init
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dsyoon
@@ -37,8 +37,7 @@ class Simulation (HTS):
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data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
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buy_line = bsLine['buy'][len(bsLine['buy'])-len(data):]
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buy_weight_line = bsLine['buy_weight'][len(bsLine['buy'])-len(data):]
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sell_line = bsLine['sell'][len(bsLine['sell'])-len(data):]
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sell_weight_line = bsLine['sell_weight'][len(bsLine['sell']) - len(data):]
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sell_line = bsLine['sell'][len(bsLine['buy'])-len(data):]
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# 그래프 설정을 위한 변수를 생성한다.
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data = data.astype({'open': 'int',
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@@ -74,16 +73,13 @@ class Simulation (HTS):
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buy_colors.append("#0C752E")
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buy_size.append(10 + (5 * buy_weight_line[i]))
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sell_size = []
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sell_colors = []
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for i in range(len(sell_line)):
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if sell_line[i] < 0:
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sell_colors.append("#ffffff")
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sell_line[i] = nan
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sell_size.append(0)
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else:
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sell_colors.append("#00ced1")
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sell_size.append(10 + (5 * sell_weight_line[i]))
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volume_colors = []
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for i in range(len(buy_line)):
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@@ -97,19 +93,7 @@ class Simulation (HTS):
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# 그래프를 설정한다.
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buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
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sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
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upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
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lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
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avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#F81191')
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avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#097F19')
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avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#097F19')
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avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#671BEA')
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avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#DFB809')
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avg200 = go.Scatter(x=data['date'], y=data["avg200"], name="avg200", line_color='#000000')
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laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
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changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200')
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baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D')
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candle_stick = go.Candlestick(x=data['date'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue', showlegend=False)
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#volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
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volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color=volume_colors, name='volume')
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@@ -131,9 +115,45 @@ class Simulation (HTS):
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rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
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rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
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upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
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lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
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avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#F81191')
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avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#097F19')
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avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#097F19')
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avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#671BEA')
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avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#DFB809')
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avg200 = go.Scatter(x=data['date'], y=data["avg200"], name="avg200", line_color='#000000')
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laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
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changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200')
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baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D')
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text_list = []
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for i in range(len(data['macd'])):
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text = "open: {}<br>close: {}<br>high: {}<br>low: {}<br>volume: {}<br><br>avg5: {:.2f}<br>avg20: {:.2f}<br>avg60: {:.2f}<br>avg200: {:.2f}<br><br>d_avg5: {:.6f}<br>d_avg20: {:.6f}<br>d_avg60: {:.6f}<br>d_avg200: {:.6f}<br>d_avg5_60: {:.6f}<br>d_avg5_200: {:.6f}<br><br>macd: {:.2f}<br>slow_k: {:.2f}<br>rsi: {:.2f}".format(
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data['open'][i],data['close'][i],data['high'][i],data['low'][i],data['volume'][i],
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data['avg5'][i],data['avg20'][i],data['avg60'][i],data['avg200'][i],
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data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i],
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max(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i]) - min(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i]),
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max(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i]) - min(data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity_avg200'][i]),
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data['macd'][i],data['slow_k'][i],data['rsi'][i]
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)
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text_list.append(text)
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candle_stick = go.Candlestick(x=data['date'],
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open=data['open'],
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high=data['high'],
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low=data['low'],
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close=data['close'],
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increasing_line_color='red',
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decreasing_line_color='blue',
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showlegend=False,
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text=text_list,
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hoverinfo="text"
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)
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#candle_data = [candle_stick, upper, lower, avg5, avg20, avg30, avg60, avg120, avg200, buy_check, sell_check, laggingSpan, changeLine, baseLine]
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candle_data = [candle_stick, avg5, avg20, avg30, avg60, avg200, buy_check, sell_check]
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#candle_data = [candle_stick, avg200, buy_check, sell_check]
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#candle_data = [candle_stick, avg5, avg200, buy_check, sell_check]
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volume_data = [volume_line]
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disparity_data = [disparity_avg5, disparity_avg20, disparity_avg30, disparity_avg60, disparity_avg120, disparity_avg200]
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macd_data = [macd_line, macd_s_line, macd_o_line]
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@@ -149,20 +169,20 @@ class Simulation (HTS):
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fig = subplots.make_subplots(
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rows=6, cols=1,
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subplot_titles=("이격도", "스토캐스틱", "RSI", "MACD", "거래량", '캔들'),
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subplot_titles=("거래량", "이격도", "스토캐스틱", "RSI", "MACD", '캔들'),
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#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
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shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
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row_heights=[200, 200, 200, 200, 200, 700]
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)
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for trace in disparity_data:
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fig.append_trace(trace, 1, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 2, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 3, 1)
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for trace in macd_data:
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fig.append_trace(trace, 4, 1)
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for trace in volume_data:
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fig.append_trace(trace, 1, 1)
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for trace in disparity_data:
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fig.append_trace(trace, 2, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 3, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 4, 1)
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for trace in macd_data:
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fig.append_trace(trace, 5, 1)
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for trace in candle_data:
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fig.append_trace(trace, 6, 1)
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