diff --git a/frontend/visualization/candles.py b/frontend/visualization/candles.py index 3300249..6bbbdda 100644 --- a/frontend/visualization/candles.py +++ b/frontend/visualization/candles.py @@ -15,7 +15,7 @@ def get_candlestick_trace(df): def get_bt_candlestick_trace(df): - df.index = pd.to_datetime(df.timestamp, unit='ms') + df.index = pd.to_datetime(df.timestamp, unit='s') return go.Scatter(x=df.index, y=df['close'], mode='lines', diff --git a/frontend/visualization/executors.py b/frontend/visualization/executors.py index 83fff4a..b94cff7 100644 --- a/frontend/visualization/executors.py +++ b/frontend/visualization/executors.py @@ -7,9 +7,9 @@ from hummingbot.connector.connector_base import TradeType def add_executors_trace(fig, executors, row, col): for executor in executors: - entry_time = pd.to_datetime(executor.timestamp, unit='ms') + entry_time = pd.to_datetime(executor.timestamp, unit='s') entry_price = executor.custom_info["current_position_average_price"] - exit_time = pd.to_datetime(executor.close_timestamp, unit='ms') + exit_time = pd.to_datetime(executor.close_timestamp, unit='s') exit_price = executor.custom_info["close_price"] name = "Buy Executor" if executor.config.side == TradeType.BUY else "Sell Executor" diff --git a/frontend/visualization/indicators.py b/frontend/visualization/indicators.py index 93f86bf..e5965a4 100644 --- a/frontend/visualization/indicators.py +++ b/frontend/visualization/indicators.py @@ -23,7 +23,7 @@ def get_bbands_traces(df, bb_length, bb_std): def get_volume_trace(df): - df.index = pd.to_datetime(df.timestamp, unit='ms') + df.index = pd.to_datetime(df.timestamp, unit='s') return go.Bar(x=df.index, y=df['volume'], name="Volume", marker_color=theme.get_color_scheme()["volume"], opacity=0.7) def get_macd_traces(df, macd_fast, macd_slow, macd_signal): diff --git a/frontend/visualization/pnl.py b/frontend/visualization/pnl.py index 11f17f6..488f75e 100644 --- a/frontend/visualization/pnl.py +++ b/frontend/visualization/pnl.py @@ -7,7 +7,7 @@ def get_pnl_trace(executors): pnl = [e.net_pnl_quote for e in executors] cum_pnl = np.cumsum(pnl) return go.Scatter( - x=pd.to_datetime([e.close_timestamp for e in executors], unit="ms"), + x=pd.to_datetime([e.close_timestamp for e in executors], unit="s"), y=cum_pnl, mode='lines', line=dict(color='gold', width=2, dash="dash"),