diff --git a/vnpy/app/spread_trading/backtesting.py b/vnpy/app/spread_trading/backtesting.py
new file mode 100644
index 00000000..199c8065
--- /dev/null
+++ b/vnpy/app/spread_trading/backtesting.py
@@ -0,0 +1,716 @@
+from collections import defaultdict
+from datetime import date, datetime, timedelta
+from typing import Callable
+from functools import lru_cache
+
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pandas import DataFrame
+
+from vnpy.trader.constant import (Direction, Offset, Exchange,
+                                  Interval, Status)
+from vnpy.trader.database import database_manager
+from vnpy.trader.object import TradeData, BarData, TickData
+from vnpy.trader.utility import round_to
+
+from .template import SpreadStrategyTemplate
+from .base import SpreadData, BacktestingMode
+
+sns.set_style("whitegrid")
+
+
+class BacktestingEngine:
+    """"""
+
+    gateway_name = "BACKTESTING"
+
+    def __init__(self):
+        """"""
+        self.spread: SpreadData = None
+
+        self.start = None
+        self.end = None
+        self.rate = 0
+        self.slippage = 0
+        self.size = 1
+        self.pricetick = 0
+        self.capital = 1_000_000
+        self.mode = BacktestingMode.BAR
+
+        self.strategy_class = None
+        self.strategy = None
+        self.tick: TickData = None
+        self.bar: BarData = None
+        self.datetime = None
+
+        self.interval = None
+        self.days = 0
+        self.callback = None
+        self.history_data = []
+
+        self.algo_count = 0
+        self.algos = {}
+        self.active_algos = {}
+
+        self.trade_count = 0
+        self.trades = {}
+
+        self.logs = []
+
+        self.daily_results = {}
+        self.daily_df = None
+
+    def output(self, msg):
+        """
+        Output message of backtesting engine.
+        """
+        print(f"{datetime.now()}\t{msg}")
+
+    def clear_data(self):
+        """
+        Clear all data of last backtesting.
+        """
+        self.strategy = None
+        self.tick = None
+        self.bar = None
+        self.datetime = None
+
+        self.algo_count = 0
+        self.algos.clear()
+        self.active_algos.clear()
+
+        self.trade_count = 0
+        self.trades.clear()
+
+        self.logs.clear()
+        self.daily_results.clear()
+
+    def set_parameters(
+        self,
+        spread: SpreadData,
+        interval: Interval,
+        start: datetime,
+        rate: float,
+        slippage: float,
+        size: float,
+        pricetick: float,
+        capital: int = 0,
+        end: datetime = None,
+        mode: BacktestingMode = BacktestingMode.BAR
+    ):
+        """"""
+        self.spread = spread
+        self.interval = Interval(interval)
+        self.rate = rate
+        self.slippage = slippage
+        self.size = size
+        self.pricetick = pricetick
+        self.start = start
+        self.capital = capital
+        self.end = end
+        self.mode = mode
+
+    def add_strategy(self, strategy_class: type, setting: dict):
+        """"""
+        self.strategy_class = strategy_class
+
+        self.strategy = strategy_class(
+            self,
+            strategy_class.__name__,
+            self.spread,
+            setting
+        )
+
+    def load_data(self):
+        """"""
+        self.output("开始加载历史数据")
+
+        if not self.end:
+            self.end = datetime.now()
+
+        if self.start >= self.end:
+            self.output("起始日期必须小于结束日期")
+            return
+
+        if self.mode == BacktestingMode.BAR:
+            self.history_data = load_bar_data(
+                self.spread,
+                self.interval,
+                self.start,
+                self.end
+            )
+        else:
+            self.history_datas = load_tick_data(
+                self.spread,
+                self.start,
+                self.end
+            )
+
+        self.output(f"历史数据加载完成，数据量：{len(self.history_data)}")
+
+    def run_backtesting(self):
+        """"""
+        if self.mode == BacktestingMode.BAR:
+            func = self.new_bar
+        else:
+            func = self.new_tick
+
+        self.strategy.on_init()
+
+        # Use the first [days] of history data for initializing strategy
+        day_count = 0
+        ix = 0
+
+        for ix, data in enumerate(self.history_data):
+            if self.datetime and data.datetime.day != self.datetime.day:
+                day_count += 1
+                if day_count >= self.days:
+                    break
+
+            self.datetime = data.datetime
+            self.callback(data)
+
+        self.strategy.inited = True
+        self.output("策略初始化完成")
+
+        self.strategy.on_start()
+        self.strategy.trading = True
+        self.output("开始回放历史数据")
+
+        # Use the rest of history data for running backtesting
+        for data in self.history_data[ix:]:
+            func(data)
+
+        self.output("历史数据回放结束")
+
+    def calculate_result(self):
+        """"""
+        self.output("开始计算逐日盯市盈亏")
+
+        if not self.trades:
+            self.output("成交记录为空，无法计算")
+            return
+
+        # Add trade data into daily reuslt.
+        for trade in self.trades.values():
+            d = trade.datetime.date()
+            daily_result = self.daily_results[d]
+            daily_result.add_trade(trade)
+
+        # Calculate daily result by iteration.
+        pre_close = 0
+        start_pos = 0
+
+        for daily_result in self.daily_results.values():
+            daily_result.calculate_pnl(
+                pre_close,
+                start_pos,
+                self.size,
+                self.rate,
+                self.slippage,
+                self.inverse
+            )
+
+            pre_close = daily_result.close_price
+            start_pos = daily_result.end_pos
+
+        # Generate dataframe
+        results = defaultdict(list)
+
+        for daily_result in self.daily_results.values():
+            for key, value in daily_result.__dict__.items():
+                results[key].append(value)
+
+        self.daily_df = DataFrame.from_dict(results).set_index("date")
+
+        self.output("逐日盯市盈亏计算完成")
+        return self.daily_df
+
+    def calculate_statistics(self, df: DataFrame = None, output=True):
+        """"""
+        self.output("开始计算策略统计指标")
+
+        # Check DataFrame input exterior
+        if df is None:
+            df = self.daily_df
+
+        # Check for init DataFrame
+        if df is None:
+            # Set all statistics to 0 if no trade.
+            start_date = ""
+            end_date = ""
+            total_days = 0
+            profit_days = 0
+            loss_days = 0
+            end_balance = 0
+            max_drawdown = 0
+            max_ddpercent = 0
+            max_drawdown_duration = 0
+            total_net_pnl = 0
+            daily_net_pnl = 0
+            total_commission = 0
+            daily_commission = 0
+            total_slippage = 0
+            daily_slippage = 0
+            total_turnover = 0
+            daily_turnover = 0
+            total_trade_count = 0
+            daily_trade_count = 0
+            total_return = 0
+            annual_return = 0
+            daily_return = 0
+            return_std = 0
+            sharpe_ratio = 0
+            return_drawdown_ratio = 0
+        else:
+            # Calculate balance related time series data
+            df["balance"] = df["net_pnl"].cumsum() + self.capital
+            df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0)
+            df["highlevel"] = (
+                df["balance"].rolling(
+                    min_periods=1, window=len(df), center=False).max()
+            )
+            df["drawdown"] = df["balance"] - df["highlevel"]
+            df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100
+
+            # Calculate statistics value
+            start_date = df.index[0]
+            end_date = df.index[-1]
+
+            total_days = len(df)
+            profit_days = len(df[df["net_pnl"] > 0])
+            loss_days = len(df[df["net_pnl"] < 0])
+
+            end_balance = df["balance"].iloc[-1]
+            max_drawdown = df["drawdown"].min()
+            max_ddpercent = df["ddpercent"].min()
+            max_drawdown_end = df["drawdown"].idxmin()
+            max_drawdown_start = df["balance"][:max_drawdown_end].argmax()
+            max_drawdown_duration = (max_drawdown_end - max_drawdown_start).days
+
+            total_net_pnl = df["net_pnl"].sum()
+            daily_net_pnl = total_net_pnl / total_days
+
+            total_commission = df["commission"].sum()
+            daily_commission = total_commission / total_days
+
+            total_slippage = df["slippage"].sum()
+            daily_slippage = total_slippage / total_days
+
+            total_turnover = df["turnover"].sum()
+            daily_turnover = total_turnover / total_days
+
+            total_trade_count = df["trade_count"].sum()
+            daily_trade_count = total_trade_count / total_days
+
+            total_return = (end_balance / self.capital - 1) * 100
+            annual_return = total_return / total_days * 240
+            daily_return = df["return"].mean() * 100
+            return_std = df["return"].std() * 100
+
+            if return_std:
+                sharpe_ratio = daily_return / return_std * np.sqrt(240)
+            else:
+                sharpe_ratio = 0
+
+            return_drawdown_ratio = -total_return / max_ddpercent
+
+        # Output
+        if output:
+            self.output("-" * 30)
+            self.output(f"首个交易日：\t{start_date}")
+            self.output(f"最后交易日：\t{end_date}")
+
+            self.output(f"总交易日：\t{total_days}")
+            self.output(f"盈利交易日：\t{profit_days}")
+            self.output(f"亏损交易日：\t{loss_days}")
+
+            self.output(f"起始资金：\t{self.capital:,.2f}")
+            self.output(f"结束资金：\t{end_balance:,.2f}")
+
+            self.output(f"总收益率：\t{total_return:,.2f}%")
+            self.output(f"年化收益：\t{annual_return:,.2f}%")
+            self.output(f"最大回撤: \t{max_drawdown:,.2f}")
+            self.output(f"百分比最大回撤: {max_ddpercent:,.2f}%")
+            self.output(f"最长回撤天数: \t{max_drawdown_duration}")
+
+            self.output(f"总盈亏：\t{total_net_pnl:,.2f}")
+            self.output(f"总手续费：\t{total_commission:,.2f}")
+            self.output(f"总滑点：\t{total_slippage:,.2f}")
+            self.output(f"总成交金额：\t{total_turnover:,.2f}")
+            self.output(f"总成交笔数：\t{total_trade_count}")
+
+            self.output(f"日均盈亏：\t{daily_net_pnl:,.2f}")
+            self.output(f"日均手续费：\t{daily_commission:,.2f}")
+            self.output(f"日均滑点：\t{daily_slippage:,.2f}")
+            self.output(f"日均成交金额：\t{daily_turnover:,.2f}")
+            self.output(f"日均成交笔数：\t{daily_trade_count}")
+
+            self.output(f"日均收益率：\t{daily_return:,.2f}%")
+            self.output(f"收益标准差：\t{return_std:,.2f}%")
+            self.output(f"Sharpe Ratio：\t{sharpe_ratio:,.2f}")
+            self.output(f"收益回撤比：\t{return_drawdown_ratio:,.2f}")
+
+        statistics = {
+            "start_date": start_date,
+            "end_date": end_date,
+            "total_days": total_days,
+            "profit_days": profit_days,
+            "loss_days": loss_days,
+            "capital": self.capital,
+            "end_balance": end_balance,
+            "max_drawdown": max_drawdown,
+            "max_ddpercent": max_ddpercent,
+            "max_drawdown_duration": max_drawdown_duration,
+            "total_net_pnl": total_net_pnl,
+            "daily_net_pnl": daily_net_pnl,
+            "total_commission": total_commission,
+            "daily_commission": daily_commission,
+            "total_slippage": total_slippage,
+            "daily_slippage": daily_slippage,
+            "total_turnover": total_turnover,
+            "daily_turnover": daily_turnover,
+            "total_trade_count": total_trade_count,
+            "daily_trade_count": daily_trade_count,
+            "total_return": total_return,
+            "annual_return": annual_return,
+            "daily_return": daily_return,
+            "return_std": return_std,
+            "sharpe_ratio": sharpe_ratio,
+            "return_drawdown_ratio": return_drawdown_ratio,
+        }
+
+        return statistics
+
+    def show_chart(self, df: DataFrame = None):
+        """"""
+        # Check DataFrame input exterior
+        if df is None:
+            df = self.daily_df
+
+        # Check for init DataFrame
+        if df is None:
+            return
+
+        plt.figure(figsize=(10, 16))
+
+        balance_plot = plt.subplot(4, 1, 1)
+        balance_plot.set_title("Balance")
+        df["balance"].plot(legend=True)
+
+        drawdown_plot = plt.subplot(4, 1, 2)
+        drawdown_plot.set_title("Drawdown")
+        drawdown_plot.fill_between(range(len(df)), df["drawdown"].values)
+
+        pnl_plot = plt.subplot(4, 1, 3)
+        pnl_plot.set_title("Daily Pnl")
+        df["net_pnl"].plot(kind="bar", legend=False, grid=False, xticks=[])
+
+        distribution_plot = plt.subplot(4, 1, 4)
+        distribution_plot.set_title("Daily Pnl Distribution")
+        df["net_pnl"].hist(bins=50)
+
+        plt.show()
+
+    def update_daily_close(self, price: float):
+        """"""
+        d = self.datetime.date()
+
+        daily_result = self.daily_results.get(d, None)
+        if daily_result:
+            daily_result.close_price = price
+        else:
+            self.daily_results[d] = DailyResult(d, price)
+
+    def new_bar(self, bar: BarData):
+        """"""
+        self.bar = bar
+        self.datetime = bar.datetime
+
+        self.cross_limit_order()
+        self.cross_stop_order()
+        self.strategy.on_bar(bar)
+
+        self.update_daily_close(bar.close_price)
+
+    def new_tick(self, tick: TickData):
+        """"""
+        self.tick = tick
+        self.datetime = tick.datetime
+
+        self.cross_limit_order()
+        self.cross_stop_order()
+        self.strategy.on_tick(tick)
+
+        self.update_daily_close(tick.last_price)
+
+    def cross_limit_order(self):
+        """
+        Cross limit order with last bar/tick data.
+        """
+        if self.mode == BacktestingMode.BAR:
+            long_cross_price = self.bar.low_price
+            short_cross_price = self.bar.high_price
+            long_best_price = self.bar.open_price
+            short_best_price = self.bar.open_price
+        else:
+            long_cross_price = self.tick.ask_price_1
+            short_cross_price = self.tick.bid_price_1
+            long_best_price = long_cross_price
+            short_best_price = short_cross_price
+
+        for order in list(self.active_limit_orders.values()):
+            # Push order update with status "not traded" (pending).
+            if order.status == Status.SUBMITTING:
+                order.status = Status.NOTTRADED
+                self.strategy.on_order(order)
+
+            # Check whether limit orders can be filled.
+            long_cross = (
+                order.direction == Direction.LONG
+                and order.price >= long_cross_price
+                and long_cross_price > 0
+            )
+
+            short_cross = (
+                order.direction == Direction.SHORT
+                and order.price <= short_cross_price
+                and short_cross_price > 0
+            )
+
+            if not long_cross and not short_cross:
+                continue
+
+            # Push order udpate with status "all traded" (filled).
+            order.traded = order.volume
+            order.status = Status.ALLTRADED
+            self.strategy.on_order(order)
+
+            self.active_limit_orders.pop(order.vt_orderid)
+
+            # Push trade update
+            self.trade_count += 1
+
+            if long_cross:
+                trade_price = min(order.price, long_best_price)
+                pos_change = order.volume
+            else:
+                trade_price = max(order.price, short_best_price)
+                pos_change = -order.volume
+
+            trade = TradeData(
+                symbol=order.symbol,
+                exchange=order.exchange,
+                orderid=order.orderid,
+                tradeid=str(self.trade_count),
+                direction=order.direction,
+                offset=order.offset,
+                price=trade_price,
+                volume=order.volume,
+                time=self.datetime.strftime("%H:%M:%S"),
+                gateway_name=self.gateway_name,
+            )
+            trade.datetime = self.datetime
+
+            self.strategy.pos += pos_change
+            self.strategy.on_trade(trade)
+
+            self.trades[trade.vt_tradeid] = trade
+
+    def load_bar(
+        self, spread: str, days: int, interval: Interval, callback: Callable
+    ):
+        """"""
+        self.days = days
+        self.callback = callback
+
+    def load_tick(self, spread: str, days: int, callback: Callable):
+        """"""
+        self.days = days
+        self.callback = callback
+
+    def start_algo(
+        self,
+        strategy: SpreadStrategyTemplate,
+        spread_name: str,
+        direction: Direction,
+        offset: Offset,
+        price: float,
+        volume: float,
+        payup: int,
+        interval: int,
+        lock: bool
+    ) -> str:
+        """"""
+        pass
+
+    def stop_algo(
+        self,
+        algoid: str
+    ):
+        """"""
+        pass
+
+    def send_order(
+        self,
+        strategy: SpreadStrategyTemplate,
+        direction: Direction,
+        offset: Offset,
+        price: float,
+        volume: float,
+        stop: bool,
+        lock: bool
+    ):
+        """"""
+        price = round_to(price, self.pricetick)
+        if stop:
+            vt_orderid = self.send_stop_order(direction, offset, price, volume)
+        else:
+            vt_orderid = self.send_limit_order(direction, offset, price, volume)
+        return [vt_orderid]
+
+    def cancel_order(self, strategy: SpreadStrategyTemplate, vt_orderid: str):
+        """
+        Cancel order by vt_orderid.
+        """
+        if vt_orderid.startswith(STOPORDER_PREFIX):
+            self.cancel_stop_order(strategy, vt_orderid)
+        else:
+            self.cancel_limit_order(strategy, vt_orderid)
+
+    def write_log(self, msg: str, strategy: SpreadStrategyTemplate = None):
+        """
+        Write log message.
+        """
+        msg = f"{self.datetime}\t{msg}"
+        self.logs.append(msg)
+
+    def send_email(self, msg: str, strategy: SpreadStrategyTemplate = None):
+        """
+        Send email to default receiver.
+        """
+        pass
+
+    def put_strategy_event(self, strategy: SpreadStrategyTemplate):
+        """
+        Put an event to update strategy status.
+        """
+        pass
+
+
+class DailyResult:
+    """"""
+
+    def __init__(self, date: date, close_price: float):
+        """"""
+        self.date = date
+        self.close_price = close_price
+        self.pre_close = 0
+
+        self.trades = []
+        self.trade_count = 0
+
+        self.start_pos = 0
+        self.end_pos = 0
+
+        self.turnover = 0
+        self.commission = 0
+        self.slippage = 0
+
+        self.trading_pnl = 0
+        self.holding_pnl = 0
+        self.total_pnl = 0
+        self.net_pnl = 0
+
+    def add_trade(self, trade: TradeData):
+        """"""
+        self.trades.append(trade)
+
+    def calculate_pnl(
+        self,
+        pre_close: float,
+        start_pos: float,
+        size: int,
+        rate: float,
+        slippage: float,
+        inverse: bool
+    ):
+        """"""
+        # If no pre_close provided on the first day,
+        # use value 1 to avoid zero division error
+        if pre_close:
+            self.pre_close = pre_close
+        else:
+            self.pre_close = 1
+
+        # Holding pnl is the pnl from holding position at day start
+        self.start_pos = start_pos
+        self.end_pos = start_pos
+
+        if not inverse:     # For normal contract
+            self.holding_pnl = self.start_pos * \
+                (self.close_price - self.pre_close) * size
+        else:               # For crypto currency inverse contract
+            self.holding_pnl = self.start_pos * \
+                (1 / self.pre_close - 1 / self.close_price) * size
+
+        # Trading pnl is the pnl from new trade during the day
+        self.trade_count = len(self.trades)
+
+        for trade in self.trades:
+            if trade.direction == Direction.LONG:
+                pos_change = trade.volume
+            else:
+                pos_change = -trade.volume
+
+            self.end_pos += pos_change
+
+            # For normal contract
+            if not inverse:
+                turnover = trade.volume * size * trade.price
+                self.trading_pnl += pos_change * \
+                    (self.close_price - trade.price) * size
+                self.slippage += trade.volume * size * slippage
+            # For crypto currency inverse contract
+            else:
+                turnover = trade.volume * size / trade.price
+                self.trading_pnl += pos_change * \
+                    (1 / trade.price - 1 / self.close_price) * size
+                self.slippage += trade.volume * size * slippage / (trade.price ** 2)
+
+            self.turnover += turnover
+            self.commission += turnover * rate
+
+        # Net pnl takes account of commission and slippage cost
+        self.total_pnl = self.trading_pnl + self.holding_pnl
+        self.net_pnl = self.total_pnl - self.commission - self.slippage
+
+
+
+@lru_cache(maxsize=999)
+def load_bar_data(
+    symbol: str,
+    exchange: Exchange,
+    interval: Interval,
+    start: datetime,
+    end: datetime
+):
+    """"""
+    return database_manager.load_bar_data(
+        symbol, exchange, interval, start, end
+    )
+
+
+@lru_cache(maxsize=999)
+def load_tick_data(
+    symbol: str,
+    exchange: Exchange,
+    start: datetime,
+    end: datetime
+):
+    """"""
+    return database_manager.load_tick_data(
+        symbol, exchange, start, end
+    )
+
diff --git a/vnpy/app/spread_trading/base.py b/vnpy/app/spread_trading/base.py
index efbe185f..374e48b5 100644
--- a/vnpy/app/spread_trading/base.py
+++ b/vnpy/app/spread_trading/base.py
@@ -347,3 +347,8 @@ def calculate_inverse_volume(
     if not price:
         return 0
     return original_volume * size / price
+
+
+class BacktestingMode(Enum):
+    BAR = 1
+    TICK = 2