blade
/
crypto_quant
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

optimize chart output

This commit is contained in:
blade 2025-08-25 16:58:38 +08:00
parent 5700e8e7e1
commit 69aff37c24
3 changed files with 201 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
core/trade/__pycache__/ma_break_statistics.cpython-312.pyc
View File

Binary file not shown.

227
core/trade/ma_break_statistics.py
View File

@ -93,10 +93,13 @@ class MaBreakStatistics:
f"{symbol} {bar} 的市场价格变化, {market_data_pct_chg.get('pct_chg', 0)}%"
)
market_data_pct_chg_list.append(market_data_pct_chg)
if len(ma_break_market_data_list) > 0:
ma_break_market_data = pd.concat(ma_break_market_data_list)
market_data_pct_chg_df = pd.DataFrame(market_data_pct_chg_list)
# 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count
ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
ma_break_market_data.reset_index(drop=True, inplace=True)
pct_chg_df = (
ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
.agg(
@ -127,7 +130,7 @@ class MaBreakStatistics:
symbol_bar_data = ma_break_market_data[
(ma_break_market_data["symbol"] == symbol)
& (ma_break_market_data["bar"] == bar)
]
].copy() # 创建副本避免SettingWithCopyWarning
if len(symbol_bar_data) > 0:
symbol_bar_data.sort_values(
by="end_timestamp", ascending=True, inplace=True
@ -136,6 +139,14 @@ class MaBreakStatistics:
symbol_bar_data["pct_chg_total"] = symbol_bar_data[
"pct_chg_total"
].cumprod()
# 将更新后的pct_chg_total数据同步更新到ma_break_market_data的对应数据行中
for idx, row in symbol_bar_data.iterrows():
mask = (ma_break_market_data["symbol"] == symbol) & \
(ma_break_market_data["bar"] == bar) & \
(ma_break_market_data["end_timestamp"] == row["end_timestamp"])
ma_break_market_data.loc[mask, "pct_chg_total"] = row["pct_chg_total"]
last_pct_chg_total = symbol_bar_data["pct_chg_total"].iloc[-1]
last_pct_chg_total = (last_pct_chg_total - 1) * 100
pct_chg_df.loc[
@ -208,7 +219,9 @@ class MaBreakStatistics:
writer, sheet_name="买卖时间间隔统计", index=False
)
chart_dict = self.draw_pct_chg_mean_chart(pct_chg_df, strategy_name)
chart_dict = self.draw_quant_pct_chg_bar_chart(pct_chg_df, strategy_name)
self.output_chart_to_excel(output_file_path, chart_dict)
chart_dict = self.draw_quant_line_chart(ma_break_market_data, strategy_name)
self.output_chart_to_excel(output_file_path, chart_dict)
return pct_chg_df
else:
@ -370,13 +383,17 @@ class MaBreakStatistics:
if len(ma_break_market_data_pair_list) > 0:
ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
# sort by end_timestamp
ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
ma_break_market_data.reset_index(drop=True, inplace=True)
logger.info(
f"获取{symbol} {bar} 的买卖记录明细成功, 买卖次数: {len(ma_break_market_data)}"
)
# 将market_data(最后一条数据的close - 第一条数据的open) / 第一条数据的open * 100
# 量化期间，市场的波动率：
# ma_break_market_data(最后一条数据的end_close - 第一条数据的begin_close) / 第一条数据的begin_close * 100
pct_chg = (
(market_data["close"].iloc[-1] - market_data["open"].iloc[0])
/ market_data["open"].iloc[0]
(ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0])
/ ma_break_market_data["begin_close"].iloc[0]
* 100
)
pct_chg = round(pct_chg, 4)
@ -511,7 +528,7 @@ class MaBreakStatistics:
pass
return condition
def draw_pct_chg_mean_chart(
def draw_quant_pct_chg_bar_chart(
self, data: pd.DataFrame, strategy_name: str = "全均线策略"
):
"""
@ -527,48 +544,204 @@ class MaBreakStatistics:
plt.rcParams["font.size"] = 11 # 设置字体大小
plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题
chart_dict = {}
column_name_dict = {"pct_chg_total": "涨跌总和", "pct_chg_mean": "涨跌均值"}
column_name_dict = {"pct_chg_total": "量化策略涨跌", "pct_chg_mean": "量化策略涨跌均值"}
for column_name, column_name_text in column_name_dict.items():
for bar in data["bar"].unique():
bar_data = data[data["bar"] == bar].copy() # 一次筛选即可
if bar_data.empty:
continue
bar_data.rename(columns={column_name: column_name_text}, inplace=True)
if column_name == "pct_chg_total":
bar_data.rename(columns={"market_pct_chg": "市场自然涨跌"}, inplace=True)
# 可选：按均值排序
bar_data.sort_values(by=column_name_text, ascending=False, inplace=True)
bar_data.reset_index(drop=True, inplace=True)
plt.figure(figsize=(10, 6))
ax = sns.barplot(
x="symbol", y=column_name_text, data=bar_data, palette="Blues_d"
)
plt.title(f"{bar}趋势{column_name_text}(%)")
plt.xlabel("symbol")
plt.ylabel(column_name_text)
plt.xticks(rotation=45, ha="right")
# 如果column_name_text是"量化策略涨跌"，则柱状图，同时绘制量化策略涨跌与市场自然涨跌的柱状图，并绘制在同一个图表中
if column_name == "pct_chg_total":
plt.figure(figsize=(12, 7))
# 设置x轴位置，为并列柱状图做准备
x = np.arange(len(bar_data))
width = 0.35 # 柱状图宽度
# 确保symbol列是字符串类型，避免matplotlib警告
bar_data["symbol"] = bar_data["symbol"].astype(str)
# 绘制量化策略涨跌柱状图（蓝色渐变色）
bars1 = plt.bar(x - width/2, bar_data[column_name_text], width,
label=column_name_text,
color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))))
# 绘制市场自然涨跌柱状图（绿色渐变色）
bars2 = plt.bar(x + width/2, bar_data["市场自然涨跌"], width,
label="市场自然涨跌",
color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))))
# 设置图表标题和标签
plt.title(f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", fontsize=14, fontweight='bold')
plt.xlabel("Symbol", fontsize=12)
plt.ylabel("涨跌幅(%)", fontsize=12)
plt.xticks(x, bar_data['symbol'], rotation=45, ha='right')
plt.legend()
plt.grid(True, alpha=0.3)
# 在量化策略涨跌柱状图上方添加数值标签
for i, (bar1, value1) in enumerate(zip(bars1, bar_data[column_name_text])):
plt.text(bar1.get_x() + bar1.get_width()/2, value1 + (0.01 if value1 >= 0 else -0.01),
f'{value1:.3f}%', ha='center', va='bottom' if value1 >= 0 else 'top',
fontsize=9, fontweight='bold', color='darkblue')
# 在市场自然涨跌柱状图上方添加数值标签
for i, (bar2, value2) in enumerate(zip(bars2, bar_data["市场自然涨跌"])):
plt.text(bar2.get_x() + bar2.get_width()/2, value2 + (0.01 if value2 >= 0 else -0.01),
f'{value2:.3f}%', ha='center', va='bottom' if value2 >= 0 else 'top',
fontsize=9, fontweight='bold', color='darkgreen')
# 在柱状图上添加数值标签
for i, v in enumerate(bar_data[column_name_text]):
ax.text(
i,
v,
f"{v:.3f}",
ha="center",
va="bottom",
fontsize=10,
fontweight="bold",
else:
plt.figure(figsize=(10, 6))
# 确保symbol列是字符串类型，避免matplotlib警告
bar_data["symbol"] = bar_data["symbol"].astype(str)
ax = sns.barplot(
x="symbol", y=column_name_text, data=bar_data, palette="Blues_d"
)
plt.title(f"{bar}趋势{column_name_text}(%)")
plt.xlabel("symbol")
plt.ylabel(column_name_text)
plt.xticks(rotation=45, ha="right")
# 在柱状图上添加数值标签
for i, v in enumerate(bar_data[column_name_text]):
ax.text(
i,
v,
f"{v:.3f}",
ha="center",
va="bottom",
fontsize=10,
fontweight="bold",
)
plt.tight_layout()
save_path = os.path.join(
self.stats_chart_dir,
f"{bar}_ma_break_{column_name}_{strategy_name}.png",
f"{bar}_bar_chart_{column_name}_{strategy_name}.png",
)
plt.savefig(save_path, dpi=150)
plt.close()
sheet_name = f"{bar}_趋势{column_name_text}分布图表_{strategy_name}"
sheet_name = f"{bar}_趋势{column_name_text}柱状图_{strategy_name}"
chart_dict[sheet_name] = save_path
return chart_dict
def draw_quant_line_chart(self, data: pd.DataFrame, strategy_name: str = "全均线策略"):
"""
根据量化策略买卖明细记录，绘制量化策略涨跌与市场自然涨跌的折线图
:param data: 量化策略买卖明细记录
:param strategy_name: 策略名称
:return: None
"""
symbols = data["symbol"].unique()
bars = data["bar"].unique()
chart_dict = {}
for symbol in symbols:
for bar in bars:
symbol_bar_data = data[(data["symbol"] == symbol) & (data["bar"] == bar)]
if symbol_bar_data.empty:
continue
# 获取第一行数据作为基准
first_row = symbol_bar_data.iloc[0].copy()
# 创建初始化行，设置基准值
init_row = first_row.copy()
init_row.loc["pct_chg_total"] = 1.0 # 量化策略初始值为1
init_row.loc["end_timestamp"] = first_row["begin_timestamp"]
init_row.loc["end_date_time"] = first_row["begin_date_time"]
init_row.loc["end_close"] = first_row["begin_close"]
init_row.loc["end_ma5"] = first_row["begin_ma5"]
init_row.loc["end_ma10"] = first_row["begin_ma10"]
init_row.loc["end_ma20"] = first_row["begin_ma20"]
init_row.loc["end_ma30"] = first_row["begin_ma30"]
init_row.loc["end_macd_diff"] = first_row["begin_macd_diff"]
init_row.loc["end_macd_dea"] = first_row["begin_macd_dea"]
init_row.loc["end_macd"] = first_row["begin_macd"]
init_row.loc["pct_chg"] = 0
init_row.loc["interval_seconds"] = 0
init_row.loc["interval_minutes"] = 0
init_row.loc["interval_hours"] = 0
init_row.loc["interval_days"] = 0
# 将初始化行添加到数据开头
symbol_bar_data = pd.concat([pd.DataFrame([init_row]), symbol_bar_data])
symbol_bar_data.sort_values(by="end_timestamp", ascending=True, inplace=True)
symbol_bar_data.reset_index(drop=True, inplace=True)
# 确保时间列是datetime类型，避免matplotlib警告
symbol_bar_data["end_date_time"] = pd.to_datetime(symbol_bar_data["end_date_time"])
# 计算市场价位归一化数据（相对于初始价格）
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close"] / init_row["end_close"]
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close_to_1"].round(4)
# 绘制折线图
plt.figure(figsize=(12, 7))
# 绘制量化策略涨跌线（蓝色）
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["pct_chg_total"],
label="量化策略涨跌", color='blue', linewidth=2, marker='o', markersize=4)
# 绘制市场自然涨跌线（绿色）
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["end_close_to_1"],
label="市场自然涨跌", color='green', linewidth=2, marker='s', markersize=4)
plt.title(f"{symbol} {bar} 量化与市场折线图_{strategy_name}",
fontsize=14, fontweight='bold')
plt.xlabel("时间", fontsize=12)
plt.ylabel("涨跌变化", fontsize=12)
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
# 设置x轴标签，避免matplotlib警告
# 选择合适的时间间隔显示标签，避免过于密集
if len(symbol_bar_data) > 30:
# 如果数据点较多，选择间隔显示，但确保第一条和最后一条始终显示
step = max(1, len(symbol_bar_data) // 30)
# 创建标签索引列表，确保包含首尾数据
label_indices = [0] # 第一条
# 添加中间间隔的标签
for i in range(step, len(symbol_bar_data) - 1, step):
label_indices.append(i)
# 添加最后一条（如果还没有包含的话）
if len(symbol_bar_data) - 1 not in label_indices:
label_indices.append(len(symbol_bar_data) - 1)
# 设置x轴标签
plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices],
symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%m-%d %H:%M'),
rotation=45, ha='right')
else:
# 如果数据点较少，全部显示
plt.xticks(symbol_bar_data["end_date_time"],
symbol_bar_data["end_date_time"].dt.strftime('%m-%d %H:%M'),
rotation=45, ha='right')
plt.tight_layout()
save_path = os.path.join(
self.stats_chart_dir,
f"{symbol}_{bar}_line_chart_{strategy_name}.png",
)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
plt.close()
sheet_name = f"{symbol}_{bar}_折线图_{strategy_name}"
chart_dict[sheet_name] = save_path
return chart_dict

1
trade_ma_strategy_main.py
View File

@ -38,6 +38,7 @@ class TradeMaStrategyMain:
for strategy_name, strategy_info in strategy_dict.items():
pct_chg_df = self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
pct_chg_df_list.append(pct_chg_df)
pct_chg_df = pd.concat(pct_chg_df_list)
def statistics_pct_chg(self, pct_chg_df: pd.DataFrame):