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app.py

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+import gradio as gr
+from PIL import Image
+from matplotlib import pyplot as plt
+import numpy as np
+import pickle
+import gzip
+import seaborn as sns
+sns.set_theme()
+
+def flip(n, t, seed=13):
+    np.random.seed(seed)
+    flips = np.random.randint(0, 2, size=(n, t))
+    heads = flips.cumsum(axis=1)
+    return heads.astype(np.uint8)
+
+def load(filename):
+    # Adapted from https://gist.github.com/thearn/5424244
+    file = gzip.GzipFile(filename, 'rb')
+    data = file.read()
+    object = pickle.loads(data)
+    file.close()
+    return object
+
+def calc_outcome(p_win, win, loss, leverage):
+    n = 1000000
+    t = 60
+    base=100
+    # heads = flip(n, t)
+    heads = load('flips.pkl.gz')
+    tails = np.arange(t)+1 - heads
+    p_loss = 1 - p_win
+    log_win = np.log(1+leverage*win)
+    log_loss = np.log(1-leverage*loss)
+    log_outcome = heads*log_win + tails*log_loss
+    outcome = base*(np.exp(log_outcome))
+    return outcome
+
+def process(p_win, win, loss, leverage):
+    outcome = calc_outcome(p_win/100., win/100., loss/100., leverage/100.)
+    ensemble = fig2img(plot_ensemble(outcome)[0])
+    time = fig2img(plot_time(outcome)[0])
+    richest = fig2img(plot_richest(outcome)[0])
+    return ensemble, time, richest
+
+def fig2img(fig):
+    """Convert a Matplotlib figure to a PIL Image and return it"""
+    import io
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+def plot_ensemble(outcome):
+    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+    ax.plot(outcome.mean(axis=0))
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Average Wealth ($)')
+    ax.set_title('Ensemble (Collective) Perspective')
+    fig.tight_layout()
+    return fig, ax
+
+def plot_richest(outcome):
+    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+    ax.plot(outcome.max(axis=0)/1e6)
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Wealth ($ million)')
+    ax.set_title('Richest Individual')
+    fig.tight_layout()
+    return fig, ax
+
+def plot_time(outcome):
+    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+    ax.plot(np.percentile(outcome, 50, axis=0))
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Median Wealth ($)')
+    ax.set_title('Time (Individual) Perspective')
+    fig.tight_layout()
+    return fig, ax
+
+css = ""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column():
+        with gr.Row():
+            p_win = gr.Slider(0, 100, value=50, label='Win Prob.(%)', interactive=True)
+        with gr.Row():
+            win = gr.Number(value=50, label='Gain (%)', interactive=True)
+            loss = gr.Number(value=40, label='Loss (%)', interactive=True)
+        with gr.Row():
+            leverage = gr.Slider(0, 100, value=100, label='Leverage (%)', interactive=True)
+    with gr.Row():
+        btn = gr.Button('Go!')
+    with gr.Column(scale=1, min_width=900):
+        with gr.Row():
+            ensemble = gr.Image(label="Ensemble Perspective", shape=(300, 300), elem_id="plot_ensemble")
+            time = gr.Image(label="Time Perspective", shape=(300, 300), elem_id="plot_time")
+            richest = gr.Image(label="Richest Individual", shape=(300, 300), elem_id="plot_richest")
+            
+    btn.click(process, [p_win, win, loss, leverage], [ensemble, time, richest])
+    
+demo.launch()

flips.pkl.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5db71f8aef5f135390eb0875b3b8a164755af1571d2d2990ed0dd598366c70c
+size 1554099

requirements.txt

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+gradio
+pillow
+matplotlib
+numpy
+seaborn