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feat: Added tabbed interface and candlestick chart #14

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feat: Added tabbed interface and candlestick chart #14

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176 changes: 126 additions & 50 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,62 @@ def sma_crossover_strategy(data: pd.DataFrame) -> pd.DataFrame:
return data


def create_candlestick_chart(data: pd.DataFrame, ticker: str) -> go.Figure:
fig = go.Figure(
data=[
go.Candlestick(
x=data.index,
open=data['Open'],
high=data['High'],
low=data['Low'],
close=data['Close'],
name=f"{ticker} Candlesticks",
increasing_line_color='#2ECC71', # Green for bullish
decreasing_line_color='#E74C3C' # Red for bearish
),
go.Scatter(
x=data.index,
y=data['SMA_20'],
line=dict(color='blue', width=1),
name="20-day SMA"
),
go.Scatter(
x=data.index,
y=data['SMA_50'],
line=dict(color='orange', width=1),
name="50-day SMA"
)
]
)

# Add volume as subplot
fig.add_trace(
go.Bar(
x=data.index,
y=data['Volume'],
name="Volume",
marker_color='rgba(100, 100, 100, 0.5)',
yaxis="y2"
)
)

fig.update_layout(
title=f"{ticker} Candlestick Chart with Volume",
xaxis_title="Date",
yaxis_title="Price",
yaxis2=dict(
title="Volume",
overlaying="y",
side="right",
showgrid=False
),
legend_title="Indicators",
hovermode="x unified",
height=600
)
return fig


def create_comparison_chart(ticker_data: dict) -> go.Figure:
fig = go.Figure()
for ticker, data in ticker_data.items():
Expand Down Expand Up @@ -124,56 +180,76 @@ def main():
ticker_data[ticker] = data

if ticker_data:
price_chart = create_comparison_chart(ticker_data)
st.plotly_chart(price_chart, use_container_width=True)

rsi_chart = create_comparison_rsi_chart(ticker_data)
st.plotly_chart(rsi_chart, use_container_width=True)

st.subheader("Recent Data")
for ticker, data in ticker_data.items():
st.write(f"**{ticker}** Stock Analysis")
st.dataframe(data.tail().style.format({'Close': '${:.2f}', 'SMA_20': '${:.2f}', 'SMA_50': '${:.2f}', 'RSI': '{:.2f}'}))

last_close = data['Close'].iloc[-1]
sma_20 = data['SMA_20'].iloc[-1]
sma_50 = data['SMA_50'].iloc[-1]
rsi = data['RSI'].iloc[-1]

if last_close > sma_20 > sma_50:
st.write(f"{ticker}: The stock is in an **uptrend**. The current price is above both the 20-day and 50-day SMAs.")
elif last_close < sma_20 < sma_50:
st.write(f"{ticker}: The stock is in a **downtrend**. The current price is below both the 20-day and 50-day SMAs.")
else:
st.write(f"{ticker}: The stock is showing **mixed signals**. Consider additional indicators for a clearer picture.")

if rsi > 70:
st.write(f"{ticker}: The RSI indicates that the stock may be **overbought**.")
elif rsi < 30:
st.write(f"{ticker}: The RSI indicates that the stock may be **oversold**.")
else:
st.write(f"{ticker}: The RSI is **neutral**, indicating neither overbought nor oversold conditions.")

final_strategy_return = data['Cumulative Strategy Return'].iloc[-1] * 100
final_market_return = data['Cumulative Market Return'].iloc[-1] * 100
num_trades = data['Position'].abs().sum()

st.write(f"**{ticker} SMA Crossover Strategy Performance:**")
st.write(f"- Total Strategy Return: {final_strategy_return:.2f}%")
st.write(f"- Total Market Return: {final_market_return:.2f}%")
st.write(f"- Number of Trades Executed: {int(num_trades)}")

fig = go.Figure()
fig.add_trace(go.Scatter(x=data.index, y=data['Cumulative Market Return'], name="Market Return"))
fig.add_trace(go.Scatter(x=data.index, y=data['Cumulative Strategy Return'], name="Strategy Return"))
fig.update_layout(
title=f"{ticker} Cumulative Returns: Market vs SMA Crossover Strategy",
xaxis_title="Date",
yaxis_title="Cumulative Return",
hovermode="x unified"
)
st.plotly_chart(fig, use_container_width=True)
# Create tabs
tab1, tab2, tab3 = st.tabs(["Candlestick Charts", "Multi-Stock Comparison", "Recent Data & Analysis"])

with tab1:
# Display candlestick charts with volume for each stock
for ticker, data in ticker_data.items():
st.subheader(f"{ticker} Candlestick Chart")
candlestick_chart = create_candlestick_chart(data, ticker)
st.plotly_chart(candlestick_chart, use_container_width=True)

with tab2:
# Display multi-stock comparison line chart
st.subheader("Price Comparison")
price_chart = create_comparison_chart(ticker_data)
st.plotly_chart(price_chart, use_container_width=True)

# RSI comparison
st.subheader("RSI Comparison")
rsi_chart = create_comparison_rsi_chart(ticker_data)
st.plotly_chart(rsi_chart, use_container_width=True)

with tab3:
# Display performance metrics and recent data
st.subheader("Recent Data & Technical Analysis")
for ticker, data in ticker_data.items():
with st.expander(f"{ticker} Analysis"):
st.write(f"**Recent Data**")
st.dataframe(data.tail().style.format({'Close': '${:.2f}', 'SMA_20': '${:.2f}', 'SMA_50': '${:.2f}', 'RSI': '{:.2f}'}))

last_close = data['Close'].iloc[-1]
sma_20 = data['SMA_20'].iloc[-1]
sma_50 = data['SMA_50'].iloc[-1]
rsi = data['RSI'].iloc[-1]

st.write(f"**Trend Analysis**")
if last_close > sma_20 > sma_50:
st.write(f"The stock is in an **uptrend**. The current price is above both the 20-day and 50-day SMAs.")
elif last_close < sma_20 < sma_50:
st.write(f"The stock is in a **downtrend**. The current price is below both the 20-day and 50-day SMAs.")
else:
st.write(f"The stock is showing **mixed signals**. Consider additional indicators for a clearer picture.")

st.write(f"**RSI Analysis**")
if rsi > 70:
st.write(f"The RSI indicates that the stock may be **overbought**.")
elif rsi < 30:
st.write(f"The RSI indicates that the stock may be **oversold**.")
else:
st.write(f"The RSI is **neutral**, indicating neither overbought nor oversold conditions.")

final_strategy_return = data['Cumulative Strategy Return'].iloc[-1] * 100
final_market_return = data['Cumulative Market Return'].iloc[-1] * 100
num_trades = data['Position'].abs().sum()

st.write(f"**SMA Crossover Strategy Performance**")
st.write(f"- Total Strategy Return: {final_strategy_return:.2f}%")
st.write(f"- Total Market Return: {final_market_return:.2f}%")
st.write(f"- Number of Trades Executed: {int(num_trades)}")

fig = go.Figure()
fig.add_trace(go.Scatter(x=data.index, y=data['Cumulative Market Return'], name="Market Return"))
fig.add_trace(go.Scatter(x=data.index, y=data['Cumulative Strategy Return'], name="Strategy Return"))
fig.update_layout(
title=f"Cumulative Returns: Market vs SMA Crossover Strategy",
xaxis_title="Date",
yaxis_title="Cumulative Return",
hovermode="x unified"
)
st.plotly_chart(fig, use_container_width=True)


if __name__ == "__main__":
main()
main()