_fee", MagicMock(return_value=0.0))
+    mocker.patch("freqtrade.exchange.Exchange.get_fee", return_value=0.0)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    patch_exchange(mocker)
    frame = _build_backtest_dataframe(data.data)
    backtesting = Backtesting(default_conf)
@ -503,7 +503,6 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
    min_date, max_date = get_timerange({pair: frame})
    results = backtesting.backtest(
        processed=data_processed,
        stake_amount=default_conf['stake_amount'],
        start_date=min_date,
        end_date=max_date,
        max_open_trades=10,
@ -514,6 +513,6 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
    for c, trade in enumerate(data.trades):
        res = results.iloc[c]
-        assert res.sell_reason == trade.sell_reason
+        assert res.sell_reason == trade.sell_reason.value
        assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
        assert res.close_date == _get_frame_time_from_offset(trade.close_tick)
--- a/tests/optimize/test_backtesting.py
+++ b/tests/optimize/test_backtesting.py
@ -9,7 +9,6 @@ import pandas as pd
 import pytest
 from arrow import Arrow
 from freqtrade import constants
 from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_backtesting
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
@ -19,6 +18,7 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import get_timerange
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import LocalTrade
 from freqtrade.resolvers import StrategyResolver
 from freqtrade.state import RunMode
 from freqtrade.strategy.interface import SellType
@ -90,7 +90,6 @@ def simple_backtest(config, contour, mocker, testdatadir) -> None:
    assert isinstance(processed, dict)
    results = backtesting.backtest(
        processed=processed,
        stake_amount=config['stake_amount'],
        start_date=min_date,
        end_date=max_date,
        max_open_trades=1,
@ -111,7 +110,6 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
    min_date, max_date = get_timerange(processed)
    return {
        'processed': processed,
        'stake_amount': conf['stake_amount'],
        'start_date': min_date,
        'end_date': max_date,
        'max_open_trades': 10,
@ -233,8 +231,7 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
    assert log_has('Parameter --fee detected, setting fee to: {} ...'.format(config['fee']), caplog)
-def test_setup_optimize_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None:
+def test_setup_optimize_configuration_stake_amount(mocker, default_conf, caplog) -> None:
    default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
    patched_configuration_load_config_file(mocker, default_conf)
@ -242,9 +239,21 @@ def test_setup_optimize_configuration_unlimited_stake_amount(mocker, default_con
        'backtesting',
        '--config', 'config.json',
        '--strategy', 'DefaultStrategy',
        '--stake-amount', '1',
        '--starting-balance', '2'
    ]
-    with pytest.raises(DependencyException, match=r'.`stake_amount`.*'):
+    conf = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
    assert isinstance(conf, dict)
    args = [
        'backtesting',
        '--config', 'config.json',
        '--strategy', 'DefaultStrategy',
        '--stake-amount', '1',
        '--starting-balance', '0.5'
    ]
    with pytest.raises(OperationalException, match=r"Starting balance .* smaller .*"):
        setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
@ -448,9 +457,48 @@ def test_backtesting_pairlist_list(default_conf, mocker, caplog, testdatadir, ti
        Backtesting(default_conf)
 def test_backtest__enter_trade(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['ask_strategy']['use_sell_signal'] = False
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    patch_exchange(mocker)
    default_conf['stake_amount'] = 'unlimited'
    backtesting = Backtesting(default_conf)
    pair = 'UNITTEST/BTC'
    row = [
        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
        1,  # Sell
        0.001,  # Open
        0.0011,  # Close
        0,  # Sell
        0.00099,  # Low
        0.0012,  # High
    ]
    trade = backtesting._enter_trade(pair, row=row, max_open_trades=2, open_trade_count=0)
    assert isinstance(trade, LocalTrade)
    assert trade.stake_amount == 495
    trade = backtesting._enter_trade(pair, row=row, max_open_trades=2, open_trade_count=2)
    assert trade is None
    # Stake-amount too high!
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=600.0)
    trade = backtesting._enter_trade(pair, row=row, max_open_trades=2, open_trade_count=0)
    assert trade is None
    # Stake-amount too high!
    mocker.patch("freqtrade.wallets.Wallets.get_trade_stake_amount",
                 side_effect=DependencyException)
    trade = backtesting._enter_trade(pair, row=row, max_open_trades=2, open_trade_count=0)
    assert trade is None
 def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['ask_strategy']['use_sell_signal'] = False
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
    pair = 'UNITTEST/BTC'
@ -461,7 +509,6 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
    min_date, max_date = get_timerange(processed)
    results = backtesting.backtest(
        processed=processed,
        stake_amount=default_conf['stake_amount'],
        start_date=min_date,
        end_date=max_date,
        max_open_trades=10,
@ -486,7 +533,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
         'trade_duration': [235, 40],
         'profit_ratio': [0.0, 0.0],
         'profit_abs': [0.0, 0.0],
-         'sell_reason': [SellType.ROI, SellType.ROI],
+         'sell_reason': [SellType.ROI.value, SellType.ROI.value],
         'initial_stop_loss_abs': [0.0940005, 0.09272236],
         'initial_stop_loss_ratio': [-0.1, -0.1],
         'stop_loss_abs': [0.0940005, 0.09272236],
@ -512,6 +559,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
 def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None:
    default_conf['ask_strategy']['use_sell_signal'] = False
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    patch_exchange(mocker)
    backtesting = Backtesting(default_conf)
@ -523,7 +571,6 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
    min_date, max_date = get_timerange(processed)
    results = backtesting.backtest(
        processed=processed,
        stake_amount=default_conf['stake_amount'],
        start_date=min_date,
        end_date=max_date,
        max_open_trades=1,
@ -558,6 +605,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
    default_conf['enable_protections'] = True
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    tests = [
        ['sine', 9],
        ['raise', 10],
@ -589,6 +637,7 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
        default_conf['protections'] = protections
        default_conf['enable_protections'] = True
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    # While buy-signals are unrealistic, running backtesting
    # over and over again should not cause different results
@ -626,6 +675,7 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
 def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    backtest_conf = _make_backtest_conf(mocker, conf=default_conf,
                                        pair='UNITTEST/BTC', datadir=testdatadir)
@ -658,6 +708,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
        dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
        return dataframe
    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
    patch_exchange(mocker)
@ -678,7 +729,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
    min_date, max_date = get_timerange(processed)
    backtest_conf = {
        'processed': processed,
        'stake_amount': default_conf['stake_amount'],
        'start_date': min_date,
        'end_date': max_date,
        'max_open_trades': 3,
@ -694,7 +744,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
    backtest_conf = {
        'processed': processed,
        'stake_amount': default_conf['stake_amount'],
        'start_date': min_date,
        'end_date': max_date,
        'max_open_trades': 1,
@ -822,6 +871,7 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
                                                    '2018-01-30 05:35:00', ], utc=True),
                      'trade_duration': [235, 40],
                      'is_open': [False, False],
                      'stake_amount': [0.01, 0.01],
                      'open_rate': [0.104445, 0.10302485],
                      'close_rate': [0.104969, 0.103541],
                      'sell_reason': [SellType.ROI, SellType.ROI]
@ -838,6 +888,7 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
                                                    '2018-01-30 08:30:00'], utc=True),
                      'trade_duration': [47, 40, 20],
                      'is_open': [False, False, False],
                      'stake_amount': [0.01, 0.01, 0.01],
                      'open_rate': [0.104445, 0.10302485, 0.122541],
                      'close_rate': [0.104969, 0.103541, 0.123541],
                      'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
--- a/tests/optimize/test_hyperopt.py
+++ b/tests/optimize/test_hyperopt.py
@ -12,10 +12,9 @@ import pytest
 from arrow import Arrow
 from filelock import Timeout
 from freqtrade import constants
 from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_hyperopt
 from freqtrade.data.history import load_data
-from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt import Hyperopt
 from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
 from freqtrade.state import RunMode
@ -130,8 +129,7 @@ def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplo
    assert log_has('Parameter --print-all detected ...', caplog)
-def test_setup_hyperopt_configuration_unlimited_stake_amount(mocker, default_conf) -> None:
+def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None:
    default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
    patched_configuration_load_config_file(mocker, default_conf)
@ -139,9 +137,20 @@ def test_setup_hyperopt_configuration_unlimited_stake_amount(mocker, default_con
        'hyperopt',
        '--config', 'config.json',
        '--hyperopt', 'DefaultHyperOpt',
        '--stake-amount', '1',
        '--starting-balance', '2'
    ]
    conf = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
    assert isinstance(conf, dict)
-    with pytest.raises(DependencyException, match=r'.`stake_amount`.*'):
+    args = [
        'hyperopt',
        '--config', 'config.json',
        '--strategy', 'DefaultStrategy',
        '--stake-amount', '1',
        '--starting-balance', '0.5'
    ]
    with pytest.raises(OperationalException, match=r"Starting balance .* smaller .*"):
        setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
--- a/tests/optimize/test_optimize_reports.py
+++ b/tests/optimize/test_optimize_reports.py
@ -48,7 +48,7 @@ def test_text_table_bt_results():
    )
    pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
-                                         max_open_trades=2, results=results)
+                                         starting_balance=4, results=results)
    assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
@ -73,11 +73,13 @@ def test_generate_backtest_stats(default_conf, testdatadir):
                                 "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
                                 "trade_duration": [123, 34, 31, 14],
                                 "is_open": [False, False, False, True],
                                 "stake_amount": [0.01, 0.01, 0.01, 0.01],
                                 "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
                                                 SellType.ROI, SellType.FORCE_SELL]
                                 }),
        'config': default_conf,
        'locks': [],
        'final_balance': 1000.02,
        'backtest_start_time': Arrow.utcnow().int_timestamp,
        'backtest_end_time': Arrow.utcnow().int_timestamp,
        }
@ -100,6 +102,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
    # Above sample had no loosing trade
    assert strat_stats['max_drawdown'] == 0.0
    # Retry with losing trade
    results = {'DefStrat': {
        'results': pd.DataFrame(
            {"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
@ -116,18 +119,31 @@ def test_generate_backtest_stats(default_conf, testdatadir):
             "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
             "close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
             "trade_duration": [123, 34, 31, 14],
-             "open_at_end": [False, False, False, True],
+             "is_open": [False, False, False, True],
-             "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+             "stake_amount": [0.01, 0.01, 0.01, 0.01],
-                             SellType.ROI, SellType.FORCE_SELL]
+             "sell_reason": [SellType.ROI, SellType.ROI,
                             SellType.STOP_LOSS, SellType.FORCE_SELL]
             }),
-        'config': default_conf}
+        'config': default_conf,
        'locks': [],
        'final_balance': 1000.02,
        'backtest_start_time': Arrow.utcnow().int_timestamp,
        'backtest_end_time': Arrow.utcnow().int_timestamp,
        }
    }
-    assert strat_stats['max_drawdown'] == 0.0
+    stats = generate_backtest_stats(btdata, results, min_date, max_date)
-    assert strat_stats['drawdown_start'] == datetime(1970, 1, 1, tzinfo=timezone.utc)
+    assert isinstance(stats, dict)
-    assert strat_stats['drawdown_end'] == datetime(1970, 1, 1, tzinfo=timezone.utc)
+    assert 'strategy' in stats
-    assert strat_stats['drawdown_end_ts'] == 0
+    assert 'DefStrat' in stats['strategy']
-    assert strat_stats['drawdown_start_ts'] == 0
+    assert 'strategy_comparison' in stats
    strat_stats = stats['strategy']['DefStrat']
    assert strat_stats['max_drawdown'] == 0.013803
    assert strat_stats['drawdown_start'] == datetime(2017, 11, 14, 22, 10, tzinfo=timezone.utc)
    assert strat_stats['drawdown_end'] == datetime(2017, 11, 14, 22, 43, tzinfo=timezone.utc)
    assert strat_stats['drawdown_end_ts'] == 1510699380000
    assert strat_stats['drawdown_start_ts'] == 1510697400000
    assert strat_stats['pairlist'] == ['UNITTEST/BTC']
    # Test storing stats
@ -189,7 +205,7 @@ def test_generate_pair_metrics():
    )
    pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
-                                         max_open_trades=2, results=results)
+                                         starting_balance=2, results=results)
    assert isinstance(pair_results, list)
    assert len(pair_results) == 2
    assert pair_results[-1]['key'] == 'TOTAL'
@ -265,7 +281,7 @@ def test_generate_sell_reason_stats():
            'wins': [2, 0, 0],
            'draws': [0, 0, 0],
            'losses': [0, 0, 1],
-            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
+            'sell_reason': [SellType.ROI.value, SellType.ROI.value, SellType.STOP_LOSS.value]
        }
    )
@ -291,6 +307,7 @@ def test_generate_sell_reason_stats():
 def test_text_table_strategy(default_conf):
    default_conf['max_open_trades'] = 2
    default_conf['dry_run_wallet'] = 3
    results = {}
    results['TestStrategy1'] = {'results': pd.DataFrame(
        {
@ -323,9 +340,9 @@ def test_text_table_strategy(default_conf):
        '|---------------+--------+----------------+----------------+------------------+'
        '----------------+----------------+--------+---------+----------|\n'
        '| TestStrategy1 |      3 |          20.00 |          60.00 |       1.10000000 |'
-        '          30.00 |        0:17:00 |      3 |       0 |        0 |\n'
+        '          36.67 |        0:17:00 |      3 |       0 |        0 |\n'
        '| TestStrategy2 |      3 |          30.00 |          90.00 |       1.30000000 |'
-        '          45.00 |        0:20:00 |      3 |       0 |        0 |'
+        '          43.33 |        0:20:00 |      3 |       0 |        0 |'
    )
    strategy_results = generate_strategy_metrics(all_results=results)
--- a/tests/plugins/test_pairlocks.py
+++ b/tests/plugins/test_pairlocks.py
@ -73,9 +73,13 @@ def test_PairLocks(use_db):
    assert PairLocks.is_pair_locked('XRP/USDT', lock_time + timedelta(minutes=-50))
    if use_db:
-        assert len(PairLock.query.all()) > 0
+        locks = PairLocks.get_all_locks()
        locks_db = PairLock.query.all()
        assert len(locks) == len(locks_db)
        assert len(locks_db) > 0
    else:
        # Nothing was pushed to the database
        assert len(PairLocks.get_all_locks()) > 0
        assert len(PairLock.query.all()) == 0
    # Reset use-db variable
    PairLocks.reset_locks()
--- a/tests/test_configuration.py
+++ b/tests/test_configuration.py
@ -430,7 +430,8 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
        '--enable-position-stacking',
        '--disable-max-market-positions',
        '--timerange', ':100',
-        '--export', '/bar/foo'
+        '--export', '/bar/foo',
        '--stake-amount', 'unlimited'
    ]
    args = Arguments(arglist).get_parsed_arg()
@ -463,6 +464,8 @@ def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> Non
    assert 'export' in config
    assert log_has('Parameter --export detected: {} ...'.format(config['export']), caplog)
    assert 'stake_amount' in config
    assert config['stake_amount'] == 'unlimited'
 def test_setup_configuration_with_stratlist(mocker, default_conf, caplog) -> None:
--- a/tests/test_freqtradebot.py
+++ b/tests/test_freqtradebot.py
@ -2243,6 +2243,7 @@ def test_check_handle_timedout_sell_usercustom(default_conf, ticker, limit_sell_
    open_trade.open_date = arrow.utcnow().shift(hours=-5).datetime
    open_trade.close_date = arrow.utcnow().shift(minutes=-601).datetime
    open_trade.close_profit_abs = 0.001
    open_trade.is_open = False
    Trade.session.add(open_trade)
@ -2290,6 +2291,7 @@ def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old,
    open_trade.open_date = arrow.utcnow().shift(hours=-5).datetime
    open_trade.close_date = arrow.utcnow().shift(minutes=-601).datetime
    open_trade.close_profit_abs = 0.001
    open_trade.is_open = False
    Trade.session.add(open_trade)
--- a/tests/test_persistence.py
+++ b/tests/test_persistence.py
@ -1,5 +1,6 @@
 # pragma pylint: disable=missing-docstring, C0103
 import logging
 from types import FunctionType
 from unittest.mock import MagicMock
 import arrow
@ -8,7 +9,7 @@ from sqlalchemy import create_engine
 from freqtrade import constants
 from freqtrade.exceptions import DependencyException, OperationalException
-from freqtrade.persistence import Order, Trade, clean_dry_run_db, init_db
+from freqtrade.persistence import LocalTrade, Order, Trade, clean_dry_run_db, init_db
 from tests.conftest import create_mock_trades, log_has, log_has_re
@ -1039,14 +1040,18 @@ def test_fee_updated(fee):
@pytest.mark.usefixtures("init_persistence")
-def test_total_open_trades_stakes(fee):
+@pytest.mark.parametrize('use_db', [True, False])
 def test_total_open_trades_stakes(fee, use_db):
    Trade.use_db = use_db
    res = Trade.total_open_trades_stakes()
    assert res == 0
-    create_mock_trades(fee)
+    create_mock_trades(fee, use_db)
    res = Trade.total_open_trades_stakes()
    assert res == 0.004
    Trade.use_db = True
@pytest.mark.usefixtures("init_persistence")
 def test_get_overall_performance(fee):
@ -1172,3 +1177,25 @@ def test_select_order(fee):
    assert order.ft_order_side == 'stoploss'
    order = trades[4].select_order('sell', False)
    assert order is None
 def test_Trade_object_idem():
    assert issubclass(Trade, LocalTrade)
    trade = vars(Trade)
    localtrade = vars(LocalTrade)
    # Parent (LocalTrade) should have the same attributes
    for item in trade:
        # Exclude private attributes and open_date (as it's not assigned a default)
        if (not item.startswith('_')
                and item not in ('delete', 'session', 'query', 'open_date')):
            assert item in localtrade
    # Fails if only a column is added without corresponding parent field
    for item in localtrade:
        if (not item.startswith('__')
                and item not in ('trades', )
                and type(getattr(LocalTrade, item)) not in (property, FunctionType)):
            assert item in trade